cellXY

The cellXY package currently contains trained models to classify cells as male or female and to predict whether a cell is a male-female doublet or not.

The classifySex function takes a count matrix as input, computes required features and predict the sex label of each cell with the trained model. We have trained models for human and mouse cells seperately, and you need to specify the genome type of your data. Similarly, the findMfDoublet function uses trained machine learning models to identify male-female doublet cells in the dataset.

Installation

If you would like to view the cellXY vignette, you can install the released version of cellXY from github using the following commands:

# devtools/remotes won't install Suggested packages from Bioconductor
BiocManager::install(c("CellBench", "BiocStyle", "scater"))

remotes::install_github("phipsonlab/cellXY", build_vignettes = TRUE, 
dependencies = "Suggest")

In order to view the vignette for cellXY use the following command:

browseVignettes("cellXY")

If you don't care to view the glorious vignette you can also install cellXY as follows:

library(devtools)
devtools::install_github("phipsonlab/cellXY")

Sex label prediction example

This is a basic example which shows you how to obtain a sex label prediction for each cell.

library(speckle)
library(SingleCellExperiment)
library(CellBench)
library(org.Hs.eg.db)

sc_data <- load_sc_data()
sc_10x <- sc_data$sc_10x

counts <- counts(sc_10x)
ann <- select(org.Hs.eg.db, keys=rownames(sc_10x),
              columns=c("ENSEMBL","SYMBOL"), keytype="ENSEMBL")
m <- match(rownames(counts), ann$ENSEMBL)
rownames(counts) <- ann$SYMBOL[m]

sex <- classifySex(counts, genome="Hs")

table(sex$prediction)
boxplot(counts["XIST",]~sex$prediction)