Update README, correct file paths in the notebook

IlyaLab · Nov 27, 2024 · b804643 · b804643
1 parent 637decf
commit b804643
Show file tree

Hide file tree

Showing 15 changed files with 94 additions and 156 deletions.
diff --git a/Composing models/Merge logical models.ipynb b/Composing models/Merge logical models.ipynb
@@ -34,8 +34,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "home = \"persistent/\" # home path for Logic Model Merger\n",
     "model1name = \"Palma2021\"\n",
-    "model2name = \"Ikonomi2020\""
+    "model2name = \"Ikonomi2020\"\n",
+    "sbml_file1 = home + \"LogicModelMerger/Models/\" + model1name + \".sbml\"\n",
+    "sbml_file2 = home + \"LogicModelMerger/Models/\" + model2name + \".sbml\""
    ]
   },
   {
@@ -435,8 +438,8 @@
    ],
    "source": [
     "# Load the networks\n",
-    "network1 = read_network(\"../Models/\" + model1name + \".sbml\")\n",
-    "network2 = read_network(\"../Models/\" + model2name + \".sbml\")\n",
+    "network1 = read_network(sbml_file1)\n",
+    "network2 = read_network(sbml_file2)\n",
     "print(\"Network #1:\")\n",
     "print(network1)\n",
     "print(\"Network #2:\")\n",
@@ -694,12 +697,12 @@
     "merged_inhibitor_wins_name = \"merged_inhibitor_wins_\" + model1name + \"_\" + model2name\n",
     "merged_or_name = \"merged_or_\" + model1name + \"_\" + model2name\n",
     "\n",
-    "write_network_to_file(merged_network_and, \"../Updated/Models/\" + merged_and_name, format=\"text\")\n",
-    "write_network_to_file(merged_network_inhibitor_wins, \"../Updated/Models/\" + merged_inhibitor_wins_name, format=\"text\")\n",
-    "write_network_to_file(merged_network_or, \"../Updated/Models/\" + merged_or_name, format=\"text\")\n",
-    "write_network_to_file(merged_network_and, \"../Updated/Models/\" + merged_and_name, format=\"sbml\")\n",
-    "write_network_to_file(merged_network_inhibitor_wins, \"../Updated/Models/\" + merged_inhibitor_wins_name, format=\"sbml\")\n",
-    "write_network_to_file(merged_network_or, \"../Updated/Models/\" + merged_or_name, format=\"sbml\")\n"
+    "write_network_to_file(merged_network_and, home + \"LogicModelMerger/Models/\" + merged_and_name, format=\"text\")\n",
+    "write_network_to_file(merged_network_inhibitor_wins, home + \"LogicModelMerger/Models/\" + merged_inhibitor_wins_name, format=\"text\")\n",
+    "write_network_to_file(merged_network_or, home + \"LogicModelMerger/Models/\" + merged_or_name, format=\"text\")\n",
+    "write_network_to_file(merged_network_and, home + \"LogicModelMerger/Models/\" + merged_and_name, format=\"sbml\")\n",
+    "write_network_to_file(merged_network_inhibitor_wins, home + \"LogicModelMerger/Models/\" + merged_inhibitor_wins_name, format=\"sbml\")\n",
+    "write_network_to_file(merged_network_or, home + \"LogicModelMerger/Models/\" + merged_or_name, format=\"sbml\")\n"
    ]
   }
  ],

diff --git a/Data/significant_paths_to_phenotypes.txt → ...xPath/significant_paths_to_phenotypes.txt b/Data/significant_paths_to_phenotypes.txt → ...xPath/significant_paths_to_phenotypes.txt
diff --git a/Evaluating the merged model/Helper functions.ipynb b/Evaluating the merged model/Helper functions.ipynb
diff --git a/README.md b/README.md
@@ -2,44 +2,45 @@
 
 ## Overview
 
-This repository contains the code and resources for merging gene regulatory network (GRN) models as described in the manuscript **"Merging Logical Models: An Application in Acute Myeloid Leukemia Modeling"**. The repository provides a complete workflow for merging logic models, enhancing our understanding of complex biological systems, and demonstrate its application in Acute Myeloid Leukemia (AML).
+This repository contains the code and resources for merging gene regulatory network (GRN) models as described in the manuscript **"Workflow for merging logical models: An application to gene regulation"**. The repository provides a semi-automated workflow for merging logic models, and demonstrate its application in Acute Myeloid Leukemia (AML) models.
 
