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scTIM is a convenient tool for cell-type indicative marker detection based on single cell RNA-seq data

Project description

Introduction:

A convenient tool for marker detection based on single cell RNA-seq data.

Installation:

pip install sc_tim

Package usage example:

Run the following python script

import numpy as np import sc_tim
if __name__ == “__main__”:
file_name = ‘scTIM-master/Package/data.txt’ ### Defining file name alpha = 0.1;beta = 0.4;gamma = 0.5; ### Setting Parameters data,gene = sc_tim.PreProcess(file_name,’y’) ### Preprocessing data p = sc_tim.CellRedMatrix(data) ### Computing cell-cell distance matrix fs = sc_tim.GeneSpecificity(data) ### Computing gene specificity red = sc_tim.GeneRedMatrix(data) ### Computing gene-gene redundancy matrix w = sc_tim.ExtractGene(data,p,red,alpha,beta,gamma) ### Identifying markers by simulating annealing marker = [gene[i] for i in range(data.shape[0]) if w[i] == 1] ### Output the marker set

For more robust solution, we repeat the simulating annealing for 10 times and use the inersection of 10 outcomes as final result and these 10 repeats can be conducted by parallel computing:

w1 = sc_tim.ExtractGene(data,p,red,alpha,beta,gamma) w2 = sc_tim.ExtractGene(data,p,red,alpha,beta,gamma) w3 = sc_tim.ExtractGene(data,p,red,alpha,beta,gamma) w4 = sc_tim.ExtractGene(data,p,red,alpha,beta,gamma) w5 = sc_tim.ExtractGene(data,p,red,alpha,beta,gamma) w6 = sc_tim.ExtractGene(data,p,red,alpha,beta,gamma) w7 = sc_tim.ExtractGene(data,p,red,alpha,beta,gamma) w8 = sc_tim.ExtractGene(data,p,red,alpha,beta,gamma) w9 = sc_tim.ExtractGene(data,p,red,alpha,beta,gamma) w10 = sc_tim.ExtractGene(data,p,red,alpha,beta,gamma) w = (np.sum([w1,w2,w3,w4,w5,w6,w7,w8,w9,w10],0)==10) ### Intersection marker = [gene[i] for i in range(data.shape[0]) if w[i] == 1] ### Output the marker set

Requirements: Operating system: Linux (strongly recommended but not necessary) Python environment: python 3 Python package: numpy Memory: >= 3.0 Gb

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