PanClassif: A machine learning classifier pipeline for TCGA pancancer classification

This is a complete machine learning pipeline package to work with TCGA cancer RNA-seq gene count data.

PanClassif PyPi

Data prerequisition

• TCGA cancer & normal samples downloaded using TCGA2STAT
• smoothed version of the above collected data using knn-smoothing (Wagner et al., 2017)
• Dataset Mendeley Click Here
• Dataset Drive Link

Functions

featSelect(homepath, cancerpath, normalpath, k)

Params

• homepath : (str) Path where you want to save all the generated files and folders.
• cancerpath : (str)
  Path where all the cancer's cancer gene expression matrix are located.
• normalpath : (str)
  Path where all the cancer's normal gene expression matrix are located.
• k : (int) The number of top genes you want to choose per cancer. (default: k=5) you can not put k less than 5

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dataProcess(homepath,names,cancerpath,smoothed_cancer,smoothed_normal,scale_mode)

Params

• homepath : (str) Path where you want to save all the generated files and folders.
• cancerpath : (str) Path where all the cancer's cancer gene expression matrix are located.
• names : (list) List of the cancer names found from featSelect function.
• smoothed_cancer : (str) Path where all the cancer's smoothed cancer gene expression matrix are located.
• smoothed_normal : (str) Path where all the cancer's smoothed normal gene expression matrix are located.
• scale_mode (int): Here (0 is for Standardization and 1 for normalization) for data scalling

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upsampled(names, homepath)

binary_merge(names, homepath)

multi_merge(names, homepath)

Params

• names : (list) List of the cancer names found from featSelect function.
• homepath : (str)
  Path where you want to save all the generated files and folders.

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classification(homepath, classifier, mode, save_model)

Params

• homepath : (str) Path where you want to save all the generated files and folders
• classifer : (sklearn's classification model) Provide the classification model's instance you want to use. For example: RandomForestClassifier(n_estimators=100).
• Or, classifer : (str) If you want to use "Neural Network" then just type "NN". For example: classifier = "NN"
• mode : (str) There is two mode 1) binary 2) multi. Use "binary" for binary classification & "multi" for multiclass classification. (default: mode = "binary")
• save_model : (str) Optional parameter. Use it only if you want to save the model. For example: save_model = "your_model_name"

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gsea(homepath)

• homepath : (str) Path where you want to save all the generated files and folders

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Example

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homepath = '/home'
cancerpath = '/home/cancer/'
normalpath = '/home/normal/'

smoothed_cancer = '/home/smoothed_cancer'
smoothed_normal = '/home/smoothed_normal'

Data Load and Process Phase

import panclassif as pc 
#You have to follow below order to work the code properly 
names = pc.featSelect(homepath,cancerpath,normalpath, k=5)
pc.dataProcess(homepath,names,cancerpath,smoothed_cancer,smoothed_normal)
pc.upsampled(names, homepath)
pc.binary_merge(names, homepath)
pc.multi_merge(names, homepath)

Classification Phase

from sklearn.ensemble import RandomForestClassifier
pc.classification(homepath, RandomForestClassifier(n_estimators=100), mode="multi", save_model="RF")

Gene enrichment check

pc.gsea(homepath)