-The workflow presented in this repository involves sequential steps:
+The workflow involves sequential steps:
 
-1. **Identifying Candidate Models**: This step involves reviewing existing literature, repositories, and databases to identify models with shared components, such as shared genes in the GRNs.
-2. **Standardizing and Annotating Models**: Standardizing the gene names using international standards like HGNC approved symbols and ensuring models are reproducible.
-3. **Reproducing Selected Models**: Verifying that selected models replicate the behaviors described in their original publications.
-4. **Merging Models**: Using the provided code to merge models with different logical combination methods (`OR`, `AND`, `Inhibitor Wins`).
+1. **Finding Models**: This step involves reviewing existing literature, repositories, and databases to identify models with shared components.
+2. **Standardizing and Annotating Models**: Convert models to SBML-qual format, and annotate gene names using HGNC approved symbols.
+3. **Reproducing Selected Models**: Verifying that selected models replicate the behaviors described in their original resources.
+4. **Merging Models**: Using the provided tool to automatically merge models with different logical combination methods (`OR`, `AND`, `Inhibitor Wins`).
 5. **Evaluating the Merged Models**: Comparing the predictive accuracy and robustness of the merged models against the original models and applying the merged models to new, untested scenarios. 
 
 ## Repository Structure
 
 This repository is organized according to the workflow described in the manuscript:
 
