scTIM
Introduction
A convenient tool for marker detection based on single cell RNA-seq data.
Installation
pip install sc_timUsage example:
Then run the following python script
>>> import numpy as np
>>> import sc_tim
>>> 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 setFor 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 setRequirements:
Python environment: python 3
numpy
Memory >= 3.0 Gb
