STdeconvolve enables reference-free cell-type deconvolution of multi-cellular pixel-resolution spatial transcriptomics data. The overall approach is detailed in the official paper out in Nature Communications.
STdeconvolve is an unsupervised machine learning approach to deconvolve multi-cellular pixel-resolution spatial transcriptomics datasets in order to recover the putative transcriptomic profiles of cell-types and their proportional representation within spatially resolved pixels without reliance on external single-cell transcriptomics references.
To install STdeconvolve, we recommend using remotes:
require(remotes)
remotes::install_github('JEFworks-Lab/STdeconvolve')STdeconvolve is also now available through Bioconductor.
Note that through Bioconductor (release 3.15), the R version must be >=4.2.
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
# The following initializes usage of Bioc devel
BiocManager::install(version='devel')
BiocManager::install("STdeconvolve")
Installation should take a few minutes on a typical desktop computer.
The default package branch R dependency is >=4.1, however, the devel branch is >=3.6.
library(STdeconvolve)
## load built in data
data(mOB)
pos<-mOB$poscd<-mOB$countsannot<-mOB$annot## remove pixels with too few genescounts<- cleanCounts(cd, min.lib.size=100)
## feature select for genescorpus<- restrictCorpus(counts, removeAbove=1.0, removeBelow=0.05)
## choose optimal number of cell-typesldas<- fitLDA(t(as.matrix(corpus)), Ks= seq(2, 9, by=1))
## get best model resultsoptLDA<- optimalModel(models=ldas, opt="min")
## extract deconvolved cell-type proportions (theta) and transcriptional profiles (beta)results<- getBetaTheta(optLDA, perc.filt=0.05, betaScale=1000)
deconProp<-results$thetadeconGexp<-results$beta## visualize deconvolved cell-type proportions
vizAllTopics(deconProp, pos,
groups=annot, group_cols= rainbow(length(levels(annot))),
r=0.4) 
More details can be found in the tutorials.
- Getting started with
STdeconvolve - Additional features with
STdeconvolve - Annotating deconvolved cell-types
- Analysis of 10X Visium data
- Examples of when
STdeconvolvemay fail - Discussion and examples of processing multiple datasets together or separately
For commands to reproduce the preprocessing of certain datasets used in the manuscript, check out:
https://jef.works/STdeconvolve/
and scroll down to the section: Reproducing Analyses.