 1. **`Standardizing & Annotating Models`**:
-    - [Standardization](Standardizing%20and%20annotating%20models/Standardization.ipynb): Converting models in text file to SBML-qual format.
-    - [Annotation](Standardizing%20and%20annotating%20models/Annotation.ipynb): Fetching HGNC gene symbols for input SBML-qual models and updating the gene names after manual verification.
+    - [Standardization](Standardizing%20and%20annotating%20models/Convert%20model%20in%20text%20file%20to%20SBML-qual.ipynb): Converting models in text file to SBML-qual format.
+    - [Annotation](Standardizing%20and%20annotating%20models/Standardize%20gene%20names%20to%20HGNC%20symbol.ipynb): Fetching HGNC gene symbols for input SBML-qual models and updating the gene names after manual verification.
 2. **`Reproducing Selected Models`**: 
     - Reproducibility check for each collected model, including:
-        - [`Bonzanni2013`](Reproducing%20selected%20models/Bonzanni2013)
-        - [`Ikonomi2020`](Reproducing%20selected%20models/Ikonomi2020)
-        - [`Krumsiek2011`](Reproducing%20selected%20models/Krumsiek2011)
-        - [`Palma2021`](Reproducing%20selected%20models/Palma2021) *(As Fig S3, S4 in the manuscript)*
+        - [Bonzanni2013](Reproducing%20selected%20models/Bonzanni2013/Bonzanni2013.ipynb)
+        - [Ikonomi2020](Reproducing%20selected%20models/Ikonomi2020/Ikonomi2020.ipynb)
+        - [Krumsiek2011](Reproducing%20selected%20models/Krumsiek2011/Krumsiek2011.ipynb)
+        - [Palma2021](Reproducing%20selected%20models/Palma2021/Palma2021.ipynb) *(As Table S3 in the manuscript)*
 3. **`Composing Models`**: 
-    - [Merge logical models](Composing%20models/Merge%20logical%20models.ipynb): Merging logical models, including the OR, AND, and Inhibitor Wins methods.
-    - Support model input in:
-        - Text files using a  EBNF description
+    - [Merge logical models](Composing%20models/Merge%20logical%20models.ipynb): Automatically merging logical models, including the OR, AND, and Inhibitor Wins methods.
+    - Support models in:
+        - Text files using a EBNF description as in `Boolnet`
         - SBML-qual files
 4. **`Evaluating the Merged Model`**: 
+    - [Functions](Evaluating%20the%20merged%20model/Helper%20functions.ipynb): Provides some helper functions for evaluating logical models.
     - Contains notebooks for various evaluation tasks:
-        - [**Coverage**](Evaluating%20the%20merged%20model/Coverage.ipynb): Assessing the coverage of AML patients with each mutation profiles using BeatAML, TCGA, AMLSG and cBioPortal data.  *(Fig S7)*
-        - [**Stable States Heatmap**](Evaluating%20the%20merged%20model/Stable%20states%20heatmap.ipynb): Visualizing stable states of the merged models and clustering them with individual models. *(Fig 3)*
-        - [**Correlation with HSC Expression**](Evaluating%20the%20merged%20model/Correlation%20with%20HSC%20expression.ipynb): Analyzing the correlation of model predictions with hematopoietic stem cell expression data. *(Fig 4, Fig S2)*
+        - [**Coverage**](Evaluating%20the%20merged%20model/Coverage.ipynb): Assessing the coverage of AML patients with each mutation profiles using BeatAML, TCGA, AMLSG and cBioPortal data.
+        - [**Stable States Heatmap**](Evaluating%20the%20merged%20model/Stable%20states%20heatmap.ipynb): Visualizing stable states of the merged models and clustering them with individual models. *(Fig 2B, 3B, S1)*
+        - [**Correlation with HSC Expression**](Evaluating%20the%20merged%20model/Correlation%20with%20HSC%20expression.ipynb): Analyzing the correlation of model predictions with hematopoietic stem cell expression data. *(Fig 2C-E, Fig S2)*
         - **Correlation with Clinical Outcomes**: Separate notebooks for evaluating correlations with different clinical indicators/datasets:
             - Blast percentages from the BeatAML data
-                - [Using approach similar to Palma et al.](Evaluating%20the%20merged%20model/Correlation%20with%20clinical%20outcome_BeatAML_Palma%20approach.ipynb) *(Fig 5a,b, Fig S5)*
-                - [Using all mutations](Evaluating%20the%20merged%20model/Correlation%20with%20clinical%20outcome_BeatAML_all%20mutation.ipynb) *(Fig 5c,d, Fig S6)*
-            - [Blast percentages from the TCGA data](Evaluating%20the%20merged%20model/Correlation%20with%20clinical%20outcome_TCGA.ipynb) *(Fig S4)*
+                - [Using approach similar to Palma et al.](Evaluating%20the%20merged%20model/Correlation%20with%20clinical%20outcome_BeatAML_Palma%20approach.ipynb) *(Fig 3C-D, Fig S4)*
+                - [Using all mutations](Evaluating%20the%20merged%20model/Correlation%20with%20clinical%20outcome_BeatAML_all%20mutation.ipynb) *(Fig 3E-F, Fig S4)*
+            - [Blast percentages from the TCGA data](Evaluating%20the%20merged%20model/Correlation%20with%20clinical%20outcome_TCGA.ipynb) *(TableS3)*
             - [Hazard ratio for death from the AMLSG data](Evaluating%20the%20merged%20model/Correlation%20with%20clinical%20outcome_AMLSG.ipynb)
 
 - [**`Data`**](Data): Contains datasets used for model evaluation.
@@ -54,14 +55,16 @@ This repository is organized according to the workflow described in the manuscri
    git clone https://github.com/IlyaLab/LogicModelMerger.git
 
 2. **Install and open the CoLoMoTo Notebook** (Optional):
-   Please refer to the usage guide on their website.
+   Please refer to the usage guide on their [website](https://colomoto.github.io/colomoto-docker/).
 
