Metadata-Version: 1.1
Name: sc-tim
Version: 0.1.0
Summary: scTIM is a convenient tool for cell-type indicative marker detection based on single cell RNA-seq data
Home-page: https://github.com/Frank-Orwell/scTIM
Author: Zhanying Feng
Author-email: zyfeng@amss.ac.cn
License: AMSS
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
        
Platform: UNKNOWN
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
