STdeconvolve enables reference-free cell-type deconvolution of multi-cellular pixel-resolution spatially resolved transcriptomics data
The overall approach is now published 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.
library(STdeconvolve) ## load built in data data(mOB) pos <- mOB$pos cd <- mOB$counts annot <- mOB$annot ## remove pixels with too few genes counts <- cleanCounts(cd, min.lib.size = 100) ## feature select for genes corpus <- restrictCorpus(counts, removeAbove=1.0, removeBelow = 0.05) ## choose optimal number of cell-types ldas <- fitLDA(t(as.matrix(corpus)), Ks = seq(2, 9, by = 1)) ## get best model results optLDA <- optimalModel(models = ldas, opt = "min") ## extract deconvolved cell-type proportions (theta) and transcriptional profiles (beta) results <- getBetaTheta(optLDA, perc.filt = 0.05, betaScale = 1000) deconProp <- results$theta deconGexp <- 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.
STdeconvolve, we recommend using
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")
We welcome any bug reports, enhancement requests, general questions, and other contributions. To submit a bug report or enhancement request, please use the
STdeconvolve GitHub issues tracker. For more substantial contributions, please fork this repo, push your changes to your fork, and submit a pull request with a good commit message.
Links below point to code outlining the preprocessing of datasets used for analyses in the manuscript.