 3. **Run notebooks:**
    Navigate to the relevant directory and open the Jupyter notebooks using the CoLoMoTo notebook or your preferred Jupyter environment.
 
 
 ### CoLoMoTo Interactive Notebook
 
-All Jupyter notebooks in this repository were conducted using the CoLoMoTo Interactive Notebook with the Docker image `colomoto/colomoto-docker:2024-03-01`. The CoLoMoTo notebook provides a unified environment to edit, execute, share, and reproduce analyses of qualitative models of biological networks.
+Jupyter notebooks in this repository were conducted using the CoLoMoTo Interactive Notebook with Docker image `colomoto/colomoto-docker:2024-03-01`.
+
+ The CoLoMoTo notebook provides a unified environment to edit, execute, share, and reproduce analyses of qualitative models of biological networks.
 
-For more information about CoLoMoTo Interactive Notebook, visit [CoLoMoTo](http://www.colomoto.org/notebook/).
+For more information, visit [CoLoMoTo](http://www.colomoto.org/notebook/).
diff --git a/Reproducing selected models/Bonzanni2013/Bonzanni2013.ipynb b/Reproducing selected models/Bonzanni2013/Bonzanni2013.ipynb
@@ -56,9 +56,10 @@
     "import matplotlib.pyplot as plt\n",
     "from matplotlib.colors import ListedColormap\n",
     "\n",
+    "home = \"persistent/\" # home path for Logic Model Merger\n",
     "model_name = \"Bonzanni2013\"\n",
-    "txt_file = \"persistent/Models/\" + model_name + \".txt\"\n",
-    "sbml_file = \"persistent/Models/\" + model_name + \".sbml\""
+    "txt_file = home + \"LogicModelMerger/Models/\" + model_name + \".txt\"\n",
+    "sbml_file = home + \"LogicModelMerger/Models/\" + model_name + \".sbml\""
    ]
   },
   {
@@ -425,7 +426,7 @@
     "\n",
     "df_simple = df.T\n",
     "df_simple.index = df_simple.index.str.replace('Attr', 'S')\n",
-    "df_simple.to_csv('persistent/SimulationResults/attr_' + model_name + '_simple.csv')\n",
+    "df_simple.to_csv(home + 'LogicModelMerger/SimulationResults/attr_' + model_name + '_simple.csv')\n",
     "df_simple"
    ]
   },
@@ -1009,7 +1010,7 @@
     "\n",
     "df = df.T\n",
     "df.index = df.index.str.replace('Attr', 'S')\n",
-    "df.to_csv('persistent/SimulationResults/attr_' + model_name + '.csv')\n",
+    "df.to_csv(home + 'LogicModelMerger/SimulationResults/attr_' + model_name + '.csv')\n",
     "df"
    ]
   },

diff --git a/Reproducing selected models/Ikonomi2020/Ikonomi2020.ipynb b/Reproducing selected models/Ikonomi2020/Ikonomi2020.ipynb
@@ -575,7 +575,12 @@
     "from matplotlib.colors import ListedColormap\n",
     "from matplotlib.gridspec import GridSpec\n",
     "import matplotlib.patches as patches\n",
-    "import matplotlib.colors as colors"
+    "import matplotlib.colors as colors\n",
+    "\n",
+    "home = \"persistent/\" # home path for Logic Model Merger\n",
+    "model_name = \"Ikonomi2020\"\n",
+    "txt_file = home + \"LogicModelMerger/Models/\" + model_name + \".txt\"\n",
+    "sbml_file = home + \"LogicModelMerger/Models/\" + model_name + \".sbml\""
    ]
   },
   {
@@ -610,8 +615,7 @@
     }
    ],
    "source": [
-    "model_name = \"Ikonomi2020\"\n",
-    "ikonomi = biolqm.load(\"../../Models/\" + model_name + \".sbml\")\n",
+    "ikonomi = biolqm.load(sbml_file)\n",
     "ikonomi_lrg = biolqm.to_ginsim(ikonomi)\n",
     "ginsim.show(ikonomi_lrg)"
    ]
@@ -1076,7 +1080,7 @@
     }
    ],
    "source": [
-    "net = boolnet.loadNetwork(\"../../Models/\" + model_name + \".txt\")\n",
+    "net = boolnet.loadNetwork(txt_file)\n",
     "attr = boolnet.getAttractors(net)\n",
     "print(attr)"
    ]
@@ -1364,7 +1368,7 @@
     "\n",
     "df = df.T\n",
     "df.index = df.index.str.replace('Attr', 'S')\n",
-    "df.to_csv('../../SimulationResults/attr_' + model_name + '.csv')\n",
+    "df.to_csv(home + 'LogicModelMerger/SimulationResults/attr_' + model_name + '.csv')\n",
     "df"
    ]
   },

