sc-REnF

An Entropy Based Feature Selection- Application on Single cell RNA Sequence Data

Pre-requisites

R version 4.0.0 or higher

Python 3.7

Install

library("devtools")

install_github("Snehalikalall/sc-REnF")

Check the installation:

library(scREnF)

Load required packages

R packages

 library(SingleCellExperiment)
 library(foreach)
 library(doParallel)
 library(Linnorm)

Python Packages:

pip install scanpy
pip install leidenalg

Usage of the R functions

Preprocess raw data using DataProcessing.R function

Biase_data<- readRDS("Data/yan.rds")
data <- assay(Biase_data) 
annotation <- Biase_data[[1]] #already factor type class
colnames(data) <- annotation
yan_process = normalized_data(data)

Use Renyifeature.R to select features using Renyi entropy, Tsallisfeature.R to select features using Tsallis entropy

# load the preprocess data. Data should be cells in row, genes in coloumn.
data=t(as.matrix(read.csv("Data/yan_process.csv",header=FALSE)))
cell<-as.matrix(read.csv("Data/yan_celltype.csv",header=FALSE))
gene<-as.matrix(read.csv("Data/yan_gene.csv",header=FALSE))
n <- nrow(data)
col<-ncol(data)
count=ncol(data)
p=40
q=0.3
nf=500
#nf: Number of feature to be selected, default is 500; P: Number of cores, default is 40;
# q-value = 0.3 for tsallis, q-value=0.7 for Renyi

# Renyi entropy based Feature Selection, the function returns data with selected features
Feadata=Renyifeature(data,cell,gene,p,q,nf)

# Tsallis entropy based Feature Selection, the function returns the data with selected features
Feadata=Tsallisfeature(data,cell,gene,p,q,nf)

A dry run on Darmanis and CBMC data

A demo run of sc-REnF for Darmanis data

A demo run of sc-REnF for CBMC data