if(!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("STdeconvolve")
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.
In this tutorial, we will walk through some of the main functionalities of
Given a counts matrix from pixel-resolution spatial transcriptomics data where
each spatially resolved measurement may represent mixtures from potentially
STdeconvolve infers the putative transcriptomic profiles of
cell-types and their proportional representation within each multi-cellular
spatially resolved pixel. Such a pixel-resolution spatial transcriptomics
dataset of the mouse olfactory bulb is built in and can be loaded.
data(mOB) pos <- mOB$pos ## x and y positions of each pixel cd <- mOB$counts ## matrix of gene counts in each pixel annot <- mOB$annot ## annotated tissue layers assigned to each pixel
STdeconvolve first feature selects for genes most likely to be relevant for
distinguishing between cell-types by looking for highly overdispersed genes
across ST pixels. Pixels with too few genes or genes with too few reads can also
## remove pixels with too few genes counts <- cleanCounts(counts = cd, min.lib.size = 100, min.reads = 1, min.detected = 1, verbose = TRUE)
## Converting to sparse matrix ...
## Filtering matrix with 262 cells and 15928 genes ...
## Resulting matrix has 260 cells and 14828 genes
## feature select for genes corpus <- restrictCorpus(counts, removeAbove = 1.0, removeBelow = 0.05, alpha = 0.05, plot = TRUE, verbose = TRUE)
## Removing 124 genes present in 100% or more of pixels...
## 14704 genes remaining...
## Removing 3009 genes present in 5% or less of pixels...
## 11695 genes remaining...
## Restricting to overdispersed genes with alpha = 0.05...
## Calculating variance fit ...
## Using gam with k=5...
## 232 overdispersed genes ...
## Using top 1000 overdispersed genes.
## number of top overdispersed genes available: 232
STdeconvolve then applies latent Dirichlet allocation (LDA), a generative
statistical model commonly used in natural language processing, to discover
STdeconvolve fits a range of LDA models to inform the
choice of an optimal
## Note: the input corpus needs to be an integer count matrix of pixels x genes ldas <- fitLDA(t(as.matrix(corpus)), Ks = seq(2, 9, by = 1), perc.rare.thresh = 0.05, plot=TRUE, verbose=TRUE)
## Time to fit LDA models was 0.96 mins
## Computing perplexity for each fitted model...
## Time to compute perplexities was 0 mins
## Getting predicted cell-types at low proportions...
## Time to compute cell-types at low proportions was 0 mins
In this example, we will use the model with the lowest model perplexity.
The shaded region indicates where a fitted model for a given K had an
alpha > 1.
alpha is an LDA parameter that is solved for during model
fitting and corresponds to the shape parameter of a symmetric Dirichlet
distribution. In the model, this Dirichlet distribution describes the cell-type
proportions in the pixels. A symmetric Dirichlet with
alpha > 1 would lead to
more uniform cell-type distributions in the pixels and difficulty identifying
distinct cell-types. Instead, we want models with
alpha < 1, resulting in
sparse distributions where only a few cell-types are represented in a given
theta matrix can be interpreted as the proportion of each
deconvolved cell-type across each spatially resolved pixel. The resulting
matrix can be interpreted as the putative gene expression profile for each
deconvolved cell-type normalized to a library size of 1. This
beta matrix can
be scaled by a depth factor (ex. 1000) for interpretability.
## select model with minimum perplexity optLDA <- optimalModel(models = ldas, opt = "min") ## Extract pixel cell-type proportions (theta) and cell-type gene expression ## profiles (beta) for the given dataset. ## We can also remove cell-types from pixels that contribute less than 5% of the ## pixel proportion and scale the deconvolved transcriptional profiles by 1000 results <- getBetaTheta(optLDA, perc.filt = 0.05, betaScale = 1000)
## Filtering out cell-types in pixels that contribute less than 0.05 of the pixel proportion.
deconProp <- results$theta deconGexp <- results$beta
We can now visualize the proportion of each deconvolved cell-type across the original spatially resolved pixels.
vizAllTopics(deconProp, pos, groups = annot, group_cols = rainbow(length(levels(annot))), r=0.4)
## Plotting scatterpies for 260 pixels with 8 cell-types...this could take a while if the dataset is large.