Merge pull request #24 from VasudhaJha/feature/export-all-files

Feature/export all files

README.md

@@ -2,13 +2,13 @@
 
 Genomap is an entropy-based cartography strategy to contrive the high dimensional gene expression data into a configured image format with explicit integration of the genomic interactions. This unique cartography casts the gene-gene interactions into a spatial configuration and enables us to extract the deep genomic interaction features and discover underlying discriminative patterns of the data. For a wide variety of applications (cell clustering and recognition, gene signature extraction, single-cell data integration, cellular trajectory analysis, dimensionality reduction, and visualization), genomap drastically improves the accuracy of data analyses as compared to state-of-the-art techniques.
 
-# Required packages
+## Required packages
 
 scipy, scikit-learn, pot, numpy
 
 If you face any issues with packages, please check the environment section of our Code-Ocean capsule (https://doi.org/10.24433/CO.0640398.v1), where you can check the package versions.
 
-# How to use genomap
+## How to use genomap
 
 The easiest way to start with genomap is to install it from pypi using 
 
@@ -17,25 +17,22 @@ pip install genomap
 ```
 The data should be in cell (row) x gene (column) format. Genomap construction needs only one parameter: the size of the genomap (row and column number). The row and column number can be any number starting from 1. You can create square or rectangular genomaps. The number of genes in your dataset should be less than or equal to the number of pixels in the genomap. Genomap construction is very fast and you should get the genomaps within a few seconds.
 
-# Sample data
+## Sample data
 
-To run the example code below, you will need to download the required data file. You can download it from [here](https://drive.google.com/file/d/1kkbI9_6zD80Jr5OhMkGlcdMOeWcRfz7b/view?usp=drive_link).
+To run the example codes below, you will need to download data files from [here](https://drive.google.com/drive/folders/1xq3bBgVP0NCMD7bGTXit0qRkL8fbutZ6?usp=drive_link).
 
-# Example code
+## Example codes
+
+### Example 1 - Construct a genomap
 
 ```python
 import pandas as pd # Please install pandas and matplotlib before you run this example
-
 import matplotlib.pyplot as plt
-
 import scipy
-
 import genomap as gp
 
 data = pd.read_csv('TM_data.csv', header=None, delim_whitespace=False)
-
 colNum=31 # Column number of genomap
-
 rowNum=31 # Row number of genomap
 
 dataNorm=scipy.stats.zscore(data,axis=0,ddof=1) # Normalization of the data
@@ -45,12 +42,50 @@ genoMaps=gp.construct_genomap(dataNorm,rowNum,colNum) # Construction of genomaps
 findI=genoMaps[0,:,:,:]
 