diff --git a/Reproducing selected models/Krumsiek2011/Krumsiek2011.ipynb b/Reproducing selected models/Krumsiek2011/Krumsiek2011.ipynb
@@ -466,9 +466,10 @@
     "import matplotlib.pyplot as plt\n",
     "from matplotlib.colors import ListedColormap\n",
     "\n",
+    "home = \"persistent/\" # home path for Logic Model Merger\n",
     "model_name = \"Krumsiek2011\"\n",
-    "txt_file = \"../../Models\" + model_name + \".txt\"\n",
-    "sbml_file = \"../../Models\" + model_name + \".sbml\""
+    "txt_file = home + \"LogicModelMerger/Models/\" + model_name + \".txt\"\n",
+    "sbml_file = home + \"LogicModelMerger/Models/\" + model_name + \".sbml\""
    ]
   },
   {
@@ -746,7 +747,7 @@
    "source": [
     "df = df.T\n",
     "df.index = df.index.str.replace('Attr', 'S')\n",
-    "df.to_csv('../../SimulationResults/attr_' + model_name + '.csv')"
+    "df.to_csv(home + 'LogicModelMerger/SimulationResults/attr_' + model_name + '.csv')"
    ]
   },
   {

diff --git a/Reproducing selected models/Latini2023/Latini2023.ipynb b/Reproducing selected models/Latini2023/Latini2023.ipynb
@@ -566,7 +566,16 @@
     "from itertools import combinations  # for iterating over sets\n",
     "import matplotlib.pyplot as plt # for modifying plots\n",
     "import seaborn as sns # for heatmap visualization\n",
-    "#import pyboolnet # for reproduce the original results"
+    "#import pyboolnet # for reproduce the original results\n",
+    "\n",
+    "home = \"persistent/\" # home path for Logic Model Merger\n",
+    "model_name_TKD = \"Latini2023_TKD\"\n",
+    "txt_file_TKD = home + \"LogicModelMerger/Models/\" + model_name_TKD + \".txt\"\n",
+    "sbml_file_TKD = home + \"LogicModelMerger/Models/\" + model_name_TKD + \".sbml\"\n",
+    "\n",
+    "model_name_JMD = \"Latini2023_JMD\"\n",
+    "txt_file_JMD = home + \"LogicModelMerger/Models/\" + model_name_JMD + \".txt\"\n",
+    "sbml_file_JMD = home + \"LogicModelMerger/Models/\" + model_name_JMD + \".sbml\""
    ]
   },
   {
@@ -621,7 +630,7 @@
    ],
    "source": [
     "# Load the data\n",
-    "JMD_lqm = biolqm.load(\"Latini2023_JMD.sbml\")\n",
+    "JMD_lqm = biolqm.load(sbml_file_JMD)\n",
     "\n",
     "# Use the GinSIM package to visualize it\n",
     "JMD_lrg = biolqm.to_ginsim(JMD_lqm)\n",
@@ -648,7 +657,7 @@
     }
    ],
    "source": [
-    "TKD_lqm = biolqm.load(\"Latini2023_TKD.sbml\")\n",
+    "TKD_lqm = biolqm.load(sbml_file_TKD)\n",
     "TKD_lrg = biolqm.to_ginsim(TKD_lqm)\n",
     "ginsim.show(TKD_lrg)"
    ]
@@ -922,7 +931,7 @@
    ],
    "source": [
     "# Read the apoptosis and proliferation data generated by ProxPath\n",
-    "apoptosis_df = pd.read_csv('paths_to_apoptosis_proliferation.txt', sep='\\t')\n",
+    "apoptosis_df = pd.read_csv(home+'LogicModelMerger/Data/ProxPath/paths_to_apoptosis_proliferation.txt', sep='\\t')\n",
     "apoptosis_df['node'] = apoptosis_df['QueryNode'].str.replace('/', '_')\n",
