Package: STdeconvolve 1.9.0

Brendan Miller

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.

Authors:Brendan Miller [aut, cre], Jean Fan [aut]

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NEWS

# Install 'STdeconvolve' in R:

install.packages('STdeconvolve', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/jefworks-lab/stdeconvolve/issues

Datasets:

mOB - Spatial transcriptomics of the mouse olfactory bulb

On BioConductor:STdeconvolve-1.9.0(bioc 3.20)STdeconvolve-1.8.0(bioc 3.19)

bioconductor-package

16 exports 0.61 score 67 dependencies

Last updated 2 months agofrom:2b5edb8070

Exports:annotateCellTypesGSEA cleanCounts correlationPlot fitLDA getBetaTheta getCorrMtx getOverdispersedGenes lsatPairs optimalModel perplexityPlot preprocess restrictCorpus topGenes vizAllTopics vizGeneCounts vizTopic

Dependencies:BH BiocParallel cli clue cluster codetools colorspace cpp11 dplyr fansi farver formatR futile.logger futile.options generics ggforce ggfun ggplot2 glue gridExtra gtable isoband labeling lambda.r lattice lifecycle liger magrittr MASS Matrix matrixStats mgcv modeltools munsell nlme NLP pillar pkgconfig plyr polyclip purrr R6 RColorBrewer Rcpp RcppArmadillo RcppEigen reshape2 rlang scales scatterpie slam snow stringi stringr systemfonts tibble tidyr tidyselect tm topicmodels tweenr utf8 vctrs viridis viridisLite withr xml2

STdeconvolve Vignette

Brendan F. Miller

Rendered fromvignette.Rmdusingknitr::rmarkdownon Jun 30 2024.

Last update: 2022-04-14
Started: 2022-02-15

Help page	Topics
Match deconvolved cell-types to ground truth cell-types based on transcriptional profiles	annotateCellTypesGSEA
Filter a counts matrix	cleanCounts
Generate heatmap of correlations	correlationPlot
Find the optimal number of cell-types K for the LDA model	fitLDA
Pull out cell-type proportions across pixels (theta) and cell-type gene probabilities (beta) matrices from fitted LDA models from fitLDA	getBetaTheta
Find Pearson's correlations between topics (cell-types) with respect to their proportions across documents (pixels), i.e. thetas, or gene probabilities, i.e. betas.	getCorrMtx
Normalize gene expression variance relative to transcriptome-wide expectations (Modified from SCDE/PAGODA2 code)	getOverdispersedGenes
Function to get Hungarian sort pairs via clue::lsat	lsatPairs
Spatial transcriptomics of the mouse olfactory bulb	mOB
Get the optimal LDA model	optimalModel
Plot the perplexity and rare cell-types versus fitted Ks	perplexityPlot
Pre-process ST pixel gene count matrices to construct corpus for input into LDA	preprocess
Restrict to informative words (genes) for topic modeling	restrictCorpus
Returns top n genes of each deconvolved cell-type for a given beta matrix	topGenes
Visualize all topic proportions across pixels with 'scatterpie'	vizAllTopics
Visualize gene counts for a given gene in the pixels. Can also see group assignment of spots.	vizGeneCounts
Visualize pixel proportions of a single cell-type.	vizTopic

Package: STdeconvolve 1.9.0

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

STdeconvolve Vignette

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