JEFworks-Lab/STdeconvolve: Reference-free Cell-Type Deconvolution of Multi-Cellular Spatially Resolved Transcriptomics Data

STdeconvolve as an unsupervised, reference-free approach to infer latent cell-type proportions and transcriptional profiles within multi-cellular spatially-resolved pixels from spatial transcriptomics (ST) datasets. STdeconvolve builds on latent Dirichlet allocation (LDA), a generative statistical model commonly used in natural language processing for discovering latent topics in collections of documents. In the context of natural language processing, given a count matrix of words in documents, LDA infers the distribution of words for each topic and the distribution of topics in each document. In the context of ST data, given a count matrix of gene expression in multi-cellular ST pixels, STdeconvolve applies LDA to infer the putative transcriptional profile for each cell-type and the proportional representation of each cell-type in each multi-cellular ST pixel.

Getting started

Package details

Bioconductor views Bayesian GeneExpression RNASeq Software Spatial Transcriptomics Visualization
Maintainer
LicenseGPL-3
Version1.3.1
URL https://jef.works/STdeconvolve/
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
install.packages("remotes")
remotes::install_github("JEFworks-Lab/STdeconvolve")
JEFworks-Lab/STdeconvolve documentation built on July 17, 2024, 7:11 a.m.