SIGMA: A clusterability measure for scRNA-seq data

IMPORTANT: This R package is deprecated. The new version is called phiclust and can be found here: Semraulab/phiclust

A package to calculate clusterability in clusters of scRNA-seq data. By applying this measure you can assess if meaningful variability is left in clusters. The corresponding paper can be found here A clusterability measure for single-cell transcriptomics reveals phenotypic subpopulations

Installation

# install.packages("devtools")
devtools::install_github("Siliegia/SIGMA")

Example

This how you can use the functions in this R package. The most important one is sigma_funct, which calculates the clusterability per cluster.

library(SIGMA)
library(splatter)
library(ggplot2)

#Load sample data simulated with splatter
data("splatO")

expr <- counts(splatO)
expr <- expr[rowSums(expr)>0,]

#Normalize and log-transform the data
expr.norm <- t(t(expr)/colSums(expr))*10000
expr.norm.log <- log(expr.norm + 1)

#Create toy example of a data set
test.cluster <- as.character(splatO$Group)
test.cluster[test.cluster == "Group3"] <- "Group2"
test.cluster[test.cluster == "Group4"] <- "Group2"

#Main funcion that calculates the clusterability
out <- sigma_funct(expr = expr.norm.log, clusters = test.cluster,
                   exclude = data.frame(clsm = log(colSums(expr) + 1)))

For ways to evaluate the results of this clusterability measure check out the vignette Guide_to_sigma and for a real scRNA-seq example you can have a look at our analysis of a fetal kidney Analysis_kidney.