     "apoptosis_df = apoptosis_df[['node', 'EndNode', 'Final_Effect']].rename(columns={'EndNode': 'phenotype', 'Final_Effect': 'apoptosis'})\n",
     "apoptosis_df.head()"
@@ -1130,96 +1139,13 @@
     "Next, the authors have simulated the levels of apoptosis and proliferation, upon combinatorial knockout of FLT3 and one of the following key druggable kinases: ERK1/2, MEK1/2, GSK3A/B, IGF1R, JNK, KRAS, MEK1/2, mTOR, PDPK1, PI3K, p38, to predict novel combinatorial treatments reverting drug resistance of FLT3-ITD cells. Since the same strategy was used in this section, the results should also be identical and thus are skipped here.  "
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Clinical outcomes\n",
-    "Using the same strategy as in [Palma 2021](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7916657/), I next compare the phenotype scores with clinical features derived from the AML TGCA dataset. Specifically, the mutation-specific peripheral blood (PB) and bone marrow (BM) blast percentages with the predictions of our models.\n",
-    "\n",
-    "The integrated network score is calculated by substracting the proliferation score by the apoptosis score.   \n",
-    "     \n",
-    "Mutation and clinical data are downloaded from the [NIH GDC website](https://gdc.cancer.gov/about-data/publications/laml_2012):\n",
-    "1. [Supplemental Table 06: All somatic mutations with annotation and readcounts from DNA and RNA sequencing](SupplementalTable06.tsv)\n",
-    "2. [Patient Clinical Data](https://portal.gdc.cancer.gov/files/c07a64a0-7588-4653-95ef-982b41a1a804?aeTable_offset=20)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Data file path\n",
-    "tsv_file_path_genes = 'SupplementalTable06.tsv'\n",
-    "tsv_file_path_clinical = 'nationwidechildrens.org_clinical_patient_laml.txt'\n",
-    "\n",
-    "# Mutation data\n",
-    "df_genes = pd.read_csv(tsv_file_path_genes, sep='\\t', usecols=['TCGA_id', 'gene_name'])\n",
-    "\n",
-    "# clinical data \n",
-    "df_clinical = pd.read_csv(tsv_file_path_clinical, sep='\\t', usecols=['bcr_patient_barcode', \n",
-    "                                                            'blast_count',\n",
-    "                                                            'percent_blasts_peripheral_blood'])\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'JMD_PROLIFERATION': {'tumor': 4, 'FLT3i': 2},\n",
-       " 'TKD_PROLIFERATION': {'tumor': 3, 'FLT3i': 3},\n",
-       " 'JMD_APOPTOSIS': {'tumor': -2, 'FLT3i': 0},\n",
-       " 'TKD_APOPTOSIS': {'tumor': -2, 'FLT3i': -2}}"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(results)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Conclusion\n",
     "**Results from the Latini et al. 2023 paper are reproducible, and identical results could be generated using different platforms (Python versus R).**   \n"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {