Protein_Metabolites_Integration.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Packges \n",
    "\n",
    "import numpy as np\n",
    "from pandas import DataFrame\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import scipy.stats as stats\n",
    "import statsmodels.api as sm\n",
    "import sklearn \n",
    "import sspa\n",
    "import sspa.utils\n",
    "import gseapy.plot as gp\n",
    "import networkx\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import scipy.stats as stats\n",
    "import statsmodels.api as sm\n",
    "import plotly.graph_objects as go\n",
    "import plotly.express as px\n",
    "import urllib.request\n",
    "import statsmodels\n",
    "import bioinfokit as bf\n",
    "import networkx as nx\n",
    "import math\n",
    "import itertools\n",
    "import fancyimpute\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Beginning Reactome download...\n",
      "Reactome DB file saved to ./Reactome_Homo_sapiens_pathways_compounds_R83.gmt\n",
      "Complete!\n"
     ]
    }
   ],
   "source": [
    "# Import Reactome\n",
    "reactome_pathways = sspa.process_reactome(\"Homo sapiens\", download_latest=True, filepath=\".\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'R-HSA-1059683': 'Interleukin-6 signaling',\n",
       " 'R-HSA-109581': 'Apoptosis',\n",
       " 'R-HSA-109582': 'Hemostasis',\n",
       " 'R-HSA-109606': 'Intrinsic Pathway for Apoptosis',\n",
       " 'R-HSA-109703': 'PKB-mediated events',\n",
       " 'R-HSA-109704': 'PI3K Cascade',\n",
       " 'R-HSA-110056': 'MAPK3 (ERK1) activation',\n",
       " 'R-HSA-110312': 'Translesion synthesis by REV1',\n",
       " 'R-HSA-110313': 'Translesion synthesis by Y family DNA polymerases bypasses lesions on DNA template',\n",
       " 'R-HSA-110314': 'Recognition of DNA damage by PCNA-containing replication complex',\n",
       " 'R-HSA-110320': 'Translesion Synthesis by POLH',\n",
       " 'R-HSA-110328': 'Recognition and association of DNA glycosylase with site containing an affected pyrimidine',\n",
       " 'R-HSA-110329': 'Cleavage of the damaged pyrimidine ',\n",
       " 'R-HSA-110330': 'Recognition and association of DNA glycosylase with site containing an affected purine',\n",
       " 'R-HSA-110331': 'Cleavage of the damaged purine',\n",
       " 'R-HSA-110357': 'Displacement of DNA glycosylase by APEX1',\n",
       " 'R-HSA-110362': 'POLB-Dependent Long Patch Base Excision Repair',\n",
       " 'R-HSA-110373': 'Resolution of AP sites via the multiple-nucleotide patch replacement pathway',\n",
       " 'R-HSA-110381': 'Resolution of AP sites via the single-nucleotide replacement pathway',\n",
       " 'R-HSA-111367': 'SLBP independent Processing of Histone Pre-mRNAs',\n",
       " 'R-HSA-111446': 'Activation of BIM and translocation to mitochondria ',\n",
       " 'R-HSA-111447': 'Activation of BAD and translocation to mitochondria ',\n",
       " 'R-HSA-111448': 'Activation of NOXA and translocation to mitochondria',\n",
       " 'R-HSA-111452': 'Activation and oligomerization of BAK protein',\n",
       " 'R-HSA-111453': 'BH3-only proteins associate with and inactivate anti-apoptotic BCL-2 members',\n",
       " 'R-HSA-111457': 'Release of apoptotic factors from the mitochondria',\n",
       " 'R-HSA-111458': 'Formation of apoptosome',\n",
       " 'R-HSA-111459': 'Activation of caspases through apoptosome-mediated cleavage',\n",
       " 'R-HSA-111461': 'Cytochrome c-mediated apoptotic response',\n",
       " 'R-HSA-111463': 'SMAC (DIABLO) binds to IAPs ',\n",
       " 'R-HSA-111464': 'SMAC(DIABLO)-mediated dissociation of IAP:caspase complexes ',\n",
       " 'R-HSA-111465': 'Apoptotic cleavage of cellular proteins',\n",
       " 'R-HSA-111469': 'SMAC, XIAP-regulated apoptotic response',\n",
       " 'R-HSA-111471': 'Apoptotic factor-mediated response',\n",
       " 'R-HSA-111885': 'Opioid Signalling',\n",
       " 'R-HSA-111931': 'PKA-mediated phosphorylation of CREB',\n",
       " 'R-HSA-111932': 'CaMK IV-mediated phosphorylation of CREB',\n",
       " 'R-HSA-111933': 'Calmodulin induced events',\n",
       " 'R-HSA-111957': 'Cam-PDE 1 activation',\n",
       " 'R-HSA-111995': 'phospho-PLA2 pathway',\n",
       " 'R-HSA-111996': 'Ca-dependent events',\n",
       " 'R-HSA-111997': 'CaM pathway',\n",
       " 'R-HSA-112040': 'G-protein mediated events',\n",
       " 'R-HSA-112043': 'PLC beta mediated events',\n",
       " 'R-HSA-112122': 'ALKBH2 mediated reversal of alkylation damage',\n",
       " 'R-HSA-112126': 'ALKBH3 mediated reversal of alkylation damage',\n",
       " 'R-HSA-112303': 'Electric Transmission Across Gap Junctions',\n",
       " 'R-HSA-112307': 'Transmission across Electrical Synapses ',\n",
       " 'R-HSA-112308': 'Presynaptic depolarization and calcium channel opening',\n",
       " 'R-HSA-112310': 'Neurotransmitter release cycle',\n",
       " 'R-HSA-112311': 'Neurotransmitter clearance',\n",
       " 'R-HSA-112313': 'Neurotransmitter uptake and metabolism In glial cells',\n",
       " 'R-HSA-112314': 'Neurotransmitter receptors and postsynaptic signal transmission',\n",
       " 'R-HSA-112315': 'Transmission across Chemical Synapses',\n",
       " 'R-HSA-112316': 'Neuronal System',\n",
       " 'R-HSA-112382': 'Formation of RNA Pol II elongation complex ',\n",
       " 'R-HSA-112399': 'IRS-mediated signalling',\n",
       " 'R-HSA-112409': 'RAF-independent MAPK1/3 activation',\n",
       " 'R-HSA-112411': 'MAPK1 (ERK2) activation',\n",
       " 'R-HSA-112412': 'SOS-mediated signalling',\n",
       " 'R-HSA-113418': 'Formation of the Early Elongation Complex',\n",
       " 'R-HSA-113501': 'Inhibition of replication initiation of damaged DNA by RB1/E2F1',\n",
       " 'R-HSA-113507': 'E2F-enabled inhibition of pre-replication complex formation',\n",
       " 'R-HSA-113510': 'E2F mediated regulation of DNA replication',\n",
       " 'R-HSA-114294': 'Activation, translocation and oligomerization of BAX',\n",
       " 'R-HSA-114452': 'Activation of BH3-only proteins',\n",
       " 'R-HSA-114508': 'Effects of PIP2 hydrolysis',\n",
       " 'R-HSA-114516': 'Disinhibition of SNARE formation',\n",
       " 'R-HSA-114604': 'GPVI-mediated activation cascade',\n",
       " 'R-HSA-114608': 'Platelet degranulation ',\n",
       " 'R-HSA-1168372': 'Downstream signaling events of B Cell Receptor (BCR)',\n",
       " 'R-HSA-1169091': 'Activation of NF-kappaB in B cells',\n",
       " 'R-HSA-1169092': 'Activation of RAS in B cells',\n",
       " 'R-HSA-1169408': 'ISG15 antiviral mechanism',\n",
       " 'R-HSA-1169410': 'Antiviral mechanism by IFN-stimulated genes',\n",
       " 'R-HSA-1170546': 'Prolactin receptor signaling',\n",
       " 'R-HSA-1181150': 'Signaling by NODAL',\n",
       " 'R-HSA-1187000': 'Fertilization',\n",
       " 'R-HSA-1221632': 'Meiotic synapsis',\n",
       " 'R-HSA-1222387': 'Tolerance of reactive oxygen produced by macrophages',\n",
       " 'R-HSA-1222449': 'Mtb iron assimilation by chelation',\n",
       " 'R-HSA-1222499': 'Latent infection - Other responses of Mtb to phagocytosis',\n",
       " 'R-HSA-1222538': 'Tolerance by Mtb to nitric oxide produced by macrophages',\n",
       " 'R-HSA-1222541': 'Cell redox homeostasis',\n",
       " 'R-HSA-1222556': 'ROS and RNS production in phagocytes',\n",
       " 'R-HSA-1226099': 'Signaling by FGFR in disease',\n",
       " 'R-HSA-1227986': 'Signaling by ERBB2',\n",
       " 'R-HSA-1227990': 'Signaling by ERBB2 in Cancer',\n",
       " 'R-HSA-1234158': 'Regulation of gene expression by Hypoxia-inducible Factor',\n",
       " 'R-HSA-1234174': 'Cellular response to hypoxia',\n",
       " 'R-HSA-1234176': 'Oxygen-dependent proline hydroxylation of Hypoxia-inducible Factor Alpha',\n",
       " 'R-HSA-1236382': 'Constitutive Signaling by Ligand-Responsive EGFR Cancer Variants',\n",
       " 'R-HSA-1236394': 'Signaling by ERBB4',\n",
       " 'R-HSA-1236973': 'Cross-presentation of particulate exogenous antigens (phagosomes)',\n",
       " 'R-HSA-1236974': 'ER-Phagosome pathway',\n",
       " 'R-HSA-1236975': 'Antigen processing-Cross presentation',\n",
       " 'R-HSA-1236977': 'Endosomal/Vacuolar pathway',\n",
       " 'R-HSA-1236978': 'Cross-presentation of soluble exogenous antigens (endosomes)',\n",
       " 'R-HSA-1237044': 'Erythrocytes take up carbon dioxide and release oxygen',\n",
       " 'R-HSA-1237112': 'Methionine salvage pathway',\n",
       " 'R-HSA-1247673': 'Erythrocytes take up oxygen and release carbon dioxide',\n",
       " 'R-HSA-1250196': 'SHC1 events in ERBB2 signaling',\n",
       " 'R-HSA-1250342': 'PI3K events in ERBB4 signaling',\n",
       " 'R-HSA-1250347': 'SHC1 events in ERBB4 signaling',\n",
       " 'R-HSA-1251932': 'PLCG1 events in ERBB2 signaling',\n",
       " 'R-HSA-1251985': 'Nuclear signaling by ERBB4',\n",
       " 'R-HSA-1253288': 'Downregulation of ERBB4 signaling',\n",
       " 'R-HSA-1257604': 'PIP3 activates AKT signaling',\n",
       " 'R-HSA-1266695': 'Interleukin-7 signaling',\n",
       " 'R-HSA-1266738': 'Developmental Biology',\n",
       " 'R-HSA-1268020': 'Mitochondrial protein import',\n",
       " 'R-HSA-1280215': 'Cytokine Signaling in Immune system',\n",
       " 'R-HSA-1280218': 'Adaptive Immune System',\n",
       " 'R-HSA-1295596': 'Spry regulation of FGF signaling',\n",
       " 'R-HSA-1296025': 'ATP sensitive Potassium channels',\n",
       " 'R-HSA-1296041': 'Activation of G protein gated Potassium channels',\n",
       " 'R-HSA-1296052': 'Ca2+ activated K+ channels',\n",
       " 'R-HSA-1296053': 'Classical Kir channels',\n",
       " 'R-HSA-1296059': 'G protein gated Potassium channels',\n",
       " 'R-HSA-1296061': 'HCN channels',\n",
       " 'R-HSA-1296065': 'Inwardly rectifying K+ channels',\n",
       " 'R-HSA-1296067': 'Potassium transport channels',\n",
       " 'R-HSA-1296071': 'Potassium Channels',\n",
       " 'R-HSA-1296072': 'Voltage gated Potassium channels',\n",
       " 'R-HSA-1296346': 'Tandem pore domain potassium channels',\n",
       " 'R-HSA-1299287': 'Tandem pore domain halothane-inhibited K+ channel (THIK)',\n",
       " 'R-HSA-1299308': 'Tandem of pore domain in a weak inwardly rectifying K+ channels (TWIK)',\n",
       " 'R-HSA-1299316': 'TWIK-releated acid-sensitive K+ channel (TASK)',\n",
       " 'R-HSA-1299344': 'TWIK-related spinal cord K+ channel (TRESK)',\n",
       " 'R-HSA-1299361': 'TWIK-related alkaline pH activated K+ channel (TALK)',\n",
       " 'R-HSA-1299503': 'TWIK related potassium channel (TREK)',\n",
       " 'R-HSA-1300642': 'Sperm Motility And Taxes',\n",
       " 'R-HSA-1300645': 'Acrosome Reaction and Sperm:Oocyte Membrane Binding',\n",
       " 'R-HSA-1306955': 'GRB7 events in ERBB2 signaling',\n",
       " 'R-HSA-1307965': 'betaKlotho-mediated ligand binding',\n",
       " 'R-HSA-1358803': 'Downregulation of ERBB2:ERBB3 signaling',\n",
       " 'R-HSA-1362277': 'Transcription of E2F targets under negative control by DREAM complex',\n",
       " 'R-HSA-1362300': 'Transcription of E2F targets under negative control by p107 (RBL1) and p130 (RBL2) in complex with HDAC1',\n",
       " 'R-HSA-1362409': 'Mitochondrial iron-sulfur cluster biogenesis',\n",
       " 'R-HSA-1368071': 'NR1D1 (REV-ERBA) represses gene expression',\n",
       " 'R-HSA-1368082': 'RORA activates gene expression',\n",
       " 'R-HSA-1368108': 'BMAL1:CLOCK,NPAS2 activates circadian gene expression',\n",
       " 'R-HSA-1369007': 'Mitochondrial ABC transporters',\n",
       " 'R-HSA-1369062': 'ABC transporters in lipid homeostasis',\n",
       " 'R-HSA-139853': 'Elevation of cytosolic Ca2+ levels',\n",
       " 'R-HSA-139910': 'Activation of BMF and translocation to mitochondria',\n",
       " 'R-HSA-139915': 'Activation of PUMA and translocation to mitochondria',\n",
       " 'R-HSA-140179': 'Amine Oxidase reactions',\n",
       " 'R-HSA-140180': 'COX reactions',\n",
       " 'R-HSA-140342': 'Apoptosis induced DNA fragmentation',\n",
       " 'R-HSA-140534': 'Caspase activation via Death Receptors in the presence of ligand',\n",
       " 'R-HSA-140834': 'Extrinsic Pathway of Fibrin Clot Formation',\n",
       " 'R-HSA-140837': 'Intrinsic Pathway of Fibrin Clot Formation',\n",
       " 'R-HSA-140875': 'Common Pathway of Fibrin Clot Formation',\n",
       " 'R-HSA-140877': 'Formation of Fibrin Clot (Clotting Cascade)',\n",
       " 'R-HSA-141333': 'Biogenic amines are oxidatively deaminated to aldehydes by MAOA and MAOB',\n",
       " 'R-HSA-141334': 'PAOs oxidise polyamines to amines',\n",
       " 'R-HSA-141405': 'Inhibition of the proteolytic activity of APC/C required for the onset of anaphase by mitotic spindle checkpoint components',\n",
       " 'R-HSA-141424': 'Amplification of signal from the kinetochores',\n",
       " 'R-HSA-141430': 'Inactivation of APC/C via direct inhibition of the APC/C complex',\n",
       " 'R-HSA-141444': 'Amplification  of signal from unattached  kinetochores via a MAD2  inhibitory signal',\n",
       " 'R-HSA-1428517': 'The citric acid (TCA) cycle and respiratory electron transport',\n",
       " 'R-HSA-1430728': 'Metabolism',\n",
       " 'R-HSA-1433557': 'Signaling by SCF-KIT',\n",
       " 'R-HSA-1433559': 'Regulation of KIT signaling',\n",
       " 'R-HSA-1433617': 'Regulation of signaling by NODAL',\n",
       " 'R-HSA-1442490': 'Collagen degradation',\n",
       " 'R-HSA-1445148': 'Translocation of SLC2A4 (GLUT4) to the plasma membrane',\n",
       " 'R-HSA-1461957': 'Beta defensins',\n",
       " 'R-HSA-1461973': 'Defensins',\n",
       " 'R-HSA-1462054': 'Alpha-defensins',\n",
       " 'R-HSA-1474151': 'Tetrahydrobiopterin (BH4) synthesis, recycling, salvage and regulation',\n",
       " 'R-HSA-1474165': 'Reproduction',\n",
       " 'R-HSA-1474228': 'Degradation of the extracellular matrix',\n",
       " 'R-HSA-1474244': 'Extracellular matrix organization',\n",
       " 'R-HSA-1474290': 'Collagen formation',\n",
       " 'R-HSA-1475029': 'Reversible hydration of carbon dioxide',\n",
       " 'R-HSA-1480926': 'O2/CO2 exchange in erythrocytes',\n",
       " 'R-HSA-1482788': 'Acyl chain remodelling of PC',\n",
       " 'R-HSA-1482798': 'Acyl chain remodeling of CL',\n",
       " 'R-HSA-1482801': 'Acyl chain remodelling of PS',\n",
       " 'R-HSA-1482839': 'Acyl chain remodelling of PE',\n",
       " 'R-HSA-1482883': 'Acyl chain remodeling of DAG and TAG',\n",
       " 'R-HSA-1482922': 'Acyl chain remodelling of PI',\n",
       " 'R-HSA-1482925': 'Acyl chain remodelling of PG',\n",
       " 'R-HSA-1483076': 'Synthesis of CL',\n",
       " 'R-HSA-1483101': 'Synthesis of PS',\n",
       " 'R-HSA-1483115': 'Hydrolysis of LPC',\n",
       " 'R-HSA-1483148': 'Synthesis of PG',\n",
       " 'R-HSA-1483152': 'Hydrolysis of LPE',\n",
       " 'R-HSA-1483166': 'Synthesis of PA',\n",
       " 'R-HSA-1483191': 'Synthesis of PC',\n",
       " 'R-HSA-1483196': 'PI and PC transport between ER and Golgi membranes',\n",
       " 'R-HSA-1483206': 'Glycerophospholipid biosynthesis',\n",
       " 'R-HSA-1483213': 'Synthesis of PE',\n",
       " 'R-HSA-1483226': 'Synthesis of PI',\n",
       " 'R-HSA-1483248': 'Synthesis of PIPs at the ER membrane',\n",
       " 'R-HSA-1483249': 'Inositol phosphate metabolism',\n",
       " 'R-HSA-1483255': 'PI Metabolism',\n",
       " 'R-HSA-1483257': 'Phospholipid metabolism',\n",
       " 'R-HSA-1489509': 'DAG and IP3 signaling',\n",
       " 'R-HSA-1500620': 'Meiosis',\n",
       " 'R-HSA-1500931': 'Cell-Cell communication',\n",
       " 'R-HSA-1502540': 'Signaling by Activin',\n",
       " 'R-HSA-1538133': 'G0 and Early G1',\n",
       " 'R-HSA-156580': 'Phase II - Conjugation of compounds',\n",
       " 'R-HSA-156581': 'Methylation',\n",
       " 'R-HSA-156582': 'Acetylation',\n",
       " 'R-HSA-156584': 'Cytosolic sulfonation of small molecules',\n",
       " 'R-HSA-156587': 'Amino Acid conjugation',\n",
       " 'R-HSA-156588': 'Glucuronidation',\n",
       " 'R-HSA-156590': 'Glutathione conjugation',\n",
       " 'R-HSA-1566948': 'Elastic fibre formation',\n",
       " 'R-HSA-1566977': 'Fibronectin matrix formation',\n",
       " 'R-HSA-156711': 'Polo-like kinase mediated events',\n",
       " 'R-HSA-156827': 'L13a-mediated translational silencing of Ceruloplasmin expression',\n",
       " 'R-HSA-156842': 'Eukaryotic Translation Elongation',\n",
       " 'R-HSA-156902': 'Peptide chain elongation',\n",
       " 'R-HSA-157118': 'Signaling by NOTCH',\n",
       " 'R-HSA-157579': 'Telomere Maintenance',\n",
       " 'R-HSA-157858': 'Gap junction trafficking and regulation',\n",
       " 'R-HSA-15869': 'Metabolism of nucleotides',\n",
       " 'R-HSA-1592230': 'Mitochondrial biogenesis',\n",
       " 'R-HSA-159227': 'Transport of the SLBP independent Mature mRNA',\n",
       " 'R-HSA-159230': 'Transport of the SLBP Dependant Mature mRNA',\n",
       " 'R-HSA-159231': 'Transport of Mature mRNA Derived from an Intronless Transcript',\n",
       " 'R-HSA-159234': 'Transport of Mature mRNAs Derived from Intronless Transcripts',\n",
       " 'R-HSA-159236': 'Transport of Mature mRNA derived from an Intron-Containing Transcript',\n",
       " 'R-HSA-1592389': 'Activation of Matrix Metalloproteinases',\n",
       " 'R-HSA-159418': 'Recycling of bile acids and salts',\n",
       " 'R-HSA-159424': 'Conjugation of carboxylic acids',\n",
       " 'R-HSA-159740': 'Gamma-carboxylation of protein precursors',\n",
       " 'R-HSA-159763': 'Transport of gamma-carboxylated protein precursors from the endoplasmic reticulum to the Golgi apparatus',\n",
       " 'R-HSA-159782': 'Removal of aminoterminal propeptides from gamma-carboxylated proteins',\n",
       " 'R-HSA-159854': 'Gamma-carboxylation, transport, and amino-terminal cleavage of proteins',\n",
       " 'R-HSA-1606322': 'ZBP1(DAI) mediated induction of type I IFNs',\n",
       " 'R-HSA-1606341': 'IRF3 mediated activation of type 1 IFN',\n",
       " 'R-HSA-1614517': 'Sulfide oxidation to sulfate',\n",
       " 'R-HSA-1614558': 'Degradation of cysteine and homocysteine',\n",
       " 'R-HSA-1614603': 'Cysteine formation from homocysteine',\n",
       " 'R-HSA-1614635': 'Sulfur amino acid metabolism',\n",
       " 'R-HSA-162582': 'Signal Transduction',\n",
       " 'R-HSA-162585': 'Uncoating of the HIV Virion',\n",
       " 'R-HSA-162587': 'HIV Life Cycle',\n",
       " 'R-HSA-162588': 'Budding and maturation of HIV virion',\n",
       " 'R-HSA-162589': 'Reverse Transcription of HIV RNA',\n",
       " 'R-HSA-162592': 'Integration of provirus',\n",
       " 'R-HSA-162594': 'Early Phase of HIV Life Cycle',\n",
       " 'R-HSA-162599': 'Late Phase of HIV Life Cycle',\n",
       " 'R-HSA-162658': 'Golgi Cisternae Pericentriolar Stack Reorganization',\n",
       " 'R-HSA-162699': 'Synthesis of dolichyl-phosphate mannose',\n",
       " 'R-HSA-162710': 'Synthesis of glycosylphosphatidylinositol (GPI)',\n",
       " 'R-HSA-162791': 'Attachment of GPI anchor to uPAR',\n",
       " 'R-HSA-162906': 'HIV Infection',\n",
       " 'R-HSA-162909': 'Host Interactions of HIV factors',\n",
       " 'R-HSA-1630316': 'Glycosaminoglycan metabolism',\n",
       " 'R-HSA-163125': 'Post-translational modification: synthesis of GPI-anchored proteins',\n",
       " 'R-HSA-163200': 'Respiratory electron transport, ATP synthesis by chemiosmotic coupling, and heat production by uncoupling proteins.',\n",
       " 'R-HSA-163210': 'Formation of ATP by chemiosmotic coupling',\n",
       " 'R-HSA-163282': 'Mitochondrial transcription initiation',\n",
       " 'R-HSA-1632852': 'Macroautophagy',\n",
       " 'R-HSA-163316': 'Mitochondrial transcription termination',\n",
       " 'R-HSA-163358': 'PKA-mediated phosphorylation of key metabolic factors',\n",
       " 'R-HSA-163359': 'Glucagon signaling in metabolic regulation',\n",
       " 'R-HSA-163560': 'Triglyceride catabolism',\n",
       " 'R-HSA-163615': 'PKA activation',\n",
       " 'R-HSA-163680': 'AMPK inhibits chREBP transcriptional activation activity',\n",
       " 'R-HSA-163685': 'Integration of energy metabolism',\n",
       " 'R-HSA-163754': 'Insulin effects increased synthesis of Xylulose-5-Phosphate',\n",
       " 'R-HSA-163765': 'ChREBP activates metabolic gene expression',\n",
       " 'R-HSA-163767': 'PP2A-mediated dephosphorylation of key metabolic factors',\n",
       " 'R-HSA-1638074': 'Keratan sulfate/keratin metabolism',\n",
       " 'R-HSA-1638091': 'Heparan sulfate/heparin (HS-GAG) metabolism',\n",
       " 'R-HSA-163841': 'Gamma carboxylation, hypusine formation and arylsulfatase activation',\n",
       " 'R-HSA-1640170': 'Cell Cycle',\n",
       " 'R-HSA-1643685': 'Disease',\n",
       " 'R-HSA-1643713': 'Signaling by EGFR in Cancer',\n",
       " 'R-HSA-164378': 'PKA activation in glucagon signalling',\n",
       " 'R-HSA-164516': 'Minus-strand DNA synthesis',\n",
       " 'R-HSA-164525': 'Plus-strand DNA synthesis',\n",
       " 'R-HSA-164843': '2-LTR circle formation',\n",
       " 'R-HSA-164938': 'Nef-mediates down modulation of cell surface receptors by recruiting them to clathrin adapters',\n",
       " 'R-HSA-164939': 'Nef mediated downregulation of CD28 cell surface expression',\n",
       " 'R-HSA-164940': 'Nef mediated downregulation of MHC class I complex cell surface expression',\n",
       " 'R-HSA-164944': 'Nef and signal transduction',\n",
       " 'R-HSA-164952': 'The role of Nef in HIV-1 replication and disease pathogenesis',\n",
       " 'R-HSA-165054': 'Rev-mediated nuclear export of HIV RNA',\n",
       " 'R-HSA-1650814': 'Collagen biosynthesis and modifying enzymes',\n",
       " 'R-HSA-165158': 'Activation of AKT2',\n",
       " 'R-HSA-165159': 'MTOR signalling',\n",
       " 'R-HSA-165160': 'PDE3B signalling',\n",
       " 'R-HSA-165181': 'Inhibition of TSC complex formation by PKB',\n",
       " 'R-HSA-1655829': 'Regulation of cholesterol biosynthesis by SREBP (SREBF)',\n",
       " 'R-HSA-166016': 'Toll Like Receptor 4 (TLR4) Cascade',\n",
       " 'R-HSA-166020': 'Transfer of LPS from LBP carrier to CD14',\n",
       " 'R-HSA-1660499': 'Synthesis of PIPs at the plasma membrane',\n",
       " 'R-HSA-1660514': 'Synthesis of PIPs at the Golgi membrane',\n",
       " 'R-HSA-1660516': 'Synthesis of PIPs at the early endosome membrane',\n",
       " 'R-HSA-1660517': 'Synthesis of PIPs at the late endosome membrane',\n",
       " 'R-HSA-166058': 'MyD88:MAL(TIRAP) cascade initiated on plasma membrane',\n",
       " 'R-HSA-1660661': 'Sphingolipid de novo biosynthesis',\n",
       " 'R-HSA-1660662': 'Glycosphingolipid metabolism',\n",
       " 'R-HSA-166166': 'MyD88-independent TLR4 cascade ',\n",
       " 'R-HSA-166187': 'Mitochondrial Uncoupling',\n",
       " 'R-HSA-166208': 'mTORC1-mediated signalling',\n",
       " 'R-HSA-1663150': 'The activation of arylsulfatases',\n",
       " 'R-HSA-166520': 'Signaling by NTRKs',\n",
       " 'R-HSA-166658': 'Complement cascade',\n",
       " 'R-HSA-166662': 'Lectin pathway of complement activation',\n",
       " 'R-HSA-166663': 'Initial triggering of complement',\n",
       " 'R-HSA-166665': 'Terminal pathway of complement',\n",
       " 'R-HSA-166786': 'Creation of C4 and C2 activators',\n",
       " 'R-HSA-167021': 'PLC-gamma1 signalling',\n",
       " 'R-HSA-167044': 'Signalling to RAS',\n",
       " 'R-HSA-167060': 'NGF processing',\n",
       " 'R-HSA-167152': 'Formation of HIV elongation complex in the absence of HIV Tat',\n",
       " 'R-HSA-167158': 'Formation of the HIV-1 Early Elongation Complex',\n",
       " 'R-HSA-167160': 'RNA Pol II CTD phosphorylation and interaction with CE during HIV infection',\n",
       " 'R-HSA-167161': 'HIV Transcription Initiation',\n",
       " 'R-HSA-167162': 'RNA Polymerase II HIV Promoter Escape',\n",
       " 'R-HSA-167169': 'HIV Transcription Elongation',\n",
       " 'R-HSA-167172': 'Transcription of the HIV genome',\n",
       " 'R-HSA-167200': 'Formation of HIV-1 elongation complex containing HIV-1 Tat',\n",
       " 'R-HSA-167238': 'Pausing and recovery of Tat-mediated HIV elongation',\n",
       " 'R-HSA-167242': 'Abortive elongation of HIV-1 transcript in the absence of Tat',\n",
       " 'R-HSA-167243': 'Tat-mediated HIV elongation arrest and recovery',\n",
       " 'R-HSA-167246': 'Tat-mediated elongation of the HIV-1 transcript',\n",
       " 'R-HSA-167287': 'HIV elongation arrest and recovery',\n",
       " 'R-HSA-167290': 'Pausing and recovery of HIV elongation',\n",
       " 'R-HSA-167590': 'Nef Mediated CD4 Down-regulation',\n",
       " 'R-HSA-167826': 'The fatty acid cycling model',\n",
       " 'R-HSA-167827': 'The proton buffering model',\n",
       " 'R-HSA-1679131': 'Trafficking and processing of endosomal TLR',\n",
       " 'R-HSA-168138': 'Toll Like Receptor 9 (TLR9) Cascade',\n",
       " 'R-HSA-168142': 'Toll Like Receptor 10 (TLR10) Cascade',\n",
       " 'R-HSA-168164': 'Toll Like Receptor 3 (TLR3) Cascade',\n",
       " 'R-HSA-168176': 'Toll Like Receptor 5 (TLR5) Cascade',\n",
       " 'R-HSA-168179': 'Toll Like Receptor TLR1:TLR2 Cascade',\n",
       " 'R-HSA-168181': 'Toll Like Receptor 7/8 (TLR7/8) Cascade',\n",
       " 'R-HSA-168188': 'Toll Like Receptor TLR6:TLR2 Cascade',\n",
       " 'R-HSA-168249': 'Innate Immune System',\n",
       " 'R-HSA-168255': 'Influenza Infection',\n",
       " 'R-HSA-168256': 'Immune System',\n",
       " 'R-HSA-168268': 'Virus Assembly and Release',\n",
       " 'R-HSA-168270': 'Fusion and Uncoating of the Influenza Virion',\n",
       " 'R-HSA-168271': 'Transport of Ribonucleoproteins into the Host Nucleus',\n",
       " 'R-HSA-168273': 'Influenza Viral RNA Transcription and Replication',\n",
       " 'R-HSA-168274': 'Export of Viral Ribonucleoproteins from Nucleus',\n",
       " 'R-HSA-168275': 'Entry of Influenza Virion into Host Cell via Endocytosis',\n",
       " 'R-HSA-168276': 'NS1 Mediated Effects on Host Pathways',\n",
       " 'R-HSA-168277': 'Influenza Virus Induced Apoptosis',\n",
       " 'R-HSA-168288': 'Fusion of the Influenza Virion to the Host Cell Endosome',\n",
       " 'R-HSA-168298': 'Release',\n",
       " 'R-HSA-168302': 'Budding',\n",
       " 'R-HSA-168303': 'Packaging of Eight RNA Segments',\n",
       " 'R-HSA-168305': 'Inhibition of Interferon Synthesis',\n",
       " 'R-HSA-168315': 'Inhibition of Host mRNA Processing and RNA Silencing',\n",
       " 'R-HSA-168316': 'Assembly of Viral Components at the Budding Site',\n",
       " 'R-HSA-168325': 'Viral Messenger RNA Synthesis',\n",
       " 'R-HSA-168330': 'Viral RNP Complexes in the Host Cell Nucleus',\n",
       " 'R-HSA-168333': 'NEP/NS2 Interacts with the Cellular Export Machinery',\n",
       " 'R-HSA-168336': 'Uncoating of the Influenza Virion',\n",
       " 'R-HSA-168638': 'NOD1/2 Signaling Pathway',\n",
       " 'R-HSA-168643': 'Nucleotide-binding domain, leucine rich repeat containing receptor (NLR) signaling pathways',\n",
       " 'R-HSA-168799': 'Neurotoxicity of clostridium toxins',\n",
       " 'R-HSA-168874': 'Transport of HA trimer, NA tetramer and M2 tetramer from the endoplasmic reticulum to the Golgi Apparatus',\n",
       " 'R-HSA-168888': 'Inhibition of IFN-beta',\n",
       " 'R-HSA-168898': 'Toll-like Receptor Cascades',\n",
       " 'R-HSA-168927': 'TICAM1, RIP1-mediated IKK complex recruitment',\n",
       " 'R-HSA-168928': 'DDX58/IFIH1-mediated induction of interferon-alpha/beta',\n",
       " 'R-HSA-169131': 'Inhibition of PKR',\n",
       " 'R-HSA-169893': 'Prolonged ERK activation events',\n",
       " 'R-HSA-169911': 'Regulation of Apoptosis',\n",
       " 'R-HSA-170145': 'Phosphorylation of proteins involved in the G2/M transition by Cyclin A:Cdc2 complexes',\n",
       " 'R-HSA-170660': 'Adenylate cyclase activating pathway',\n",
       " 'R-HSA-170670': 'Adenylate cyclase inhibitory pathway',\n",
       " 'R-HSA-170822': 'Regulation of Glucokinase by Glucokinase Regulatory Protein',\n",
       " 'R-HSA-170834': 'Signaling by TGF-beta Receptor Complex',\n",
       " 'R-HSA-170968': 'Frs2-mediated activation',\n",
       " 'R-HSA-170984': 'ARMS-mediated activation',\n",
       " 'R-HSA-171007': 'p38MAPK events',\n",
       " 'R-HSA-171286': 'Synthesis and processing of ENV and VPU',\n",
       " 'R-HSA-171306': 'Packaging Of Telomere Ends',\n",
       " 'R-HSA-171319': 'Telomere Extension By Telomerase',\n",
       " 'R-HSA-173107': 'Binding and entry of HIV virion',\n",
       " 'R-HSA-173599': 'Formation of the active cofactor, UDP-glucuronate',\n",
       " 'R-HSA-173623': 'Classical antibody-mediated complement activation',\n",
       " 'R-HSA-173736': 'Alternative complement activation',\n",
       " 'R-HSA-174048': 'APC/C:Cdc20 mediated degradation of Cyclin B',\n",
       " 'R-HSA-174084': 'Autodegradation of Cdh1 by Cdh1:APC/C',\n",
       " 'R-HSA-174113': 'SCF-beta-TrCP mediated degradation of Emi1',\n",
       " 'R-HSA-174143': 'APC/C-mediated degradation of cell cycle proteins',\n",
       " 'R-HSA-174154': 'APC/C:Cdc20 mediated degradation of Securin',\n",
       " 'R-HSA-174178': 'APC/C:Cdh1 mediated degradation of Cdc20 and other APC/C:Cdh1 targeted proteins in late mitosis/early G1',\n",
       " 'R-HSA-174184': 'Cdc20:Phospho-APC/C mediated degradation of Cyclin A',\n",
       " 'R-HSA-174362': 'Transport and synthesis of PAPS',\n",
       " 'R-HSA-174403': 'Glutathione synthesis and recycling',\n",
       " 'R-HSA-174411': 'Polymerase switching on the C-strand of the telomere',\n",
       " 'R-HSA-174414': 'Processive synthesis on the C-strand of the telomere',\n",
       " 'R-HSA-174417': 'Telomere C-strand (Lagging Strand) Synthesis',\n",
       " 'R-HSA-174430': 'Telomere C-strand synthesis initiation',\n",
       " 'R-HSA-174437': 'Removal of the Flap Intermediate from the C-strand',\n",
       " 'R-HSA-174490': 'Membrane binding and targetting of GAG proteins',\n",
       " 'R-HSA-174495': 'Synthesis And Processing Of GAG, GAGPOL Polyproteins',\n",
       " 'R-HSA-174577': 'Activation of C3 and C5',\n",
       " 'R-HSA-174824': 'Plasma lipoprotein assembly, remodeling, and clearance',\n",
       " 'R-HSA-175474': 'Assembly Of The HIV Virion',\n",
       " 'R-HSA-175567': 'Integration of viral DNA into host genomic DNA',\n",
       " 'R-HSA-176033': 'Interactions of Vpr with host cellular proteins',\n",
       " 'R-HSA-176034': 'Interactions of Tat with host cellular proteins',\n",
       " 'R-HSA-176187': 'Activation of ATR in response to replication stress',\n",
       " 'R-HSA-176407': 'Conversion from APC/C:Cdc20 to APC/C:Cdh1 in late anaphase',\n",
       " 'R-HSA-176408': 'Regulation of APC/C activators between G1/S and early anaphase',\n",
       " 'R-HSA-176409': 'APC/C:Cdc20 mediated degradation of mitotic proteins',\n",
       " 'R-HSA-176412': 'Phosphorylation of the APC/C',\n",
       " 'R-HSA-176417': 'Phosphorylation of Emi1',\n",
       " 'R-HSA-176814': 'Activation of APC/C and APC/C:Cdc20 mediated degradation of mitotic proteins',\n",
       " 'R-HSA-176974': 'Unwinding of DNA',\n",
       " 'R-HSA-177128': 'Conjugation of salicylate with glycine',\n",
       " 'R-HSA-177135': 'Conjugation of benzoate with glycine',\n",
       " 'R-HSA-177162': 'Conjugation of phenylacetate with glutamine',\n",
       " 'R-HSA-177243': 'Interactions of Rev with host cellular proteins',\n",
       " 'R-HSA-177504': 'Retrograde neurotrophin signalling',\n",
       " 'R-HSA-177539': 'Autointegration results in viral DNA circles',\n",
       " 'R-HSA-177929': 'Signaling by EGFR',\n",
       " 'R-HSA-1793185': 'Chondroitin sulfate/dermatan sulfate metabolism',\n",
       " 'R-HSA-179409': 'APC-Cdc20 mediated degradation of Nek2A',\n",
       " 'R-HSA-179419': 'APC:Cdc20 mediated degradation of cell cycle proteins prior to satisfation of the cell cycle checkpoint',\n",
       " 'R-HSA-179812': 'GRB2 events in EGFR signaling',\n",
       " 'R-HSA-1799339': 'SRP-dependent cotranslational protein targeting to membrane',\n",
       " 'R-HSA-180024': 'DARPP-32 events',\n",
       " 'R-HSA-180292': 'GAB1 signalosome',\n",
       " 'R-HSA-180336': 'SHC1 events in EGFR signaling',\n",
       " 'R-HSA-180534': 'Vpu mediated degradation of CD4',\n",
       " 'R-HSA-180585': 'Vif-mediated degradation of APOBEC3G',\n",
       " 'R-HSA-180689': 'APOBEC3G mediated resistance to HIV-1 infection',\n",
       " 'R-HSA-180746': 'Nuclear import of Rev protein',\n",
       " 'R-HSA-180786': 'Extension of Telomeres',\n",
       " 'R-HSA-180897': 'Vpr-mediated induction of apoptosis by mitochondrial outer membrane permeabilization',\n",
       " 'R-HSA-180910': 'Vpr-mediated nuclear import of PICs',\n",
       " 'R-HSA-1810476': 'RIP-mediated NFkB activation via ZBP1',\n",
       " 'R-HSA-181429': 'Serotonin Neurotransmitter Release Cycle',\n",
       " 'R-HSA-181430': 'Norepinephrine Neurotransmitter Release Cycle',\n",
       " 'R-HSA-181431': 'Acetylcholine binding and downstream events',\n",
       " 'R-HSA-181438': 'Toll Like Receptor 2 (TLR2) Cascade',\n",
       " 'R-HSA-182218': 'Nef Mediated CD8 Down-regulation',\n",
       " 'R-HSA-182971': 'EGFR downregulation',\n",
       " 'R-HSA-1834941': 'STING mediated induction of host immune responses',\n",
       " 'R-HSA-1834949': 'Cytosolic sensors of pathogen-associated DNA ',\n",
       " 'R-HSA-1839117': 'Signaling by cytosolic FGFR1 fusion mutants',\n",
       " 'R-HSA-1839120': 'Signaling by FGFR1 amplification mutants',\n",
       " 'R-HSA-1839122': 'Signaling by activated point mutants of FGFR1',\n",
       " 'R-HSA-1839124': 'FGFR1 mutant receptor activation',\n",
       " 'R-HSA-1839126': 'FGFR2 mutant receptor activation',\n",
       " 'R-HSA-1839128': 'FGFR4 mutant receptor activation',\n",
       " 'R-HSA-1839130': 'Signaling by activated point mutants of FGFR3',\n",
       " 'R-HSA-1852241': 'Organelle biogenesis and maintenance',\n",
       " 'R-HSA-1855167': 'Synthesis of pyrophosphates in the cytosol',\n",
       " 'R-HSA-1855183': 'Synthesis of IP2, IP, and Ins in the cytosol',\n",
       " 'R-HSA-1855191': 'Synthesis of IPs in the nucleus',\n",
       " 'R-HSA-1855204': 'Synthesis of IP3 and IP4 in the cytosol',\n",
       " 'R-HSA-1855231': 'Synthesis of IPs in the ER lumen',\n",
       " 'R-HSA-186712': 'Regulation of beta-cell development',\n",
       " 'R-HSA-186763': 'Downstream signal transduction',\n",
       " 'R-HSA-186797': 'Signaling by PDGF',\n",
       " 'R-HSA-187015': 'Activation of TRKA receptors',\n",
       " 'R-HSA-187024': 'NGF-independant TRKA activation',\n",
       " 'R-HSA-187037': 'Signaling by NTRK1 (TRKA)',\n",
       " 'R-HSA-187042': 'TRKA activation by NGF',\n",
       " 'R-HSA-187577': 'SCF(Skp2)-mediated degradation of p27/p21',\n",
       " 'R-HSA-187687': 'Signalling to ERKs',\n",
       " 'R-HSA-187706': 'Signalling to p38 via RIT and RIN',\n",
       " 'R-HSA-189085': 'Digestion of dietary carbohydrate',\n",
       " 'R-HSA-189200': 'Cellular hexose transport',\n",
       " 'R-HSA-189445': 'Metabolism of porphyrins',\n",
       " 'R-HSA-189451': 'Heme biosynthesis',\n",
       " 'R-HSA-189483': 'Heme degradation',\n",
       " 'R-HSA-190236': 'Signaling by FGFR',\n",
       " 'R-HSA-190239': 'FGFR3 ligand binding and activation',\n",
       " 'R-HSA-190241': 'FGFR2 ligand binding and activation',\n",
       " 'R-HSA-190242': 'FGFR1 ligand binding and activation',\n",
       " 'R-HSA-190322': 'FGFR4 ligand binding and activation',\n",
       " 'R-HSA-190370': 'FGFR1b ligand binding and activation',\n",
       " 'R-HSA-190371': 'FGFR3b ligand binding and activation',\n",
       " 'R-HSA-190372': 'FGFR3c ligand binding and activation',\n",
       " 'R-HSA-190373': 'FGFR1c ligand binding and activation',\n",
       " 'R-HSA-190374': 'FGFR1c and Klotho ligand binding and activation',\n",
       " 'R-HSA-190375': 'FGFR2c ligand binding and activation',\n",
       " 'R-HSA-190377': 'FGFR2b ligand binding and activation',\n",
       " 'R-HSA-190704': 'Oligomerization of connexins into connexons',\n",
       " 'R-HSA-190827': 'Transport of connexins along the secretory pathway',\n",
       " 'R-HSA-190828': 'Gap junction trafficking',\n",
       " 'R-HSA-190840': 'Microtubule-dependent trafficking of connexons from Golgi to the plasma membrane',\n",
       " 'R-HSA-190861': 'Gap junction assembly',\n",
       " 'R-HSA-190872': 'Transport of connexons to the plasma membrane',\n",
       " 'R-HSA-190873': 'Gap junction degradation',\n",
       " 'R-HSA-1912399': 'Pre-NOTCH Processing in the Endoplasmic Reticulum',\n",
       " 'R-HSA-1912408': 'Pre-NOTCH Transcription and Translation',\n",
       " 'R-HSA-1912420': 'Pre-NOTCH Processing in Golgi',\n",
       " 'R-HSA-1912422': 'Pre-NOTCH Expression and Processing',\n",
       " 'R-HSA-191273': 'Cholesterol biosynthesis',\n",
       " 'R-HSA-191650': 'Regulation of gap junction activity',\n",
       " 'R-HSA-191859': 'snRNP Assembly',\n",
       " 'R-HSA-192105': 'Synthesis of bile acids and bile salts',\n",
       " 'R-HSA-192456': 'Digestion of dietary lipid',\n",
       " 'R-HSA-192814': 'vRNA Synthesis',\n",
       " 'R-HSA-192823': 'Viral mRNA Translation',\n",
       " 'R-HSA-192869': 'cRNA Synthesis',\n",
       " 'R-HSA-192905': 'vRNP Assembly',\n",
       " 'R-HSA-193048': 'Androgen biosynthesis',\n",
       " 'R-HSA-193144': 'Estrogen biosynthesis',\n",
       " 'R-HSA-193368': 'Synthesis of bile acids and bile salts via 7alpha-hydroxycholesterol',\n",
       " 'R-HSA-193634': 'Axonal growth inhibition (RHOA activation)',\n",
       " 'R-HSA-193639': 'p75NTR signals via NF-kB',\n",
       " 'R-HSA-193648': 'NRAGE signals death through JNK',\n",
       " 'R-HSA-193670': 'p75NTR negatively regulates cell cycle via SC1',\n",
       " 'R-HSA-193681': 'Ceramide signalling',\n",
       " 'R-HSA-193692': 'Regulated proteolysis of p75NTR',\n",
       " 'R-HSA-193697': 'p75NTR regulates axonogenesis',\n",
       " 'R-HSA-193704': 'p75 NTR receptor-mediated signalling',\n",
       " 'R-HSA-193775': 'Synthesis of bile acids and bile salts via 24-hydroxycholesterol',\n",
       " 'R-HSA-193807': 'Synthesis of bile acids and bile salts via 27-hydroxycholesterol',\n",
       " 'R-HSA-193993': 'Mineralocorticoid biosynthesis',\n",
       " 'R-HSA-194002': 'Glucocorticoid biosynthesis',\n",
       " 'R-HSA-194068': 'Bile acid and bile salt metabolism',\n",
       " 'R-HSA-194138': 'Signaling by VEGF',\n",
       " 'R-HSA-194306': 'Neurophilin interactions with VEGF and VEGFR',\n",
       " 'R-HSA-194313': 'VEGF ligand-receptor interactions',\n",
       " 'R-HSA-194315': 'Signaling by Rho GTPases',\n",
       " 'R-HSA-194441': 'Metabolism of non-coding RNA',\n",
       " 'R-HSA-195253': 'Degradation of beta-catenin by the destruction complex',\n",
       " 'R-HSA-195258': 'RHO GTPase Effectors',\n",
       " 'R-HSA-195399': 'VEGF binds to VEGFR leading to receptor dimerization',\n",
       " 'R-HSA-195721': 'Signaling by WNT',\n",
       " 'R-HSA-196025': 'Formation of annular gap junctions',\n",
       " 'R-HSA-196071': 'Metabolism of steroid hormones',\n",
       " 'R-HSA-196108': 'Pregnenolone biosynthesis',\n",
       " 'R-HSA-196299': 'Beta-catenin phosphorylation cascade',\n",
       " 'R-HSA-1963640': 'GRB2 events in ERBB2 signaling',\n",
       " 'R-HSA-1963642': 'PI3K events in ERBB2 signaling',\n",
       " 'R-HSA-196741': 'Cobalamin (Cbl, vitamin B12) transport and metabolism',\n",
       " 'R-HSA-196757': 'Metabolism of folate and pterines',\n",
       " 'R-HSA-196780': 'Biotin transport and metabolism',\n",
       " 'R-HSA-196783': 'Coenzyme A biosynthesis',\n",
       " 'R-HSA-196791': 'Vitamin D (calciferol) metabolism',\n",
       " 'R-HSA-196807': 'Nicotinate metabolism',\n",
       " 'R-HSA-196819': 'Vitamin B1 (thiamin) metabolism',\n",
       " 'R-HSA-196836': 'Vitamin C (ascorbate) metabolism',\n",
       " 'R-HSA-196843': 'Vitamin B2 (riboflavin) metabolism',\n",
       " 'R-HSA-196849': 'Metabolism of water-soluble vitamins and cofactors',\n",
       " 'R-HSA-196854': 'Metabolism of vitamins and cofactors',\n",
       " 'R-HSA-1971475': 'A tetrasaccharide linker sequence is required for GAG synthesis',\n",
       " 'R-HSA-197264': 'Nicotinamide salvaging',\n",
       " 'R-HSA-1980143': 'Signaling by NOTCH1',\n",
       " 'R-HSA-1980145': 'Signaling by NOTCH2',\n",
       " 'R-HSA-198203': 'PI3K/AKT activation',\n",
       " 'R-HSA-198323': 'AKT phosphorylates targets in the cytosol',\n",
       " 'R-HSA-198693': 'AKT phosphorylates targets in the nucleus',\n",
       " 'R-HSA-198725': 'Nuclear Events (kinase and transcription factor activation)',\n",
       " 'R-HSA-198745': 'Signalling to STAT3',\n",
       " 'R-HSA-198753': 'ERK/MAPK targets',\n",
       " 'R-HSA-198765': 'Signalling to ERK5',\n",
       " 'R-HSA-198933': 'Immunoregulatory interactions between a Lymphoid and a non-Lymphoid cell',\n",
       " 'R-HSA-1989781': 'PPARA activates gene expression',\n",
       " 'R-HSA-199220': 'Vitamin B5 (pantothenate) metabolism',\n",
       " 'R-HSA-199418': 'Negative regulation of the PI3K/AKT network',\n",
       " 'R-HSA-199920': 'CREB phosphorylation',\n",
       " 'R-HSA-199977': 'ER to Golgi Anterograde Transport',\n",
       " 'R-HSA-199991': 'Membrane Trafficking',\n",
       " 'R-HSA-199992': 'trans-Golgi Network Vesicle Budding',\n",
       " 'R-HSA-200425': 'Carnitine metabolism',\n",
       " 'R-HSA-201451': 'Signaling by BMP',\n",
       " 'R-HSA-201556': 'Signaling by ALK',\n",
       " 'R-HSA-201681': 'TCF dependent signaling in response to WNT',\n",
       " 'R-HSA-201688': 'WNT mediated activation of DVL',\n",
       " 'R-HSA-201722': 'Formation of the beta-catenin:TCF transactivating complex',\n",
       " 'R-HSA-202040': 'G-protein activation',\n",
       " 'R-HSA-202131': 'Metabolism of nitric oxide: NOS3 activation and regulation',\n",
       " 'R-HSA-2022090': 'Assembly of collagen fibrils and other multimeric structures',\n",
       " 'R-HSA-2022377': 'Metabolism of Angiotensinogen to Angiotensins',\n",
       " 'R-HSA-2022854': 'Keratan sulfate biosynthesis',\n",
       " 'R-HSA-2022857': 'Keratan sulfate degradation',\n",
       " 'R-HSA-2022870': 'Chondroitin sulfate biosynthesis',\n",
       " 'R-HSA-2022923': 'Dermatan sulfate biosynthesis',\n",
       " 'R-HSA-2022928': 'HS-GAG biosynthesis',\n",
       " 'R-HSA-2023837': 'Signaling by FGFR2 amplification mutants',\n",
       " 'R-HSA-202403': 'TCR signaling',\n",
       " 'R-HSA-2024096': 'HS-GAG degradation',\n",
       " 'R-HSA-2024101': 'CS/DS degradation',\n",
       " 'R-HSA-202424': 'Downstream TCR signaling',\n",
       " 'R-HSA-202427': 'Phosphorylation of CD3 and TCR zeta chains',\n",
       " 'R-HSA-202430': 'Translocation of ZAP-70 to Immunological synapse',\n",
       " 'R-HSA-202433': 'Generation of second messenger molecules',\n",
       " 'R-HSA-2025928': 'Calcineurin activates NFAT',\n",
       " 'R-HSA-202670': 'ERKs are inactivated',\n",
       " 'R-HSA-202733': 'Cell surface interactions at the vascular wall',\n",
       " 'R-HSA-2028269': 'Signaling by Hippo',\n",
       " 'R-HSA-2029480': 'Fcgamma receptor (FCGR) dependent phagocytosis',\n",
       " 'R-HSA-2029481': 'FCGR activation',\n",
       " 'R-HSA-2029482': 'Regulation of actin dynamics for phagocytic cup formation',\n",
       " 'R-HSA-2029485': 'Role of phospholipids in phagocytosis',\n",
       " 'R-HSA-2032785': 'YAP1- and WWTR1 (TAZ)-stimulated gene expression',\n",
       " 'R-HSA-2033514': 'FGFR3 mutant receptor activation',\n",
       " 'R-HSA-2033515': 't(4;14) translocations of FGFR3',\n",
       " 'R-HSA-2033519': 'Activated point mutants of FGFR2',\n",
       " 'R-HSA-203615': 'eNOS activation',\n",
       " 'R-HSA-203641': 'NOSTRIN mediated eNOS trafficking',\n",
       " 'R-HSA-203754': 'NOSIP mediated eNOS trafficking',\n",
       " 'R-HSA-203927': 'MicroRNA (miRNA) biogenesis',\n",
       " 'R-HSA-204005': 'COPII-mediated vesicle transport',\n",
       " 'R-HSA-204174': 'Regulation of pyruvate dehydrogenase (PDH) complex',\n",
       " 'R-HSA-2046104': 'alpha-linolenic (omega3) and linoleic (omega6) acid metabolism',\n",
       " 'R-HSA-2046105': 'Linoleic acid (LA) metabolism',\n",
       " 'R-HSA-2046106': 'alpha-linolenic acid (ALA) metabolism',\n",
       " 'R-HSA-204626': 'Hypusine synthesis from eIF5A-lysine',\n",
       " 'R-HSA-204998': 'Cell death signalling via NRAGE, NRIF and NADE',\n",
       " 'R-HSA-205017': 'NFG and proNGF binds to p75NTR',\n",
       " 'R-HSA-205025': 'NADE modulates death signalling',\n",
       " 'R-HSA-205043': 'NRIF signals cell death from the nucleus',\n",
       " 'R-HSA-209543': 'p75NTR recruits signalling complexes',\n",
       " 'R-HSA-209560': 'NF-kB is activated and signals survival',\n",
       " 'R-HSA-209563': 'Axonal growth stimulation',\n",
       " 'R-HSA-209776': 'Metabolism of amine-derived hormones',\n",
       " 'R-HSA-209822': 'Glycoprotein hormones',\n",
       " 'R-HSA-209905': 'Catecholamine biosynthesis',\n",
       " 'R-HSA-209931': 'Serotonin and melatonin biosynthesis',\n",
       " 'R-HSA-209952': 'Peptide hormone biosynthesis',\n",
       " 'R-HSA-209968': 'Thyroxine biosynthesis',\n",
       " 'R-HSA-210455': 'Astrocytic Glutamate-Glutamine Uptake And Metabolism',\n",
       " 'R-HSA-210500': 'Glutamate Neurotransmitter Release Cycle',\n",
       " 'R-HSA-210744': 'Regulation of gene expression in late stage (branching morphogenesis) pancreatic bud precursor cells',\n",
       " 'R-HSA-210745': 'Regulation of gene expression in beta cells',\n",
       " 'R-HSA-210746': 'Regulation of gene expression in endocrine-committed (NEUROG3+) progenitor cells',\n",
       " 'R-HSA-210747': 'Regulation of gene expression in early pancreatic precursor cells',\n",
       " 'R-HSA-210990': 'PECAM1 interactions',\n",
       " 'R-HSA-210991': 'Basigin interactions',\n",
       " 'R-HSA-210993': 'Tie2 Signaling',\n",
       " 'R-HSA-211000': 'Gene Silencing by RNA',\n",
       " 'R-HSA-211163': 'AKT-mediated inactivation of FOXO1A',\n",
       " 'R-HSA-211728': 'Regulation of PAK-2p34 activity by PS-GAP/RHG10',\n",
       " 'R-HSA-211733': 'Regulation of activated PAK-2p34 by proteasome mediated degradation',\n",
       " 'R-HSA-211736': 'Stimulation of the cell death response by PAK-2p34',\n",
       " 'R-HSA-211859': 'Biological oxidations',\n",
       " 'R-HSA-211897': 'Cytochrome P450 - arranged by substrate type',\n",
       " 'R-HSA-211916': 'Vitamins',\n",
       " 'R-HSA-211935': 'Fatty acids',\n",
       " 'R-HSA-211945': 'Phase I - Functionalization of compounds',\n",
       " 'R-HSA-211957': 'Aromatic amines can be N-hydroxylated or N-dealkylated by CYP1A2',\n",
       " 'R-HSA-211958': 'Miscellaneous substrates',\n",
       " 'R-HSA-211976': 'Endogenous sterols',\n",
       " 'R-HSA-211979': 'Eicosanoids',\n",
       " 'R-HSA-211981': 'Xenobiotics',\n",
       " 'R-HSA-211994': 'Sterols are 12-hydroxylated by CYP8B1',\n",
       " 'R-HSA-211999': 'CYP2E1 reactions',\n",
       " 'R-HSA-212165': 'Epigenetic regulation of gene expression',\n",
       " 'R-HSA-2122947': 'NOTCH1 Intracellular Domain Regulates Transcription',\n",
       " 'R-HSA-2122948': 'Activated NOTCH1 Transmits Signal to the Nucleus',\n",
       " 'R-HSA-212300': 'PRC2 methylates histones and DNA',\n",
       " 'R-HSA-212436': 'Generic Transcription Pathway',\n",
       " 'R-HSA-212676': 'Dopamine Neurotransmitter Release Cycle',\n",
       " 'R-HSA-212718': 'EGFR interacts with phospholipase C-gamma',\n",
       " 'R-HSA-2129379': 'Molecules associated with elastic fibres',\n",
       " 'R-HSA-2132295': 'MHC class II antigen presentation',\n",
       " 'R-HSA-2142670': 'Synthesis of epoxy (EET) and dihydroxyeicosatrienoic acids (DHET)',\n",
       " 'R-HSA-2142688': 'Synthesis of 5-eicosatetraenoic acids',\n",
       " 'R-HSA-2142691': 'Synthesis of Leukotrienes (LT) and Eoxins (EX)',\n",
       " 'R-HSA-2142696': 'Synthesis of Hepoxilins (HX) and Trioxilins (TrX)',\n",
       " 'R-HSA-2142700': 'Synthesis of Lipoxins (LX)',\n",
       " 'R-HSA-2142712': 'Synthesis of 12-eicosatetraenoic acid derivatives',\n",
       " 'R-HSA-2142753': 'Arachidonic acid metabolism',\n",
       " 'R-HSA-2142770': 'Synthesis of 15-eicosatetraenoic acid derivatives',\n",
       " 'R-HSA-2142789': 'Ubiquinol biosynthesis',\n",
       " 'R-HSA-2142816': 'Synthesis of (16-20)-hydroxyeicosatetraenoic acids (HETE)',\n",
       " 'R-HSA-2142845': 'Hyaluronan metabolism',\n",
       " 'R-HSA-2142850': 'Hyaluronan biosynthesis and export',\n",
       " 'R-HSA-2151201': 'Transcriptional activation of mitochondrial biogenesis',\n",
       " 'R-HSA-2151209': 'Activation of PPARGC1A (PGC-1alpha) by phosphorylation',\n",
       " 'R-HSA-2160456': 'Phenylketonuria',\n",
       " 'R-HSA-216083': 'Integrin cell surface interactions',\n",
       " 'R-HSA-2160916': 'Hyaluronan uptake and degradation',\n",
       " 'R-HSA-2161517': 'Abacavir transmembrane transport',\n",
       " 'R-HSA-2161522': 'Abacavir ADME',\n",
       " 'R-HSA-2161541': 'Abacavir metabolism',\n",
       " 'R-HSA-2162123': 'Synthesis of Prostaglandins (PG) and Thromboxanes (TX)',\n",
       " 'R-HSA-2168880': 'Scavenging of heme from plasma',\n",
       " 'R-HSA-2172127': 'DAP12 interactions',\n",
       " 'R-HSA-217271': 'FMO oxidises nucleophiles',\n",
       " 'R-HSA-2173782': 'Binding and Uptake of Ligands by Scavenger Receptors',\n",
       " 'R-HSA-2173788': 'Downregulation of TGF-beta receptor signaling',\n",
       " 'R-HSA-2173789': 'TGF-beta receptor signaling activates SMADs',\n",
       " 'R-HSA-2173791': 'TGF-beta receptor signaling in EMT (epithelial to mesenchymal transition)',\n",
       " 'R-HSA-2173793': 'Transcriptional activity of SMAD2/SMAD3:SMAD4 heterotrimer',\n",
       " 'R-HSA-2173795': 'Downregulation of SMAD2/3:SMAD4 transcriptional activity',\n",
       " 'R-HSA-2173796': 'SMAD2/SMAD3:SMAD4 heterotrimer regulates transcription',\n",
       " 'R-HSA-2179392': 'EGFR Transactivation by Gastrin',\n",
       " 'R-HSA-2187335': 'The retinoid cycle in cones (daylight vision)',\n",
       " 'R-HSA-2187338': 'Visual phototransduction',\n",
       " 'R-HSA-2197563': 'NOTCH2 intracellular domain regulates transcription',\n",
       " 'R-HSA-2206280': 'MPS IX - Natowicz syndrome',\n",
       " 'R-HSA-2206281': 'Mucopolysaccharidoses',\n",
       " 'R-HSA-2206282': 'MPS IIIB - Sanfilippo syndrome B',\n",
       " 'R-HSA-2206285': 'MPS VI - Maroteaux-Lamy syndrome',\n",
       " 'R-HSA-2206290': 'MPS IV - Morquio syndrome A',\n",
       " 'R-HSA-2206291': 'MPS IIIC - Sanfilippo syndrome C',\n",
       " 'R-HSA-2206292': 'MPS VII - Sly syndrome',\n",
       " 'R-HSA-2206296': 'MPS II - Hunter syndrome',\n",
       " 'R-HSA-2206302': 'MPS I - Hurler syndrome',\n",
       " 'R-HSA-2206305': 'MPS IIID - Sanfilippo syndrome D',\n",
       " 'R-HSA-2206307': 'MPS IIIA - Sanfilippo syndrome A',\n",
       " 'R-HSA-2206308': 'MPS IV - Morquio syndrome B',\n",
       " 'R-HSA-2214320': 'Anchoring fibril formation',\n",
       " 'R-HSA-2219528': 'PI3K/AKT Signaling in Cancer',\n",
       " 'R-HSA-2219530': 'Constitutive Signaling by Aberrant PI3K in Cancer',\n",
       " 'R-HSA-2243919': 'Crosslinking of collagen fibrils',\n",
       " 'R-HSA-2262752': 'Cellular responses to stress',\n",
       " 'R-HSA-2299718': 'Condensation of Prophase Chromosomes',\n",
       " 'R-HSA-2393930': 'Phosphate bond hydrolysis by NUDT proteins',\n",
       " 'R-HSA-2395516': 'Electron transport from NADPH to Ferredoxin',\n",
       " 'R-HSA-2404192': 'Signaling by Type 1 Insulin-like Growth Factor 1 Receptor (IGF1R)',\n",
       " 'R-HSA-2408508': 'Metabolism of ingested SeMet, Sec, MeSec into H2Se',\n",
       " 'R-HSA-2408522': 'Selenoamino acid metabolism',\n",
       " 'R-HSA-2408550': 'Metabolism of ingested H2SeO4 and H2SeO3 into H2Se',\n",
       " 'R-HSA-2408552': 'Methylation of MeSeH for excretion',\n",
       " 'R-HSA-2408557': 'Selenocysteine synthesis',\n",
       " 'R-HSA-2424491': 'DAP12 signaling',\n",
       " 'R-HSA-2426168': 'Activation of gene expression by SREBF (SREBP)',\n",
       " 'R-HSA-2428924': 'IGF1R signaling cascade',\n",
       " 'R-HSA-2428928': 'IRS-related events triggered by IGF1R',\n",
       " 'R-HSA-2428933': 'SHC-related events triggered by IGF1R',\n",
       " 'R-HSA-2453864': 'Retinoid cycle disease events',\n",
       " 'R-HSA-2453902': 'The canonical retinoid cycle in rods (twilight vision)',\n",
       " 'R-HSA-2454202': 'Fc epsilon receptor (FCERI) signaling',\n",
       " 'R-HSA-2465910': 'MASTL Facilitates Mitotic Progression',\n",
       " 'R-HSA-2466712': 'Biosynthesis of A2E, implicated in retinal degradation',\n",
       " 'R-HSA-2467813': 'Separation of Sister Chromatids',\n",
       " 'R-HSA-2468052': 'Establishment of Sister Chromatid Cohesion',\n",
       " 'R-HSA-2470946': 'Cohesin Loading onto Chromatin',\n",
       " 'R-HSA-2473224': 'Antagonism of Activin by Follistatin',\n",
       " 'R-HSA-2474795': 'Diseases associated with visual transduction',\n",
       " 'R-HSA-2485179': 'Activation of the phototransduction cascade',\n",
       " 'R-HSA-2500257': 'Resolution of Sister Chromatid Cohesion',\n",
       " 'R-HSA-2514853': 'Condensation of Prometaphase Chromosomes',\n",
       " 'R-HSA-2514856': 'The phototransduction cascade',\n",
       " 'R-HSA-2514859': 'Inactivation, recovery and regulation of the phototransduction cascade',\n",
       " 'R-HSA-2534343': 'Interaction With Cumulus Cells And The Zona Pellucida',\n",
       " 'R-HSA-2555396': 'Mitotic Metaphase and Anaphase',\n",
       " 'R-HSA-2559580': 'Oxidative Stress Induced Senescence',\n",
       " 'R-HSA-2559582': 'Senescence-Associated Secretory Phenotype (SASP)',\n",
       " 'R-HSA-2559583': 'Cellular Senescence',\n",
       " 'R-HSA-2559584': 'Formation of Senescence-Associated Heterochromatin Foci (SAHF)',\n",
       " 'R-HSA-2559585': 'Oncogene Induced Senescence',\n",
       " 'R-HSA-2559586': 'DNA Damage/Telomere Stress Induced Senescence',\n",
       " 'R-HSA-2562578': 'TRIF-mediated programmed cell death',\n",
       " 'R-HSA-2564830': 'Cytosolic iron-sulfur cluster assembly',\n",
       " 'R-HSA-2565942': 'Regulation of PLK1 Activity at G2/M Transition',\n",
       " 'R-HSA-2586552': 'Signaling by Leptin',\n",
       " 'R-HSA-2644602': 'Signaling by NOTCH1 PEST Domain Mutants in Cancer',\n",
       " 'R-HSA-2644603': 'Signaling by NOTCH1 in Cancer',\n",
       " 'R-HSA-2644605': 'FBXW7 Mutants and NOTCH1 in Cancer',\n",
       " 'R-HSA-2644606': 'Constitutive Signaling by NOTCH1 PEST Domain Mutants',\n",
       " 'R-HSA-2644607': 'Loss of Function of FBXW7 in Cancer and NOTCH1 Signaling',\n",
       " 'R-HSA-264642': 'Acetylcholine Neurotransmitter Release Cycle',\n",
       " 'R-HSA-264870': 'Caspase-mediated cleavage of cytoskeletal proteins',\n",
       " 'R-HSA-264876': 'Insulin processing',\n",
       " 'R-HSA-2660825': 'Signaling by NOTCH1 t(7;9)(NOTCH1:M1580_K2555) Translocation Mutant',\n",
       " 'R-HSA-2660826': 'Constitutive Signaling by NOTCH1 t(7;9)(NOTCH1:M1580_K2555) Translocation Mutant',\n",
       " 'R-HSA-2672351': 'Stimuli-sensing channels',\n",
       " 'R-HSA-2682334': 'EPH-Ephrin signaling',\n",
       " 'R-HSA-2691230': 'Signaling by NOTCH1 HD Domain Mutants in Cancer',\n",
       " 'R-HSA-2691232': 'Constitutive Signaling by NOTCH1 HD Domain Mutants',\n",
       " 'R-HSA-2730905': 'Role of LAT2/NTAL/LAB on calcium mobilization',\n",
       " 'R-HSA-2855086': 'Ficolins bind to repetitive carbohydrate structures on the target cell surface',\n",
       " 'R-HSA-2871796': 'FCERI mediated MAPK activation',\n",
       " 'R-HSA-2871809': 'FCERI mediated Ca+2 mobilization',\n",
       " 'R-HSA-2871837': 'FCERI mediated NF-kB activation',\n",
       " 'R-HSA-2892245': 'POU5F1 (OCT4), SOX2, NANOG repress genes related to differentiation',\n",
       " 'R-HSA-2892247': 'POU5F1 (OCT4), SOX2, NANOG activate genes related to proliferation',\n",
       " 'R-HSA-2894858': 'Signaling by NOTCH1 HD+PEST Domain Mutants in Cancer',\n",
       " 'R-HSA-2894862': 'Constitutive Signaling by NOTCH1 HD+PEST Domain Mutants',\n",
       " 'R-HSA-2978092': 'Abnormal conversion of 2-oxoglutarate to 2-hydroxyglutarate',\n",
       " 'R-HSA-2979096': 'NOTCH2 Activation and Transmission of Signal to the Nucleus',\n",
       " 'R-HSA-2980736': 'Peptide hormone metabolism',\n",
       " 'R-HSA-2980766': 'Nuclear Envelope Breakdown',\n",
       " 'R-HSA-2980767': 'Activation of NIMA Kinases NEK9, NEK6, NEK7',\n",
       " 'R-HSA-2990846': 'SUMOylation',\n",
       " 'R-HSA-2995383': 'Initiation of Nuclear Envelope (NE) Reformation',\n",
       " 'R-HSA-2995410': 'Nuclear Envelope (NE) Reassembly',\n",
       " 'R-HSA-3000157': 'Laminin interactions',\n",
       " 'R-HSA-3000170': 'Syndecan interactions',\n",
       " 'R-HSA-3000171': 'Non-integrin membrane-ECM interactions',\n",
       " 'R-HSA-3000178': 'ECM proteoglycans',\n",
       " 'R-HSA-3000471': 'Scavenging by Class B Receptors',\n",
       " 'R-HSA-3000480': 'Scavenging by Class A Receptors',\n",
       " 'R-HSA-3000484': 'Scavenging by Class F Receptors',\n",
       " 'R-HSA-3000497': 'Scavenging by Class H Receptors',\n",
       " 'R-HSA-3065676': 'SUMO is conjugated to E1 (UBA2:SAE1)',\n",
       " 'R-HSA-3065678': 'SUMO is transferred from E1 to E2 (UBE2I, UBC9)',\n",
       " 'R-HSA-3065679': 'SUMO is proteolytically processed',\n",
       " 'R-HSA-3108214': 'SUMOylation of DNA damage response and repair proteins',\n",
       " 'R-HSA-3108232': 'SUMO E3 ligases SUMOylate target proteins',\n",
       " 'R-HSA-3134963': 'DEx/H-box helicases activate type I IFN and inflammatory cytokines production ',\n",
       " 'R-HSA-3134973': 'LRR FLII-interacting protein 1 (LRRFIP1) activates type I IFN production',\n",
       " 'R-HSA-3134975': 'Regulation of innate immune responses to cytosolic DNA',\n",
       " 'R-HSA-3214815': 'HDACs deacetylate histones',\n",
       " 'R-HSA-3214841': 'PKMTs methylate histone lysines',\n",
       " 'R-HSA-3214842': 'HDMs demethylate histones',\n",
       " 'R-HSA-3214847': 'HATs acetylate histones',\n",
       " 'R-HSA-3214858': 'RMTs methylate histone arginines',\n",
       " 'R-HSA-3215018': 'Processing and activation of SUMO',\n",
       " 'R-HSA-3229121': 'Glycogen storage diseases',\n",
       " 'R-HSA-3229133': 'Glycogen storage disease type Ib (SLC37A4)',\n",
       " 'R-HSA-3232118': 'SUMOylation of transcription factors',\n",
       " 'R-HSA-3232142': 'SUMOylation of ubiquitinylation proteins',\n",
       " 'R-HSA-3238698': 'WNT ligand biogenesis and trafficking',\n",
       " 'R-HSA-3247509': 'Chromatin modifying enzymes',\n",
       " 'R-HSA-3248023': 'Regulation by TREX1',\n",
       " 'R-HSA-3249367': 'STAT6-mediated induction of chemokines',\n",
       " 'R-HSA-3270619': 'IRF3-mediated induction of type I IFN',\n",
       " 'R-HSA-3274531': 'Glycogen storage disease type Ia (G6PC)',\n",
       " 'R-HSA-3282872': 'Severe congenital neutropenia type 4 (G6PC3)',\n",
       " 'R-HSA-3295583': 'TRP channels',\n",
       " 'R-HSA-3296197': 'Hydroxycarboxylic acid-binding receptors',\n",
       " 'R-HSA-3296469': 'Defects in cobalamin (B12) metabolism',\n",
       " 'R-HSA-3296482': 'Defects in vitamin and cofactor metabolism',\n",
       " 'R-HSA-3299685': 'Detoxification of Reactive Oxygen Species',\n",
       " 'R-HSA-3301854': 'Nuclear Pore Complex (NPC) Disassembly',\n",
       " 'R-HSA-3304347': 'Loss of Function of SMAD4 in Cancer',\n",
       " 'R-HSA-3304349': 'Loss of Function of SMAD2/3 in Cancer',\n",
       " 'R-HSA-3304351': 'Signaling by TGF-beta Receptor Complex in Cancer',\n",
       " 'R-HSA-3304356': 'SMAD2/3 Phosphorylation Motif Mutants in Cancer',\n",
       " 'R-HSA-3311021': 'SMAD4 MH2 Domain Mutants in Cancer',\n",
       " 'R-HSA-3315487': 'SMAD2/3 MH2 Domain Mutants in Cancer',\n",
       " 'R-HSA-3322077': 'Glycogen synthesis',\n",
       " 'R-HSA-3323169': 'Defects in biotin (Btn) metabolism',\n",
       " 'R-HSA-3359454': 'Defective TCN2 causes TCN2 deficiency',\n",
       " 'R-HSA-3359457': 'Defective CBLIF causes IFD',\n",
       " 'R-HSA-3359462': 'Defective AMN causes MGA1',\n",
       " 'R-HSA-3359463': 'Defective CUBN causes MGA1',\n",
       " 'R-HSA-3359467': 'Defective MTRR causes HMAE',\n",
       " 'R-HSA-3359469': 'Defective MTR causes HMAG',\n",
       " 'R-HSA-3359471': 'Defective MMAB causes MMA, cblB type',\n",
       " 'R-HSA-3359473': 'Defective MMADHC causes MMAHCD',\n",
       " 'R-HSA-3359474': 'Defective MMACHC causes MAHCC',\n",
       " 'R-HSA-3359475': 'Defective MMAA causes MMA, cblA type',\n",
       " 'R-HSA-3359478': 'Defective MUT causes MMAM',\n",
       " 'R-HSA-3359485': 'Defective CD320 causes MMATC',\n",
       " 'R-HSA-3371378': 'Regulation by c-FLIP',\n",
       " 'R-HSA-3371453': 'Regulation of HSF1-mediated heat shock response',\n",
       " 'R-HSA-3371497': 'HSP90 chaperone cycle for steroid hormone receptors (SHR) in the presence of ligand',\n",
       " 'R-HSA-3371511': 'HSF1 activation',\n",
       " 'R-HSA-3371556': 'Cellular response to heat stress',\n",
       " 'R-HSA-3371568': 'Attenuation phase',\n",
       " 'R-HSA-3371571': 'HSF1-dependent transactivation',\n",
       " 'R-HSA-3371598': 'Defective BTD causes biotidinase deficiency',\n",
       " 'R-HSA-3371599': 'Defective HLCS causes multiple carboxylase deficiency',\n",
       " 'R-HSA-349425': 'Autodegradation of the E3 ubiquitin ligase COP1',\n",
       " 'R-HSA-350054': 'Notch-HLH transcription pathway',\n",
       " 'R-HSA-350562': 'Regulation of ornithine decarboxylase (ODC)',\n",
       " 'R-HSA-350864': 'Regulation of thyroid hormone activity',\n",
       " 'R-HSA-351143': 'Agmatine biosynthesis',\n",
       " 'R-HSA-351200': 'Interconversion of polyamines',\n",
       " 'R-HSA-351202': 'Metabolism of polyamines',\n",
       " 'R-HSA-351906': 'Apoptotic cleavage of cell adhesion  proteins',\n",
       " 'R-HSA-352230': 'Amino acid transport across the plasma membrane',\n",
       " 'R-HSA-352238': 'Breakdown of the nuclear lamina',\n",
       " 'R-HSA-354192': 'Integrin signaling',\n",
       " 'R-HSA-354194': 'GRB2:SOS provides linkage to MAPK signaling for Integrins ',\n",
       " 'R-HSA-3560782': 'Diseases associated with glycosaminoglycan metabolism',\n",
       " 'R-HSA-3560783': 'Defective B4GALT7 causes EDS, progeroid type',\n",
       " 'R-HSA-3560792': 'Defective SLC26A2 causes chondrodysplasias',\n",
       " 'R-HSA-3560796': 'Defective PAPSS2 causes SEMD-PA',\n",
       " 'R-HSA-3560801': 'Defective B3GAT3 causes JDSSDHD',\n",
       " 'R-HSA-3595172': 'Defective CHST3 causes SEDCJD',\n",
       " 'R-HSA-3595174': 'Defective CHST14 causes EDS, musculocontractural type',\n",
       " 'R-HSA-3595177': 'Defective CHSY1 causes TPBS',\n",
       " 'R-HSA-3642278': 'Loss of Function of TGFBR2 in Cancer',\n",
       " 'R-HSA-3642279': 'TGFBR2 MSI Frameshift Mutants in Cancer',\n",
       " 'R-HSA-3645790': 'TGFBR2 Kinase Domain Mutants in Cancer',\n",
       " 'R-HSA-3656225': 'Defective CHST6 causes MCDC1',\n",
       " 'R-HSA-3656234': 'Defective HEXA causes GM2G1',\n",
       " 'R-HSA-3656237': 'Defective EXT2 causes exostoses 2',\n",
       " 'R-HSA-3656243': 'Defective ST3GAL3 causes MCT12 and EIEE15',\n",
       " 'R-HSA-3656244': 'Defective B4GALT1 causes B4GALT1-CDG (CDG-2d)',\n",
       " 'R-HSA-3656248': 'Defective HEXB causes GM2G2',\n",
       " 'R-HSA-3656253': 'Defective EXT1 causes exostoses 1, TRPS2 and CHDS',\n",
       " 'R-HSA-3656532': 'TGFBR1 KD Mutants in Cancer',\n",
       " 'R-HSA-3656534': 'Loss of Function of TGFBR1 in Cancer',\n",
       " 'R-HSA-3656535': 'TGFBR1 LBD Mutants in Cancer',\n",
       " 'R-HSA-3700989': 'Transcriptional Regulation by TP53',\n",
       " 'R-HSA-372708': 'p130Cas linkage to MAPK signaling for integrins',\n",
       " 'R-HSA-372790': 'Signaling by GPCR',\n",
       " 'R-HSA-373076': 'Class A/1 (Rhodopsin-like receptors)',\n",
       " 'R-HSA-373080': 'Class B/2 (Secretin family receptors)',\n",
       " 'R-HSA-373752': 'Netrin-1 signaling',\n",
       " 'R-HSA-373753': 'Nephrin family interactions',\n",
       " 'R-HSA-373755': 'Semaphorin interactions',\n",
       " 'R-HSA-373756': 'SDK interactions',\n",
       " 'R-HSA-373760': 'L1CAM interactions',\n",
       " 'R-HSA-375165': 'NCAM signaling for neurite out-growth',\n",
       " 'R-HSA-375276': 'Peptide ligand-binding receptors',\n",
       " 'R-HSA-375280': 'Amine ligand-binding receptors',\n",
       " 'R-HSA-375281': 'Hormone ligand-binding receptors',\n",
       " 'R-HSA-376172': 'DSCAM interactions',\n",
       " 'R-HSA-376176': 'Signaling by ROBO receptors',\n",
       " 'R-HSA-3769402': 'Deactivation of the beta-catenin transactivating complex',\n",
       " 'R-HSA-3772470': 'Negative regulation of TCF-dependent signaling by WNT ligand antagonists',\n",
       " 'R-HSA-3781860': 'Diseases associated with N-glycosylation of proteins',\n",
       " 'R-HSA-3781865': 'Diseases of glycosylation',\n",
       " 'R-HSA-3785653': 'Myoclonic epilepsy of Lafora',\n",
       " 'R-HSA-379397': 'Enzymatic degradation of dopamine by COMT',\n",
       " 'R-HSA-379398': 'Enzymatic degradation of Dopamine by monoamine oxidase',\n",
       " 'R-HSA-379401': 'Dopamine clearance from the synaptic cleft',\n",
       " 'R-HSA-379716': 'Cytosolic tRNA aminoacylation',\n",
       " 'R-HSA-379724': 'tRNA Aminoacylation',\n",
       " 'R-HSA-379726': 'Mitochondrial tRNA aminoacylation',\n",
       " 'R-HSA-380095': 'Tachykinin receptors bind tachykinins',\n",
       " 'R-HSA-380108': 'Chemokine receptors bind chemokines',\n",
       " 'R-HSA-380259': 'Loss of Nlp from mitotic centrosomes',\n",
       " 'R-HSA-380270': 'Recruitment of mitotic centrosome proteins and complexes',\n",
       " 'R-HSA-380284': 'Loss of proteins required for interphase microtubule organization from the centrosome',\n",
       " 'R-HSA-380287': 'Centrosome maturation',\n",
       " 'R-HSA-380320': 'Recruitment of NuMA to mitotic centrosomes',\n",
       " 'R-HSA-380612': 'Metabolism of serotonin',\n",
       " 'R-HSA-380615': 'Serotonin clearance from the synaptic cleft',\n",
       " 'R-HSA-380972': 'Energy dependent regulation of mTOR by LKB1-AMPK',\n",
       " 'R-HSA-380994': 'ATF4 activates genes in response to endoplasmic reticulum  stress',\n",
       " 'R-HSA-381033': 'ATF6 (ATF6-alpha) activates chaperones',\n",
       " 'R-HSA-381038': 'XBP1(S) activates chaperone genes',\n",
       " 'R-HSA-381042': 'PERK regulates gene expression',\n",
       " 'R-HSA-381070': 'IRE1alpha activates chaperones',\n",
       " 'R-HSA-381119': 'Unfolded Protein Response (UPR)',\n",
       " 'R-HSA-381183': 'ATF6 (ATF6-alpha) activates chaperone genes',\n",
       " 'R-HSA-381340': 'Transcriptional regulation of white adipocyte differentiation',\n",
       " 'R-HSA-381426': 'Regulation of Insulin-like Growth Factor (IGF) transport and uptake by Insulin-like Growth Factor Binding Proteins (IGFBPs)',\n",
       " 'R-HSA-3814836': 'Glycogen storage disease type XV (GYG1)',\n",
       " 'R-HSA-381676': 'Glucagon-like Peptide-1 (GLP1) regulates insulin secretion',\n",
       " 'R-HSA-381753': 'Olfactory Signaling Pathway',\n",
       " 'R-HSA-381771': 'Synthesis, secretion, and inactivation of Glucagon-like Peptide-1 (GLP-1)',\n",
       " 'R-HSA-382551': 'Transport of small molecules',\n",
       " 'R-HSA-382556': 'ABC-family proteins mediated transport',\n",
       " 'R-HSA-3828062': 'Glycogen storage disease type 0 (muscle GYS1)',\n",
       " 'R-HSA-383280': 'Nuclear Receptor transcription pathway',\n",
       " 'R-HSA-3858494': 'Beta-catenin independent WNT signaling',\n",
       " 'R-HSA-3858516': 'Glycogen storage disease type 0 (liver GYS2)',\n",
       " 'R-HSA-3878781': 'Glycogen storage disease type IV (GBE1)',\n",
       " 'R-HSA-388396': 'GPCR downstream signalling',\n",
       " 'R-HSA-388479': 'Vasopressin-like receptors',\n",
       " 'R-HSA-388841': 'Costimulation by the CD28 family',\n",
       " 'R-HSA-388844': 'Receptor-type tyrosine-protein phosphatases',\n",
       " 'R-HSA-389356': 'CD28 co-stimulation',\n",
       " 'R-HSA-389357': 'CD28 dependent PI3K/Akt signaling',\n",
       " 'R-HSA-389359': 'CD28 dependent Vav1 pathway',\n",
       " 'R-HSA-389397': 'Orexin and neuropeptides FF and QRFP bind to their respective receptors',\n",
       " 'R-HSA-389513': 'CTLA4 inhibitory signaling',\n",
       " 'R-HSA-389542': 'NADPH regeneration',\n",
       " 'R-HSA-389599': 'Alpha-oxidation of phytanate',\n",
       " 'R-HSA-389661': 'Glyoxylate metabolism and glycine degradation',\n",
       " 'R-HSA-389887': 'Beta-oxidation of pristanoyl-CoA',\n",
       " 'R-HSA-3899300': 'SUMOylation of transcription cofactors',\n",
       " 'R-HSA-389948': 'PD-1 signaling',\n",
       " 'R-HSA-389957': 'Prefoldin mediated transfer of substrate  to CCT/TriC',\n",
       " 'R-HSA-389958': 'Cooperation of Prefoldin and TriC/CCT  in actin and tubulin folding',\n",
       " 'R-HSA-389960': 'Formation of tubulin folding intermediates by CCT/TriC',\n",
       " 'R-HSA-389977': 'Post-chaperonin tubulin folding pathway',\n",
       " 'R-HSA-390247': 'Beta-oxidation of very long chain fatty acids',\n",
       " 'R-HSA-390450': 'Folding of actin by CCT/TriC',\n",
       " 'R-HSA-390466': 'Chaperonin-mediated protein folding',\n",
       " 'R-HSA-390471': 'Association of TriC/CCT with target proteins during biosynthesis',\n",
       " 'R-HSA-390522': 'Striated Muscle Contraction',\n",
       " 'R-HSA-390648': 'Muscarinic acetylcholine receptors',\n",
       " 'R-HSA-390650': 'Histamine receptors',\n",
       " 'R-HSA-390651': 'Dopamine receptors',\n",
       " 'R-HSA-390666': 'Serotonin receptors',\n",
       " 'R-HSA-390696': 'Adrenoceptors',\n",
       " 'R-HSA-3906995': 'Diseases associated with O-glycosylation of proteins',\n",
       " 'R-HSA-390918': 'Peroxisomal lipid metabolism',\n",
       " 'R-HSA-391160': 'Signal regulatory protein family interactions',\n",
       " 'R-HSA-391251': 'Protein folding',\n",
       " 'R-HSA-391903': 'Eicosanoid ligand-binding receptors',\n",
       " 'R-HSA-391906': 'Leukotriene receptors',\n",
       " 'R-HSA-391908': 'Prostanoid ligand receptors',\n",
       " 'R-HSA-392023': 'Adrenaline signalling through Alpha-2 adrenergic receptor',\n",
       " 'R-HSA-392154': 'Nitric oxide stimulates guanylate cyclase',\n",
       " 'R-HSA-392170': 'ADP signalling through P2Y purinoceptor 12',\n",
       " 'R-HSA-392451': 'G beta:gamma signalling through PI3Kgamma',\n",
       " 'R-HSA-392499': 'Metabolism of proteins',\n",
       " 'R-HSA-392517': 'Rap1 signalling',\n",
       " 'R-HSA-392518': 'Signal amplification',\n",
       " 'R-HSA-392851': 'Prostacyclin signalling through prostacyclin receptor',\n",
       " 'R-HSA-3928662': 'EPHB-mediated forward signaling',\n",
       " 'R-HSA-3928663': 'EPHA-mediated growth cone collapse',\n",
       " 'R-HSA-3928664': 'Ephrin signaling',\n",
       " 'R-HSA-3928665': 'EPH-ephrin mediated repulsion of cells',\n",
       " 'R-HSA-397014': 'Muscle contraction',\n",
       " 'R-HSA-397795': 'G-protein beta:gamma signalling',\n",
       " 'R-HSA-399710': 'Activation of AMPA receptors',\n",
       " 'R-HSA-399719': 'Trafficking of AMPA receptors',\n",
       " 'R-HSA-399721': 'Glutamate binding, activation of AMPA receptors and synaptic plasticity',\n",
       " 'R-HSA-399954': 'Sema3A PAK dependent Axon repulsion',\n",
       " 'R-HSA-399955': 'SEMA3A-Plexin repulsion signaling by inhibiting Integrin adhesion',\n",
       " ...}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Make two lists to zip together\n",
    "reactome_uniprot = sspa.process_reactome(\"Homo sapiens\",\"/Users/sarapatti/Documents/Code MRes DS SP 2022-2023/Databases/UniProt2Reactome_All_Levels.txt\") \n",
    "reactome_uniprot_id = reactome_uniprot.index\n",
    "reactome_uniprot_pathway = reactome_uniprot[\"Pathway_name\"]\n",
    "\n",
    "# Make a dictionary with two lists \n",
    "reactome_dict = dict(zip(reactome_uniprot_id, reactome_uniprot_pathway)) # zip is a way of pulling two lists together (need to be same length) and put it into a tupple (pair of things)\n",
    "reactome_dict\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "names_pathways = reactome_pathways.iloc[:,:1]\n",
    "names_pathways.index.name = 'Node'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "          Pathway_1      Pathway_2  Correlation  Correlation_abs  \\\n",
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       "22197  4.199130e-01  1.000000e+00             -0.000000                  0.0  \n",
       "22198  1.831827e-01  1.000000e+00             -0.000000                  0.5  \n",
       "22199  7.262464e-01  1.000000e+00             -0.000000                  0.0  \n",
       "22200  0.000000e+00  0.000000e+00                   inf                  0.5  \n",
       "\n",
       "[22201 rows x 8 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read in protein and metabolite data\n",
    "\n",
    "#All metabolite data from Metabolomics_EDA\n",
    "metabolite_data = pd.read_csv('/Users/sarapatti/Documents/Code MRes DS SP 2022-2023/Exported files/metabolite_all.csv',index_col=0)\n",
    "metabolite_data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>204697</th>\n",
       "      <td>R-HSA-5658442</td>\n",
       "      <td>R-HSA-198725</td>\n",
       "      <td>0.127387</td>\n",
       "      <td>0.127387</td>\n",
       "      <td>0.041298</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204696</th>\n",
       "      <td>R-HSA-5658442</td>\n",
       "      <td>R-HSA-1971475</td>\n",
       "      <td>0.070057</td>\n",
       "      <td>0.070057</td>\n",
       "      <td>0.263138</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204695</th>\n",
       "      <td>R-HSA-5658442</td>\n",
       "      <td>R-HSA-196854</td>\n",
       "      <td>-0.090006</td>\n",
       "      <td>0.090006</td>\n",
       "      <td>0.150209</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204718</th>\n",
       "      <td>R-HSA-5658442</td>\n",
       "      <td>R-HSA-202733</td>\n",
       "      <td>0.088104</td>\n",
       "      <td>0.088104</td>\n",
       "      <td>0.159050</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235635</th>\n",
       "      <td>R-HSA-69656</td>\n",
       "      <td>R-HSA-196741</td>\n",
       "      <td>0.180385</td>\n",
       "      <td>0.180385</td>\n",
       "      <td>0.003713</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>328329 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Pathway_1      Pathway_2  Correlation  Correlation_abs     p_val  \\\n",
       "0        R-HSA-109581   R-HSA-109581     1.000000         1.000000  0.000000   \n",
       "93159   R-HSA-2033519  R-HSA-5654693     1.000000         1.000000  0.000000   \n",
       "93160   R-HSA-2033519  R-HSA-5654695     1.000000         1.000000  0.000000   \n",
       "93161   R-HSA-2033519  R-HSA-5654696     1.000000         1.000000  0.000000   \n",
       "93162   R-HSA-2033519  R-HSA-5654699     1.000000         1.000000  0.000000   \n",
       "...               ...            ...          ...              ...       ...   \n",
       "204697  R-HSA-5658442   R-HSA-198725     0.127387         0.127387  0.041298   \n",
       "204696  R-HSA-5658442  R-HSA-1971475     0.070057         0.070057  0.263138   \n",
       "204695  R-HSA-5658442   R-HSA-196854    -0.090006         0.090006  0.150209   \n",
       "204718  R-HSA-5658442   R-HSA-202733     0.088104         0.088104  0.159050   \n",
       "235635    R-HSA-69656   R-HSA-196741     0.180385         0.180385  0.003713   \n",
       "\n",
       "        p_val_adjust  log10_p_val_adjusted  Overlap_Correlation  \n",
       "0                0.0                   inf                  1.0  \n",
       "93159            0.0                   inf                  0.0  \n",
       "93160            0.0                   inf                  0.0  \n",
       "93161            0.0                   inf                  0.0  \n",
       "93162            0.0                   inf                  0.0  \n",
       "...              ...                   ...                  ...  \n",
       "204697           1.0                  -0.0                  0.0  \n",
       "204696           1.0                  -0.0                  0.0  \n",
       "204695           1.0                  -0.0                  0.0  \n",
       "204718           1.0                  -0.0                  0.0  \n",
       "235635           1.0                  -0.0                  0.0  \n",
       "\n",
       "[328329 rows x 8 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#All protein data from Proteomics_EDA\n",
    "protein_data =  pd.read_csv('/Users/sarapatti/Documents/Code MRes DS SP 2022-2023/Exported files/protein_all.csv', index_col=0)\n",
    "protein_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#All protein data from Metabolomics Node data\n",
    "protein_data_PA_score = pd.read_csv('/Users/sarapatti/Documents/Code MRes DS SP 2022-2023/Exported files/Protein_pathwayscores.csv',index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#All metabolite data from Metabolomics Node data\n",
    "metabolite_data_PA_score = pd.read_csv('/Users/sarapatti/Documents/Code MRes DS SP 2022-2023/Exported files/Metabolite_pathwayscores.csv',index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make dataframe with average score for metabolites and proteins\n",
    "merge_pathwayscores = pd.merge(metabolite_data_PA_score, protein_data_PA_score, on=\"Node\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.25"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "costs = np.column_stack(([3, 2, 1, 3], [7, 6, 6, 5]))\n",
    "costs\n",
    "\n",
    "mean_costs = np.mean(costs[:,0])\n",
    "mean_costs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Node</th>\n",
       "      <th>Healthy_mean</th>\n",
       "      <th>Mild_mean</th>\n",
       "      <th>Moderate_mean</th>\n",
       "      <th>Severe_mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>R-HSA-112315</td>\n",
       "      <td>-0.241658</td>\n",
       "      <td>0.279002</td>\n",
       "      <td>0.187481</td>\n",
       "      <td>0.227342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>R-HSA-1428517</td>\n",
       "      <td>-0.187296</td>\n",
       "      <td>0.236115</td>\n",
       "      <td>0.112211</td>\n",
       "      <td>0.187404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>R-HSA-1483257</td>\n",
       "      <td>-0.272196</td>\n",
       "      <td>0.454792</td>\n",
       "      <td>0.133978</td>\n",
       "      <td>0.199945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>R-HSA-156580</td>\n",
       "      <td>-0.222747</td>\n",
       "      <td>0.144694</td>\n",
       "      <td>0.213035</td>\n",
       "      <td>0.325688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>R-HSA-15869</td>\n",
       "      <td>-0.036131</td>\n",
       "      <td>0.042835</td>\n",
       "      <td>0.004563</td>\n",
       "      <td>0.017219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>R-HSA-1592230</td>\n",
       "      <td>-0.074145</td>\n",
       "      <td>0.081413</td>\n",
       "      <td>0.064120</td>\n",
       "      <td>0.033541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>R-HSA-168249</td>\n",
       "      <td>-0.215081</td>\n",
       "      <td>0.157009</td>\n",
       "      <td>0.199603</td>\n",
       "      <td>0.291898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>R-HSA-196071</td>\n",
       "      <td>0.005790</td>\n",
       "      <td>-0.045410</td>\n",
       "      <td>0.031995</td>\n",
       "      <td>-0.008271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>R-HSA-196849</td>\n",
       "      <td>-0.192159</td>\n",
       "      <td>0.003532</td>\n",
       "      <td>0.224535</td>\n",
       "      <td>0.398576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>R-HSA-196854</td>\n",
       "      <td>0.175658</td>\n",
       "      <td>-0.189589</td>\n",
       "      <td>-0.113094</td>\n",
       "      <td>-0.232238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>R-HSA-211859</td>\n",
       "      <td>0.055415</td>\n",
       "      <td>-0.006292</td>\n",
       "      <td>-0.066810</td>\n",
       "      <td>-0.062597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>R-HSA-211945</td>\n",
       "      <td>-0.039946</td>\n",
       "      <td>0.097626</td>\n",
       "      <td>0.011073</td>\n",
       "      <td>-0.020973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>R-HSA-212436</td>\n",
       "      <td>-0.200270</td>\n",
       "      <td>0.065766</td>\n",
       "      <td>0.201638</td>\n",
       "      <td>0.355193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>R-HSA-2142753</td>\n",
       "      <td>0.066685</td>\n",
       "      <td>-0.160753</td>\n",
       "      <td>-0.001416</td>\n",
       "      <td>0.035021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>R-HSA-2151201</td>\n",
       "      <td>-0.000355</td>\n",
       "      <td>-0.072026</td>\n",
       "      <td>0.044624</td>\n",
       "      <td>0.012768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>R-HSA-2187338</td>\n",
       "      <td>-0.110235</td>\n",
       "      <td>0.091909</td>\n",
       "      <td>0.108784</td>\n",
       "      <td>0.081690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>R-HSA-2262752</td>\n",
       "      <td>0.184147</td>\n",
       "      <td>-0.252343</td>\n",
       "      <td>-0.101861</td>\n",
       "      <td>-0.189248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>R-HSA-2408508</td>\n",
       "      <td>-0.241867</td>\n",
       "      <td>-0.005920</td>\n",
       "      <td>0.335772</td>\n",
       "      <td>0.431489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>R-HSA-2408522</td>\n",
       "      <td>-0.241867</td>\n",
       "      <td>-0.005920</td>\n",
       "      <td>0.335772</td>\n",
       "      <td>0.431489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>R-HSA-2980736</td>\n",
       "      <td>0.052633</td>\n",
       "      <td>0.075818</td>\n",
       "      <td>-0.134042</td>\n",
       "      <td>-0.063979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>R-HSA-372790</td>\n",
       "      <td>0.170780</td>\n",
       "      <td>-0.193996</td>\n",
       "      <td>-0.110009</td>\n",
       "      <td>-0.208897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>R-HSA-373076</td>\n",
       "      <td>0.178433</td>\n",
       "      <td>-0.183682</td>\n",
       "      <td>-0.121126</td>\n",
       "      <td>-0.241623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>R-HSA-381340</td>\n",
       "      <td>0.112019</td>\n",
       "      <td>-0.227758</td>\n",
       "      <td>-0.022594</td>\n",
       "      <td>-0.075415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>R-HSA-388396</td>\n",
       "      <td>0.170162</td>\n",
       "      <td>-0.212847</td>\n",
       "      <td>-0.101309</td>\n",
       "      <td>-0.191585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>R-HSA-416476</td>\n",
       "      <td>-0.104926</td>\n",
       "      <td>0.129922</td>\n",
       "      <td>0.111309</td>\n",
       "      <td>-0.014171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>R-HSA-418555</td>\n",
       "      <td>0.044163</td>\n",
       "      <td>0.056599</td>\n",
       "      <td>-0.067910</td>\n",
       "      <td>-0.111248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>R-HSA-418594</td>\n",
       "      <td>0.047369</td>\n",
       "      <td>-0.091895</td>\n",
       "      <td>-0.012613</td>\n",
       "      <td>0.016423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>R-HSA-428157</td>\n",
       "      <td>-0.075110</td>\n",
       "      <td>0.151212</td>\n",
       "      <td>0.048349</td>\n",
       "      <td>0.028489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>R-HSA-500792</td>\n",
       "      <td>0.163994</td>\n",
       "      <td>-0.161777</td>\n",
       "      <td>-0.113171</td>\n",
       "      <td>-0.226731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>R-HSA-556833</td>\n",
       "      <td>-0.247140</td>\n",
       "      <td>0.270047</td>\n",
       "      <td>0.188280</td>\n",
       "      <td>0.291152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>R-HSA-5663205</td>\n",
       "      <td>-0.245883</td>\n",
       "      <td>0.146065</td>\n",
       "      <td>0.236580</td>\n",
       "      <td>0.379109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>R-HSA-5668914</td>\n",
       "      <td>-0.271694</td>\n",
       "      <td>0.201255</td>\n",
       "      <td>0.264206</td>\n",
       "      <td>0.374672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>R-HSA-5683826</td>\n",
       "      <td>-0.337681</td>\n",
       "      <td>0.141538</td>\n",
       "      <td>0.400119</td>\n",
       "      <td>0.506022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>R-HSA-597592</td>\n",
       "      <td>-0.259195</td>\n",
       "      <td>0.036379</td>\n",
       "      <td>0.328007</td>\n",
       "      <td>0.429836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>R-HSA-6806667</td>\n",
       "      <td>-0.109838</td>\n",
       "      <td>0.095959</td>\n",
       "      <td>0.103089</td>\n",
       "      <td>0.089136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>R-HSA-70326</td>\n",
       "      <td>-0.109622</td>\n",
       "      <td>0.164048</td>\n",
       "      <td>0.034656</td>\n",
       "      <td>0.165287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>R-HSA-71291</td>\n",
       "      <td>-0.009589</td>\n",
       "      <td>-0.073429</td>\n",
       "      <td>0.079531</td>\n",
       "      <td>0.049991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>R-HSA-71387</td>\n",
       "      <td>-0.219420</td>\n",
       "      <td>0.031258</td>\n",
       "      <td>0.275416</td>\n",
       "      <td>0.382850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>R-HSA-71406</td>\n",
       "      <td>-0.187296</td>\n",
       "      <td>0.236115</td>\n",
       "      <td>0.112211</td>\n",
       "      <td>0.187404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>R-HSA-73857</td>\n",
       "      <td>-0.200270</td>\n",
       "      <td>0.065766</td>\n",
       "      <td>0.201638</td>\n",
       "      <td>0.355193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>R-HSA-73884</td>\n",
       "      <td>-0.272610</td>\n",
       "      <td>0.188668</td>\n",
       "      <td>0.247124</td>\n",
       "      <td>0.428210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>R-HSA-8956319</td>\n",
       "      <td>-0.244214</td>\n",
       "      <td>0.149759</td>\n",
       "      <td>0.243807</td>\n",
       "      <td>0.376801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>R-HSA-8956321</td>\n",
       "      <td>-0.315946</td>\n",
       "      <td>0.231490</td>\n",
       "      <td>0.291077</td>\n",
       "      <td>0.461152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>R-HSA-8957322</td>\n",
       "      <td>-0.085679</td>\n",
       "      <td>0.067873</td>\n",
       "      <td>0.083170</td>\n",
       "      <td>0.068733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>R-HSA-8978868</td>\n",
       "      <td>0.191272</td>\n",
       "      <td>-0.094991</td>\n",
       "      <td>-0.222125</td>\n",
       "      <td>-0.209842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>R-HSA-9707564</td>\n",
       "      <td>-0.161362</td>\n",
       "      <td>0.105971</td>\n",
       "      <td>0.162012</td>\n",
       "      <td>0.173293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>R-HSA-9707616</td>\n",
       "      <td>-0.161362</td>\n",
       "      <td>0.105971</td>\n",
       "      <td>0.162012</td>\n",
       "      <td>0.173293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>R-HSA-9711123</td>\n",
       "      <td>0.157557</td>\n",
       "      <td>-0.277151</td>\n",
       "      <td>-0.042688</td>\n",
       "      <td>-0.151149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>R-HSA-975634</td>\n",
       "      <td>-0.105812</td>\n",
       "      <td>0.073885</td>\n",
       "      <td>0.106693</td>\n",
       "      <td>0.098152</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Node  Healthy_mean  Mild_mean  Moderate_mean  Severe_mean\n",
       "0    R-HSA-112315     -0.241658   0.279002       0.187481     0.227342\n",
       "1   R-HSA-1428517     -0.187296   0.236115       0.112211     0.187404\n",
       "2   R-HSA-1483257     -0.272196   0.454792       0.133978     0.199945\n",
       "3    R-HSA-156580     -0.222747   0.144694       0.213035     0.325688\n",
       "4     R-HSA-15869     -0.036131   0.042835       0.004563     0.017219\n",
       "5   R-HSA-1592230     -0.074145   0.081413       0.064120     0.033541\n",
       "6    R-HSA-168249     -0.215081   0.157009       0.199603     0.291898\n",
       "7    R-HSA-196071      0.005790  -0.045410       0.031995    -0.008271\n",
       "8    R-HSA-196849     -0.192159   0.003532       0.224535     0.398576\n",
       "9    R-HSA-196854      0.175658  -0.189589      -0.113094    -0.232238\n",
       "10   R-HSA-211859      0.055415  -0.006292      -0.066810    -0.062597\n",
       "11   R-HSA-211945     -0.039946   0.097626       0.011073    -0.020973\n",
       "12   R-HSA-212436     -0.200270   0.065766       0.201638     0.355193\n",
       "13  R-HSA-2142753      0.066685  -0.160753      -0.001416     0.035021\n",
       "14  R-HSA-2151201     -0.000355  -0.072026       0.044624     0.012768\n",
       "15  R-HSA-2187338     -0.110235   0.091909       0.108784     0.081690\n",
       "16  R-HSA-2262752      0.184147  -0.252343      -0.101861    -0.189248\n",
       "17  R-HSA-2408508     -0.241867  -0.005920       0.335772     0.431489\n",
       "18  R-HSA-2408522     -0.241867  -0.005920       0.335772     0.431489\n",
       "19  R-HSA-2980736      0.052633   0.075818      -0.134042    -0.063979\n",
       "20   R-HSA-372790      0.170780  -0.193996      -0.110009    -0.208897\n",
       "21   R-HSA-373076      0.178433  -0.183682      -0.121126    -0.241623\n",
       "22   R-HSA-381340      0.112019  -0.227758      -0.022594    -0.075415\n",
       "23   R-HSA-388396      0.170162  -0.212847      -0.101309    -0.191585\n",
       "24   R-HSA-416476     -0.104926   0.129922       0.111309    -0.014171\n",
       "25   R-HSA-418555      0.044163   0.056599      -0.067910    -0.111248\n",
       "26   R-HSA-418594      0.047369  -0.091895      -0.012613     0.016423\n",
       "27   R-HSA-428157     -0.075110   0.151212       0.048349     0.028489\n",
       "28   R-HSA-500792      0.163994  -0.161777      -0.113171    -0.226731\n",
       "29   R-HSA-556833     -0.247140   0.270047       0.188280     0.291152\n",
       "30  R-HSA-5663205     -0.245883   0.146065       0.236580     0.379109\n",
       "31  R-HSA-5668914     -0.271694   0.201255       0.264206     0.374672\n",
       "32  R-HSA-5683826     -0.337681   0.141538       0.400119     0.506022\n",
       "33   R-HSA-597592     -0.259195   0.036379       0.328007     0.429836\n",
       "34  R-HSA-6806667     -0.109838   0.095959       0.103089     0.089136\n",
       "35    R-HSA-70326     -0.109622   0.164048       0.034656     0.165287\n",
       "36    R-HSA-71291     -0.009589  -0.073429       0.079531     0.049991\n",
       "37    R-HSA-71387     -0.219420   0.031258       0.275416     0.382850\n",
       "38    R-HSA-71406     -0.187296   0.236115       0.112211     0.187404\n",
       "39    R-HSA-73857     -0.200270   0.065766       0.201638     0.355193\n",
       "40    R-HSA-73884     -0.272610   0.188668       0.247124     0.428210\n",
       "41  R-HSA-8956319     -0.244214   0.149759       0.243807     0.376801\n",
       "42  R-HSA-8956321     -0.315946   0.231490       0.291077     0.461152\n",
       "43  R-HSA-8957322     -0.085679   0.067873       0.083170     0.068733\n",
       "44  R-HSA-8978868      0.191272  -0.094991      -0.222125    -0.209842\n",
       "45  R-HSA-9707564     -0.161362   0.105971       0.162012     0.173293\n",
       "46  R-HSA-9707616     -0.161362   0.105971       0.162012     0.173293\n",
       "47  R-HSA-9711123      0.157557  -0.277151      -0.042688    -0.151149\n",
       "48   R-HSA-975634     -0.105812   0.073885       0.106693     0.098152"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# Make new dataframe with just means\n",
    "merge_mean_pathwayscores = pd.DataFrame(merge_pathwayscores[\"Node\"])\n",
    "merge_mean_pathwayscores[\"Healthy_mean\"] = merge_pathwayscores[[\"Pathway_Score_healthy_x\", \"Pathway_Score_healthy_y\"]].mean(axis=1)\n",
    "merge_mean_pathwayscores[\"Mild_mean\"] = merge_pathwayscores[[\"Pathway_Score_mild_x\", \"Pathway_Score_mild_y\"]].mean(axis=1)\n",
    "merge_mean_pathwayscores[\"Moderate_mean\"] = merge_pathwayscores[[\"Pathway_Score_moderate_x\", \"Pathway_Score_moderate_y\"]].mean(axis=1)\n",
    "merge_mean_pathwayscores[\"Severe_mean\"] = merge_pathwayscores[[\"Pathway_Score_severe_x\", \"Pathway_Score_severe_y\"]].mean(axis=1)\n",
    "merge_mean_pathwayscores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Parent</th>\n",
       "      <th>Node</th>\n",
       "      <th>Hierarchy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10591</th>\n",
       "      <td>R-HSA-109581</td>\n",
       "      <td>R-HSA-109606</td>\n",
       "      <td>R-HSA-5357801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10592</th>\n",
       "      <td>R-HSA-109581</td>\n",
       "      <td>R-HSA-169911</td>\n",
       "      <td>R-HSA-5357801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10593</th>\n",
       "      <td>R-HSA-109581</td>\n",
       "      <td>R-HSA-5357769</td>\n",
       "      <td>R-HSA-5357801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10594</th>\n",
       "      <td>R-HSA-109581</td>\n",
       "      <td>R-HSA-75153</td>\n",
       "      <td>R-HSA-5357801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10595</th>\n",
       "      <td>R-HSA-109582</td>\n",
       "      <td>R-HSA-140877</td>\n",
       "      <td>R-HSA-109582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>R-HSA-8963743</td>\n",
       "      <td>R-HSA-8963743</td>\n",
       "      <td>R-HSA-8963743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>R-HSA-9609507</td>\n",
       "      <td>R-HSA-9609507</td>\n",
       "      <td>R-HSA-9609507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>R-HSA-9612973</td>\n",
       "      <td>R-HSA-9612973</td>\n",
       "      <td>R-HSA-9612973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>R-HSA-9709957</td>\n",
       "      <td>R-HSA-9709957</td>\n",
       "      <td>R-HSA-9709957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>R-HSA-9748784</td>\n",
       "      <td>R-HSA-9748784</td>\n",
       "      <td>R-HSA-9748784</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2657 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Parent           Node      Hierarchy\n",
       "10591   R-HSA-109581   R-HSA-109606  R-HSA-5357801\n",
       "10592   R-HSA-109581   R-HSA-169911  R-HSA-5357801\n",
       "10593   R-HSA-109581  R-HSA-5357769  R-HSA-5357801\n",
       "10594   R-HSA-109581    R-HSA-75153  R-HSA-5357801\n",
       "10595   R-HSA-109582   R-HSA-140877   R-HSA-109582\n",
       "...              ...            ...            ...\n",
       "24     R-HSA-8963743  R-HSA-8963743  R-HSA-8963743\n",
       "25     R-HSA-9609507  R-HSA-9609507  R-HSA-9609507\n",
       "26     R-HSA-9612973  R-HSA-9612973  R-HSA-9612973\n",
       "27     R-HSA-9709957  R-HSA-9709957  R-HSA-9709957\n",
       "28     R-HSA-9748784  R-HSA-9748784  R-HSA-9748784\n",
       "\n",
       "[2657 rows x 3 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pathway hiercharcy\n",
    "hierarchy = pd.read_csv('/Users/sarapatti/Documents/Code MRes DS SP 2022-2023/Exported files/hierarchy_hsa_all.csv', index_col=0)\n",
    "hierarchy.columns = ['Parent', 'Node', 'Hierarchy']\n",
    "hierarchy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Parent</th>\n",
       "      <th>Node</th>\n",
       "      <th>Hierarchy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10591</th>\n",
       "      <td>R-HSA-109581</td>\n",
       "      <td>R-HSA-109606</td>\n",
       "      <td>Programmed Cell Death</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10592</th>\n",
       "      <td>R-HSA-109581</td>\n",
       "      <td>R-HSA-169911</td>\n",
       "      <td>Programmed Cell Death</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10593</th>\n",
       "      <td>R-HSA-109581</td>\n",
       "      <td>R-HSA-5357769</td>\n",
       "      <td>Programmed Cell Death</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10594</th>\n",
       "      <td>R-HSA-109581</td>\n",
       "      <td>R-HSA-75153</td>\n",
       "      <td>Programmed Cell Death</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10595</th>\n",
       "      <td>R-HSA-109582</td>\n",
       "      <td>R-HSA-140877</td>\n",
       "      <td>Hemostasis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>R-HSA-8963743</td>\n",
       "      <td>R-HSA-8963743</td>\n",
       "      <td>Digestion and absorption</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>R-HSA-9609507</td>\n",
       "      <td>R-HSA-9609507</td>\n",
       "      <td>Protein localization</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>R-HSA-9612973</td>\n",
       "      <td>R-HSA-9612973</td>\n",
       "      <td>Autophagy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>R-HSA-9709957</td>\n",
       "      <td>R-HSA-9709957</td>\n",
       "      <td>Sensory Perception</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>R-HSA-9748784</td>\n",
       "      <td>R-HSA-9748784</td>\n",
       "      <td>Drug ADME</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2657 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Parent           Node                 Hierarchy\n",
       "10591   R-HSA-109581   R-HSA-109606     Programmed Cell Death\n",
       "10592   R-HSA-109581   R-HSA-169911     Programmed Cell Death\n",
       "10593   R-HSA-109581  R-HSA-5357769     Programmed Cell Death\n",
       "10594   R-HSA-109581    R-HSA-75153     Programmed Cell Death\n",
       "10595   R-HSA-109582   R-HSA-140877                Hemostasis\n",
       "...              ...            ...                       ...\n",
       "24     R-HSA-8963743  R-HSA-8963743  Digestion and absorption\n",
       "25     R-HSA-9609507  R-HSA-9609507      Protein localization\n",
       "26     R-HSA-9612973  R-HSA-9612973                 Autophagy\n",
       "27     R-HSA-9709957  R-HSA-9709957        Sensory Perception\n",
       "28     R-HSA-9748784  R-HSA-9748784                 Drug ADME\n",
       "\n",
       "[2657 rows x 3 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Name pathways with names\n",
    "\n",
    "hierarchy['Hierarchy'] = hierarchy['Hierarchy'].map(reactome_dict)\n",
    "hierarchy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Pathway_1</th>\n",
       "      <th>Pathway_2</th>\n",
       "      <th>Correlation_protein</th>\n",
       "      <th>Correlation_abs_protein</th>\n",
       "      <th>p_val_protein</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "    <tr>\n",
       "      <th>93159</th>\n",
       "      <td>R-HSA-2033519</td>\n",
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       "      <th>93160</th>\n",
       "      <td>R-HSA-2033519</td>\n",
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       "      <td>1.000000</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>93161</th>\n",
       "      <td>R-HSA-2033519</td>\n",
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       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93162</th>\n",
       "      <td>R-HSA-2033519</td>\n",
       "      <td>R-HSA-5654699</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204697</th>\n",
       "      <td>R-HSA-5658442</td>\n",
       "      <td>R-HSA-198725</td>\n",
       "      <td>0.127387</td>\n",
       "      <td>0.127387</td>\n",
       "      <td>0.041298</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>204696</th>\n",
       "      <td>R-HSA-5658442</td>\n",
       "      <td>R-HSA-1971475</td>\n",
       "      <td>0.070057</td>\n",
       "      <td>0.070057</td>\n",
       "      <td>0.263138</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204695</th>\n",
       "      <td>R-HSA-5658442</td>\n",
       "      <td>R-HSA-196854</td>\n",
       "      <td>-0.090006</td>\n",
       "      <td>0.090006</td>\n",
       "      <td>0.150209</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204718</th>\n",
       "      <td>R-HSA-5658442</td>\n",
       "      <td>R-HSA-202733</td>\n",
       "      <td>0.088104</td>\n",
       "      <td>0.088104</td>\n",
       "      <td>0.159050</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>235635</th>\n",
       "      <td>R-HSA-69656</td>\n",
       "      <td>R-HSA-196741</td>\n",
       "      <td>0.180385</td>\n",
       "      <td>0.180385</td>\n",
       "      <td>0.003713</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>328329 rows × 8 columns</p>\n",
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       "            Pathway_1      Pathway_2  Correlation_protein  \\\n",
       "0        R-HSA-109581   R-HSA-109581             1.000000   \n",
       "93159   R-HSA-2033519  R-HSA-5654693             1.000000   \n",
       "93160   R-HSA-2033519  R-HSA-5654695             1.000000   \n",
       "93161   R-HSA-2033519  R-HSA-5654696             1.000000   \n",
       "93162   R-HSA-2033519  R-HSA-5654699             1.000000   \n",
       "...               ...            ...                  ...   \n",
       "204697  R-HSA-5658442   R-HSA-198725             0.127387   \n",
       "204696  R-HSA-5658442  R-HSA-1971475             0.070057   \n",
       "204695  R-HSA-5658442   R-HSA-196854            -0.090006   \n",
       "204718  R-HSA-5658442   R-HSA-202733             0.088104   \n",
       "235635    R-HSA-69656   R-HSA-196741             0.180385   \n",
       "\n",
       "        Correlation_abs_protein  p_val_protein  p_val_adjust_protein  \\\n",
       "0                      1.000000       0.000000                   0.0   \n",
       "93159                  1.000000       0.000000                   0.0   \n",
       "93160                  1.000000       0.000000                   0.0   \n",
       "93161                  1.000000       0.000000                   0.0   \n",
       "93162                  1.000000       0.000000                   0.0   \n",
       "...                         ...            ...                   ...   \n",
       "204697                 0.127387       0.041298                   1.0   \n",
       "204696                 0.070057       0.263138                   1.0   \n",
       "204695                 0.090006       0.150209                   1.0   \n",
       "204718                 0.088104       0.159050                   1.0   \n",
       "235635                 0.180385       0.003713                   1.0   \n",
       "\n",
       "        log10_p_val_adjusted_protein  Overlap_protein  \n",
       "0                                inf              1.0  \n",
       "93159                            inf              0.0  \n",
       "93160                            inf              0.0  \n",
       "93161                            inf              0.0  \n",
       "93162                            inf              0.0  \n",
       "...                              ...              ...  \n",
       "204697                          -0.0              0.0  \n",
       "204696                          -0.0              0.0  \n",
       "204695                          -0.0              0.0  \n",
       "204718                          -0.0              0.0  \n",
       "235635                          -0.0              0.0  \n",
       "\n",
       "[328329 rows x 8 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "protein_data.columns = [\"Pathway_1\", \"Pathway_2\", \"Correlation_protein\", \"Correlation_abs_protein\", \"p_val_protein\", \"p_val_adjust_protein\", \"log10_p_val_adjusted_protein\", \"Overlap_protein\"]\n",
    "protein_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "      <td>-0.025499</td>\n",
       "      <td>0.025499</td>\n",
       "      <td>6.806149e-01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>-0.000000</td>\n",
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       "      <td>-0.000000</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>22198</th>\n",
       "      <td>R-HSA-975634</td>\n",
       "      <td>R-HSA-9749641</td>\n",
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       "          Pathway_1      Pathway_2  Correlation_met  Correlation_abs_met  \\\n",
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       "22200  R-HSA-975634   R-HSA-975634         1.000000             1.000000   \n",
       "\n",
       "          p_val_met  p_val_adjust_met  log10_p_val_adjusted_met  Overlap_met  \n",
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       "1      5.486517e-09      1.218062e-04                  3.914331          0.0  \n",
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       "4      2.269312e-48      5.038100e-44                 43.297733          0.0  \n",
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       "22198  1.831827e-01      1.000000e+00                 -0.000000          0.5  \n",
       "22199  7.262464e-01      1.000000e+00                 -0.000000          0.0  \n",
       "22200  0.000000e+00      0.000000e+00                       inf          0.5  \n",
       "\n",
       "[22201 rows x 8 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metabolite_data.columns = [\"Pathway_1\", \"Pathway_2\", \"Correlation_met\", \"Correlation_abs_met\", \"p_val_met\", \"p_val_adjust_met\", \"log10_p_val_adjusted_met\", \"Overlap_met\"]\n",
    "metabolite_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
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       "      <td>R-HSA-156580</td>\n",
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       "      <td>0.269162</td>\n",
       "      <td>9.588652e-06</td>\n",
       "      <td>2.128777e-01</td>\n",
       "      <td>0.671870</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.626231</td>\n",
       "      <td>0.626231</td>\n",
       "      <td>2.145876e-29</td>\n",
       "      <td>7.045532e-24</td>\n",
       "      <td>23.152086</td>\n",
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       "      <td>3.665577e-28</td>\n",
       "      <td>8.137948e-24</td>\n",
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       "      <td>0.666543</td>\n",
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       "      <td>7.207718e-29</td>\n",
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       "      <td>2.329066e-96</td>\n",
       "      <td>95.632818</td>\n",
       "      <td>0.5</td>\n",
       "      <td>-0.644940</td>\n",
       "      <td>0.644940</td>\n",
       "      <td>1.285432e-31</td>\n",
       "      <td>4.220445e-26</td>\n",
       "      <td>25.374642</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2397</th>\n",
       "      <td>R-HSA-975634</td>\n",
       "      <td>R-HSA-9707564</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.736979</td>\n",
       "      <td>0.736979</td>\n",
       "      <td>2.830697e-45</td>\n",
       "      <td>9.294000e-40</td>\n",
       "      <td>39.031797</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2398</th>\n",
       "      <td>R-HSA-975634</td>\n",
       "      <td>R-HSA-9707616</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>inf</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.736979</td>\n",
       "      <td>0.736979</td>\n",
       "      <td>2.830697e-45</td>\n",
       "      <td>9.294000e-40</td>\n",
       "      <td>39.031797</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2399</th>\n",
       "      <td>R-HSA-975634</td>\n",
       "      <td>R-HSA-9711123</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.5</td>\n",
       "      <td>-0.743888</td>\n",
       "      <td>0.743888</td>\n",
       "      <td>1.562217e-46</td>\n",
       "      <td>5.129210e-41</td>\n",
       "      <td>40.289950</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2400</th>\n",
       "      <td>R-HSA-975634</td>\n",
       "      <td>R-HSA-975634</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2401 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Pathway_1      Pathway_2  Correlation_met  Correlation_abs_met  \\\n",
       "0     R-HSA-112315   R-HSA-112315         1.000000             1.000000   \n",
       "1     R-HSA-112315  R-HSA-1428517        -0.193927             0.193927   \n",
       "2     R-HSA-112315  R-HSA-1483257         0.418575             0.418575   \n",
       "3     R-HSA-112315   R-HSA-156580         0.269162             0.269162   \n",
       "4     R-HSA-112315    R-HSA-15869        -0.609690             0.609690   \n",
       "...            ...            ...              ...                  ...   \n",
       "2396  R-HSA-975634  R-HSA-8978868        -0.908177             0.908177   \n",
       "2397  R-HSA-975634  R-HSA-9707564         1.000000             1.000000   \n",
       "2398  R-HSA-975634  R-HSA-9707616         1.000000             1.000000   \n",
       "2399  R-HSA-975634  R-HSA-9711123         1.000000             1.000000   \n",
       "2400  R-HSA-975634   R-HSA-975634         1.000000             1.000000   \n",
       "\n",
       "          p_val_met  p_val_adjust_met  log10_p_val_adjusted_met  Overlap_met  \\\n",
       "0      0.000000e+00      0.000000e+00                       inf          0.0   \n",
       "1      1.577485e-03      1.000000e+00                 -0.000000          0.0   \n",
       "2      1.403258e-12      3.115372e-08                  7.506490          0.0   \n",
       "3      9.588652e-06      2.128777e-01                  0.671870          0.0   \n",
       "4      3.665577e-28      8.137948e-24                 23.089485          0.0   \n",
       "...             ...               ...                       ...          ...   \n",
       "2396  1.049081e-100      2.329066e-96                 95.632818          0.5   \n",
       "2397   0.000000e+00      0.000000e+00                       inf          0.5   \n",
       "2398   0.000000e+00      0.000000e+00                       inf          1.0   \n",
       "2399   0.000000e+00      0.000000e+00                       inf          0.5   \n",
       "2400   0.000000e+00      0.000000e+00                       inf          0.5   \n",
       "\n",
       "      Correlation_protein  Correlation_abs_protein  p_val_protein  \\\n",
       "0                1.000000                 1.000000   0.000000e+00   \n",
       "1                0.716394                 0.716394   9.469183e-42   \n",
       "2                0.825260                 0.825260   3.197925e-65   \n",
       "3                0.626231                 0.626231   2.145876e-29   \n",
       "4                0.666543                 0.666543   2.195273e-34   \n",
       "...                   ...                      ...            ...   \n",
       "2396            -0.644940                 0.644940   1.285432e-31   \n",
       "2397             0.736979                 0.736979   2.830697e-45   \n",
       "2398             0.736979                 0.736979   2.830697e-45   \n",
       "2399            -0.743888                 0.743888   1.562217e-46   \n",
       "2400             1.000000                 1.000000   0.000000e+00   \n",
       "\n",
       "      p_val_adjust_protein  log10_p_val_adjusted_protein  Overlap_protein  \n",
       "0             0.000000e+00                           inf              0.0  \n",
       "1             3.109007e-36                     35.507378              0.0  \n",
       "2             1.049972e-59                     58.978822              0.0  \n",
       "3             7.045532e-24                     23.152086              0.0  \n",
       "4             7.207718e-29                     28.142202              0.0  \n",
       "...                    ...                           ...              ...  \n",
       "2396          4.220445e-26                     25.374642              0.0  \n",
       "2397          9.294000e-40                     39.031797              0.0  \n",
       "2398          9.294000e-40                     39.031797              0.0  \n",
       "2399          5.129210e-41                     40.289950              0.0  \n",
       "2400          0.000000e+00                           inf              0.0  \n",
       "\n",
       "[2401 rows x 14 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Merge dataframes\n",
    "\n",
    "merge_p_m =pd.merge(metabolite_data, protein_data, on =[\"Pathway_1\", \"Pathway_2\"])\n",
    "merge_p_m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2401, 16)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Drop uneeded columns\n",
    "merge_p_m.drop(columns = [\"log10_p_val_adjusted_met\", \"log10_p_val_adjusted_protein\"])\n",
    "\n",
    "# Calculate average correlation of protein and metabolite\n",
    "merge_p_m[\"average_correlation_abs\"] = (merge_p_m[\"Correlation_abs_met\"] + merge_p_m[\"Correlation_abs_protein\"])/2\n",
    "\n",
    "merge_p_m[\"average_overlap\"] = (merge_p_m[\"Overlap_met\"] + merge_p_m[\"Overlap_protein\"])/2\n",
