Metadata-Version: 1.1
Name: sc-tim
Version: 0.0.4
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
        
        >>> 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: python3;numpy;Memory >= 3.0 Gb.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