 plt.figure(1) # Plot the first genomap
-
 plt.imshow(findI, origin = 'lower', extent = [0, 10, 0, 10], aspect = 1)
-
 plt.title('Genomap of a cell from TM dataset')
 ```
 
+### Example 2 - Try out genoVis, genoTraj and genoMOI
+
+```python
+import scipy.io as sio
+import numpy as np
+from genomap.genoVis import compute_genoVis
+from genomap.genoTraj import compute_genoTraj
+from genomap.genoMOI import compute_genoMOI
+
+dx = sio.loadmat('reducedData_divseq.mat')
+data=dx['X']
+gt_data = sio.loadmat('GT_divseq.mat')
+y = np.squeeze(gt_data['GT'])
+n_clusters = len(np.unique(y))
+
+
+resVis=compute_genoVis(data,n_clusters=n_clusters, colNum=33,rowNum=33)
+
+dx = sio.loadmat('organoidData.mat')
+data=dx['X3']
+gt_data = sio.loadmat('cellsPsudo.mat')
+Y_time = np.squeeze(gt_data['newGT'])
+
+
+outGenoTraj=compute_genoTraj(data)
+
+dx = sio.loadmat('MOIdata/dataBaronX.mat')
+data=dx['dataBaron']
+dx = sio.loadmat('MOIdata/dataMuraroX.mat')
+data2=dx['dataMuraro']
+dx = sio.loadmat('MOIdata/dataScapleX.mat')
+data3=dx['dataScaple']
+dx = sio.loadmat('MOIdata/dataWangX.mat')
+data4=dx['dataWang']
+dx = sio.loadmat('MOIdata/dataXinX.mat')
+data5=dx['dataXin']
+
+resVis=compute_genoMOI(data, data2, data3, data4, data5, colNum=44, rowNum=44)
+```
+
 # Citation
 
 If you use the genomap code, please cite our Nature Communications paper: https://www.nature.com/articles/s41467-023-36383-6

genomap/genoMOI/genoMOI.py

@@ -6,14 +6,14 @@
 """
 
 from tensorflow.keras.optimizers import Adam
-from genomap.ConvIDEC import ConvIDEC
+from utils.ConvIDEC import ConvIDEC
 # from ConvIDEC import ConvIDEC
 from sklearn.feature_selection import VarianceThreshold
 from genomap.genomap import construct_genomap
 import umap
-from genomap.gTraj_utils import nearest_divisible_by_four
+from utils.gTraj_utils import nearest_divisible_by_four
 # from gTraj_utils import nearest_divisible_by_four
-from genomap.utils_MOI import *
+from utils.utils_MOI import *
 # from utils_MOI import * 
 
 def compute_genoMOI(*arrays,n_clusters=None, colNum, rowNum):  

genomap/genoTraj/genoTraj.py

@@ -7,11 +7,11 @@
 
 import phate
 import numpy as np
-from genomap.class_discriminative_opt import ClassDiscriminative_OPT
+from utils.class_discriminative_opt import ClassDiscriminative_OPT
 import scipy
-from genomap.gTraj_utils import compute_cluster_distances, nearest_divisible_by_four
+from utils.gTraj_utils import compute_cluster_distances, nearest_divisible_by_four
 from tensorflow.keras.optimizers import Adam
-from genomap.ConvIDEC import ConvIDEC
+from utils.ConvIDEC import ConvIDEC
 from sklearn.feature_selection import VarianceThreshold
 from genomap.genomap import construct_genomap
 # from gTraj_utils import nearest_divisible_by_four

genomap/genoVis/genoVis.py

@@ -6,12 +6,12 @@
 """
 
 from tensorflow.keras.optimizers import Adam
-from genomap.ConvIDEC import ConvIDEC
+from utils.ConvIDEC import ConvIDEC
 from sklearn.feature_selection import VarianceThreshold
 from genomap.genomap import construct_genomap
 import umap
 # from gTraj_utils import nearest_divisible_by_four
-from genomap.gTraj_utils import nearest_divisible_by_four
+from utils.gTraj_utils import nearest_divisible_by_four
 import scanpy as sc
 import numpy as np
 import pandas as pd

genomap/ConvDEC.py → genomap/utils/ConvDEC.py

@@ -9,8 +9,7 @@
 from tensorflow.keras.layers import Conv2D, Conv2DTranspose, Dense, Flatten, Reshape, InputLayer
 from tensorflow.keras.models import Sequential, Model
 from tensorflow.keras.preprocessing.image import ImageDataGenerator
-from FcDEC import FcDEC, ClusteringLayer
-
+from .FcDEC import FcDEC, ClusteringLayer
 
 def CAE(input_shape=(28, 28, 1), filters=[32, 64, 128, 10]):
     model = Sequential()

genomap/ConvIDEC.py → genomap/utils/ConvIDEC.py

@@ -8,7 +8,7 @@
 """
 
 from tensorflow.keras.models import Model
-from ConvDEC import ConvDEC
+from .ConvDEC import ConvDEC
 
 
 class ConvIDEC(ConvDEC):

genomap/FcDEC.py → genomap/utils/FcDEC.py

@@ -17,7 +17,7 @@
 from tensorflow.keras.initializers import VarianceScaling
 from tensorflow.keras.preprocessing.image import ImageDataGenerator
 from sklearn.cluster import KMeans
-import metrics
+from .metrics import *
 
 
 def autoencoder(dims, act='relu'):

genomap/FcIDEC.py → genomap/utils/FcIDEC.py

@@ -8,7 +8,7 @@
 """
 
 from tensorflow.keras.models import Model
-from FcDEC import FcDEC
+from .FcDEC import FcDEC
 
 
 class FcIDEC(FcDEC):

genomap/utils/__init__.py

@@ -0,0 +1,9 @@
+from .class_discriminative_opt import *
+from .ConvDEC import *
+from .ConvIDEC import *
+from .FcDEC import *
+from .FcIDEC import *
+from .group_centroid_opt import *
+from .gTraj_utils import *
+from .metrics import *
+from .utils_MOI import *

setup.py

@@ -5,7 +5,7 @@
 
 setup(
     name="genomap",
-    version="1.0.5",
+    version="1.0.6",
     author="Md Tauhidul Islam",
     author_email="[email protected]",
     description="Genomap converts tabular gene expression data into spatially meaningful images.",