    "\n",
    "print(merge_p_m.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total pathways: (2401, 16)\n",
      "Drop same pathways: (2352, 16)\n",
      "Drop pathways with p adjusted greater than 0.05 metabolites (926, 16)\n",
      "Drop pathways with p adjusted greater than 0.05 proteins (860, 16)\n",
      "Drop pathways with overlap greater than 0.5 (849, 16)\n"
     ]
    }
   ],
   "source": [
    "# Total \n",
    "print(\"Total pathways:\",merge_p_m.shape)\n",
    "\n",
    "# Drop same pathways\n",
    "merge_p_m= merge_p_m.loc[(merge_p_m[\"Pathway_1\"] != merge_p_m[\"Pathway_2\"])]\n",
    "print(\"Drop same pathways:\", merge_p_m.shape)\n",
    "\n",
    "# Drop pathways with p adjusted greater than 0.05\n",
    "merge_p_m = merge_p_m.loc[(merge_p_m[\"p_val_adjust_met\"]<= 0.05)]\n",
    "print(\"Drop pathways with p adjusted greater than 0.05 metabolites\", merge_p_m.shape)\n",
    "\n",
    "merge_p_m = merge_p_m.loc[(merge_p_m[\"p_val_adjust_protein\"]<= 0.05)]\n",
    "print(\"Drop pathways with p adjusted greater than 0.05 proteins\", merge_p_m.shape)\n",
    "\n",
    "# Drop pathways with overlap > 0.5\n",
    "merge_p_m = merge_p_m.loc[(merge_p_m[\"average_overlap\"]<= 0.5)]\n",
    "print(\"Drop pathways with overlap greater than 0.5\", merge_p_m.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Merge network - protein and metabolite')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make edges\n",
    "G_merge = nx.from_pandas_edgelist(df=merge_p_m, source='Pathway_1', target='Pathway_2', edge_attr=['average_correlation_abs', 'average_overlap'])\n",
    "weight_corr = [i['average_correlation_abs'] for i in dict(G_merge.edges).values()]\n",
    "overlap_coef = [i['average_overlap'] for i in dict(G_merge.edges).values()] \n",
    "\n",
    "#Draw graph\n",
    "nx.draw(G_merge, with_labels=False, node_color = \"black\", node_size= 8, edge_color=weight_corr, width = overlap_coef, font_size=5, edge_cmap=plt.cm.Reds)\n",
    "plt.title('Merge network - protein and metabolite')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of nodes: 48\n",
      "Number of edges: 429\n",
      "Graph density: 0.3803191489361702\n"
     ]
    }
   ],
   "source": [
    "# Proprties of graph\n",
    "\n",
    "number_of_nodes = G_merge.number_of_nodes()\n",
    "print(\"Number of nodes:\", number_of_nodes)\n",
    "\n",
    "number_of_edges= G_merge.number_of_edges()\n",
    "print(\"Number of edges:\", number_of_edges)\n",
    "\n",
    "density =nx.density(G_merge)\n",
    "print(\"Graph density:\", density)\n",
    "\n",
    "## Add more attributes\n",
    "degree_centrality = nx.degree_centrality(G_merge) # Compute the degree centrality for nodes\n",
    "degree_centrality_df = pd.DataFrame(list(degree_centrality.items()), columns= [\"Node\", \"Degree_centrality\"])\n",
    "degree_centrality_df\n",
    "\n",
    "\n",
    "clustering = nx.clustering(G_merge) # Compute the clustering coefficent for nodes\n",
    "clustering_df = pd.DataFrame(list(clustering.items()), columns= [\"Node\", \"Clustering\"])\n",
    "clustering_df\n",
    "\n",
    "btwn_centrality = nx.betweenness_centrality(G_merge)\n",
    "btwn_centrality_df = pd.DataFrame(list(btwn_centrality.items()), columns= [\"Node\", \"Betweenness_centrality\"])\n",
    "btwn_centrality_df\n",
    "\n",
    "degree = list(G_merge.degree)\n",
    "degree_df = pd.DataFrame(degree, columns = [\"Nodes\", \"Degree\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # LOCKING BECAUSE IT DOES RANDOM SEEDING\n",
    "\n",
    "# # Community \n",
    "\n",
    "# import networkx.algorithms.community as nx_comm\n",
    "# louvain_communities = nx_comm.louvain_communities(G_merge)\n",
    "# louvain_communities = pd.DataFrame(louvain_communities)\n",
    "# louvain_communities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Export to excel\n",
    "# louvain_communities.to_excel('/Users/sarapatti/Documents/Code MRes DS SP 2022-2023/Exported files/louvain_communities_merge.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import from excel\n",
    "louvain_communities = pd.read_excel('/Users/sarapatti/Documents/Code MRes DS SP 2022-2023/Exported files/organized_community_merge.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "49\n",
      "48\n"
     ]
    }
   ],
   "source": [
    "# Merge attributes\n",
    "\n",
    "attributes_df = pd.merge(degree_centrality_df, clustering_df[\"Clustering\"], left_index = True, right_index = True)\n",
    "attributes_df = pd.merge(attributes_df, btwn_centrality_df[\"Betweenness_centrality\"], left_index = True, right_index = True)\n",
    "attributes_df = pd.merge(attributes_df, degree_df[\"Degree\"], left_index = True, right_index = True)\n",
    "attributes_df = pd.merge(attributes_df, merge_mean_pathwayscores, on=[\"Node\"])\n",
    "attributes_df = pd.merge(attributes_df, hierarchy, on=[\"Node\"])\n",
    "attributes_df = pd.merge(attributes_df, louvain_communities, on=[\"Node\"])\n",
    "attributes_df = pd.merge(attributes_df, names_pathways, on=['Node'])\n",
    "print(len(attributes_df))\n",
    "\n",
    "# Drop duplicates\n",
    "attributes_df_nodups = attributes_df.drop_duplicates(subset=['Node'])\n",
    "print(len(attributes_df_nodups))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Degree_centrality': 0.6595744680851063,\n",
       " 'Clustering': 0.46021505376344085,\n",
       " 'Betweenness_centrality': 0.26171296622767853,\n",
       " 'Degree': 31,\n",
       " 'Healthy_mean': -0.24165755983990644,\n",
       " 'Mild_mean': 0.279002030882816,\n",
       " 'Moderate_mean': 0.18748102289101076,\n",
       " 'Severe_mean': 0.2273424468664945,\n",
       " 'Parent': 'R-HSA-112316',\n",
       " 'Hierarchy': 'Neuronal System',\n",
       " 'Community': 2,\n",
       " 'Pathway_name': 'Transmission across Chemical Synapses'}"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "attributes_dict = attributes_df_nodups.set_index('Node').to_dict('index')\n",
    "attributes_dict[\"R-HSA-112315\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make edges\n",
    "G_merge = nx.from_pandas_edgelist(df=merge_p_m, source='Pathway_1', target='Pathway_2', edge_attr=['average_correlation_abs', 'average_overlap'])\n",
    "weight_corr = [i['average_correlation_abs'] for i in dict(G_merge.edges).values()]\n",
    "overlap_coef = [i['average_overlap'] for i in dict(G_merge.edges).values()] \n",
    "\n",
    "#Set Node Attributes\n",
    "nx.set_node_attributes(G_merge, attributes_dict)\n",
    "\n",
    "#Draw graph\n",
    "nx.draw(G_merge, with_labels=False,edge_color=weight_corr, width = overlap_coef, font_size=5, edge_cmap=plt.cm.Reds, cmap=plt.cm.Greens, alpha=0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Save file\n",
    "#nx.write_gml(G_merge, '/Users/sarapatti/Documents/Code MRes DS SP 2022-2023/Cytoscape/Merge_full_network_withattributes_1.17.23_V4.gml')\n",
    "nx.write_gml(G_merge, '/Users/sarapatti/Documents/Code MRes DS SP 2022-2023/Cytoscape_files/Merge_full_network_withattributes_V6.gml')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Increase p value strenouness from 0.05 to 0.005"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total pathways: (849, 16)\n",
      "Drop same pathways: (849, 16)\n",
      "Drop pathways with p adjusted greater than 0.005 metabolites (800, 16)\n",
      "Drop pathways with p adjusted greater than 0.005 proteins (794, 16)\n",
      "Drop pathways with overlap greater than 0.5 (794, 16)\n",
      "(794, 16)\n"
     ]
    }
   ],
   "source": [
    "# Total \n",
    "print(\"Total pathways:\",merge_p_m.shape)\n",
    "\n",
    "# Drop same pathways\n",
    "merge_p_m= merge_p_m.loc[(merge_p_m[\"Pathway_1\"] != merge_p_m[\"Pathway_2\"])]\n",
    "print(\"Drop same pathways:\", merge_p_m.shape)\n",
    "\n",
    "# Drop pathways with p adjusted greater than 0.05\n",
    "merge_p_m = merge_p_m.loc[(merge_p_m[\"p_val_adjust_met\"]<= 0.005)]\n",
    "print(\"Drop pathways with p adjusted greater than 0.005 metabolites\", merge_p_m.shape)\n",
    "\n",
    "merge_p_m = merge_p_m.loc[(merge_p_m[\"p_val_adjust_protein\"]<= 0.005)]\n",
    "print(\"Drop pathways with p adjusted greater than 0.005 proteins\", merge_p_m.shape)\n",
    "\n",
    "# Drop pathways with overlap > 0.5\n",
    "merge_p_m = merge_p_m.loc[(merge_p_m[\"average_overlap\"]<= 0.5)]\n",
    "print(\"Drop pathways with overlap greater than 0.5\", merge_p_m.shape)\n",
    "\n",
    "merge_p_m_new = merge_p_m\n",
    "print(merge_p_m_new.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Merge network - protein and metabolite')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make edges\n",
    "G_merge_new = nx.from_pandas_edgelist(df=merge_p_m_new, source='Pathway_1', target='Pathway_2', edge_attr=['average_correlation_abs', 'average_overlap'])\n",
    "weight_corr = [i['average_correlation_abs'] for i in dict(G_merge_new.edges).values()]\n",
    "overlap_coef = [i['average_overlap'] for i in dict(G_merge_new.edges).values()] \n",
    "\n",
    "#Draw graph\n",
    "nx.draw(G_merge_new, with_labels=False, node_color = \"black\", node_size= 8, edge_color=weight_corr, width = overlap_coef, font_size=5, edge_cmap=plt.cm.Reds)\n",
    "plt.title('Merge network - protein and metabolite')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of nodes: 48\n",
      "Number of edges: 401\n",
      "Graph density: 0.3554964539007092\n"
     ]
    }
   ],
   "source": [
    "# Proprties of graph\n",
    "\n",
    "number_of_nodes_new = G_merge_new.number_of_nodes()\n",
    "print(\"Number of nodes:\", number_of_nodes_new)\n",
    "\n",
    "\n",
    "number_of_edges_new = G_merge_new.number_of_edges()\n",
    "print(\"Number of edges:\", number_of_edges_new)\n",
    "\n",
    "density_new =nx.density(G_merge_new)\n",
    "print(\"Graph density:\", density_new)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "## Add more attributes\n",
    "degree_centrality = nx.degree_centrality(G_merge_new) # Compute the degree centrality for nodes\n",
    "degree_centrality_df = pd.DataFrame(list(degree_centrality.items()), columns= [\"Node\", \"Degree_centrality\"])\n",
    "degree_centrality_df\n",
    "\n",
    "\n",
    "clustering = nx.clustering(G_merge_new) # Compute the clustering coefficent for nodes\n",
    "clustering_df = pd.DataFrame(list(clustering.items()), columns= [\"Node\", \"Clustering\"])\n",
    "clustering_df\n",
    "\n",
    "btwn_centrality = nx.betweenness_centrality(G_merge_new)\n",
    "btwn_centrality_df = pd.DataFrame(list(btwn_centrality.items()), columns= [\"Node\", \"Betweenness_centrality\"])\n",
    "btwn_centrality_df\n",
    "\n",
    "degree = list(G_merge.degree)\n",
    "degree_df = pd.DataFrame(degree, columns = [\"Nodes\", \"Degree\"])\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pathway length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "metabolite_molecules = pd.read_csv('/Users/sarapatti/Documents/Code MRes DS SP 2022-2023/number_of_metabolites.csv')\n",
    "protein_molecules = pd.read_csv('/Users/sarapatti/Documents/Code MRes DS SP 2022-2023/number_of_proteins.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rename\n",
    "metabolite_molecules.columns = [\"Node\",\"Number_of_molecules\", \"Number_of_molecules_(log10)\"]\n",
    "metabolite_molecules[\"Omics\"]= \"Metabolomic\"\n",
    "\n",
    "protein_molecules.columns = [\"Node\",\"Number_of_molecules\", \"Number_of_molecules_(log10)\"]\n",
    "protein_molecules[\"Omics\"]= \"Proteomic\"\n",
    "\n",
    "attributes_df_length = pd.merge(protein_molecules, attributes_df, on = 'Node')\n",
    "integrate_molecules = attributes_df_length.iloc[:,:3]\n",
    "integrate_molecules[\"Omics\"]= \"Integrated\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Concat files\n",
    "number_molecules = pd.concat([metabolite_molecules, protein_molecules, integrate_molecules], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Node</th>\n",
       "      <th>Number_of_molecules</th>\n",
       "      <th>Number_of_molecules_(log10)</th>\n",
       "      <th>Omics</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>R-HSA-110331</td>\n",
       "      <td>2</td>\n",
       "      <td>0.301030</td>\n",
       "      <td>Metabolomic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>R-HSA-112310</td>\n",
       "      <td>8</td>\n",
       "      <td>0.903090</td>\n",
       "      <td>Metabolomic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>R-HSA-112311</td>\n",
       "      <td>3</td>\n",
       "      <td>0.477121</td>\n",
       "      <td>Metabolomic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>R-HSA-112315</td>\n",
       "      <td>10</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Metabolomic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>R-HSA-112316</td>\n",
       "      <td>10</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Metabolomic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>807</th>\n",
       "      <td>R-HSA-9707564</td>\n",
       "      <td>2</td>\n",
       "      <td>0.301030</td>\n",
       "      <td>Integrated</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>808</th>\n",
       "      <td>R-HSA-9707616</td>\n",
       "      <td>2</td>\n",
       "      <td>0.301030</td>\n",
       "      <td>Integrated</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>809</th>\n",
       "      <td>R-HSA-9711123</td>\n",
       "      <td>8</td>\n",
       "      <td>0.903090</td>\n",
       "      <td>Integrated</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>810</th>\n",
       "      <td>R-HSA-975634</td>\n",
       "      <td>3</td>\n",
       "      <td>0.477121</td>\n",
       "      <td>Integrated</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>811</th>\n",
       "      <td>R-HSA-975634</td>\n",
       "      <td>3</td>\n",
       "      <td>0.477121</td>\n",
       "      <td>Integrated</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>812 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Node  Number_of_molecules  Number_of_molecules_(log10)  \\\n",
       "0     R-HSA-110331                    2                     0.301030   \n",
       "1     R-HSA-112310                    8                     0.903090   \n",
       "2     R-HSA-112311                    3                     0.477121   \n",
       "3     R-HSA-112315                   10                     1.000000   \n",
       "4     R-HSA-112316                   10                     1.000000   \n",
       "..             ...                  ...                          ...   \n",
       "807  R-HSA-9707564                    2                     0.301030   \n",
       "808  R-HSA-9707616                    2                     0.301030   \n",
       "809  R-HSA-9711123                    8                     0.903090   \n",
       "810   R-HSA-975634                    3                     0.477121   \n",
       "811   R-HSA-975634                    3                     0.477121   \n",
       "\n",
       "           Omics  \n",
       "0    Metabolomic  \n",
       "1    Metabolomic  \n",
       "2    Metabolomic  \n",
       "3    Metabolomic  \n",
       "4    Metabolomic  \n",
       "..           ...  \n",
       "807   Integrated  \n",
       "808   Integrated  \n",
       "809   Integrated  \n",
       "810   Integrated  \n",
       "811   Integrated  \n",
       "\n",
       "[812 rows x 4 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "number_molecules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of molecules (Log10)')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot dimensions\n",
    "plt.figure(figsize=(10,5)) # has to be above the graph\n",
    "\n",
    "#Making the violin plot\n",
    "ax = sns.violinplot(data = number_molecules, y= 'Number_of_molecules_(log10)',  x = 'Omics', palette= \"pastel\")\n",
    "ax.set(xlabel=None)\n",
    "# Labels\n",
    "plt.ylabel(\"Number of molecules (Log10)\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python_3104",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "c9ca5e3136d0330de2a9294dd81319dfd613941b64e9f48a76d4b964decbcf1c"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}