Reference-free cell type deconvolution of multi-cellular pixel-resolution spatially resolved transcriptomics data

Abstract

Recent technological advancements have enabled spatially resolved transcriptomic profiling but at multi-cellular pixel resolution, thereby hindering the identification of cell-type-specific spatial patterns and gene expression variation. To address this challenge, we develop STdeconvolve as a reference-free approach to deconvolve underlying cell types comprising such multi-cellular pixel resolution spatial transcriptomics (ST) datasets. Using simulated as well as real ST datasets from diverse spatial transcriptomics technologies comprising a variety of spatial resolutions such as Spatial Transcriptomics, 10X Visium, DBiT-seq, and Slide-seq, we show that STdeconvolve can effectively recover cell-type transcriptional profiles and their proportional representation within pixels without reliance on external single-cell transcriptomics references. STdeconvolve provides comparable performance to existing reference-based methods when suitable single-cell references are available, as well as potentially superior performance when suitable single-cell references are not available. STdeconvolve is available as an open-source R software package with the source code available at https://github.com/JEFworks-Lab/STdeconvolve.

BayesTME: A unified statistical framework for spatial transcriptomics

“…We benchmarked BayesTME against other methods: BayesSpace 11 , cell2location 16 , DestVI 15 , CARD 29 , RCTD 30 , STdeconvolve 14 , stLearn 12 , and Giotto 13 on simulated data based on real single-cell RNA sequencing (scRNA) data. We randomly sampled K * cell types from a previously-clustered scRNA dataset 16 ; we conducted experiments for K * from 3 to 8.…”

“…Spatial clustering methods 11,12,13 fuse spots together to effectively capture regions of constant cell type proportion with varying cell counts. Spot deconvolution methods 14,15,16 separate the aggregate signals into independent component signals with each attributable to a different cell type. Spatial differential expression methods 17,18 assess the aggregate spot signal to detect regions where individual genes or gene sets follow a spatial pattern.…”

Uncovering the spatial landscape of molecular interactions within the tumor microenvironment through latent spaces

“…To this end, we calculate the fractional gene expression FSE kj in pattern k at spot j as where i is the gene index. We use the ‘vizAllTopics‘ function from the ‘STdeconvolve‘ package [17] to visualize each spot as a pie chart showing the fractional gene expression in each pattern.…”

“…, where i is the gene index. We use the 'vizAllTopics' function from the 'STdeconvolve' package [17] to visualize each spot as a pie chart showing the fractional gene expression in each pattern.…”

“…The technique has been applied to characterize the cellular and molecular composition of tumors [13][14][15]. Robust analysis pipelines for cell-based analysis and cellular deconvolution have been proposed to model the cellular composition of spatial-transcriptomics data [16][17][18][19][20] and cellular phenotypes within each spot [21]. While spot deconvolution methods can infer linear combinations of molecular markers that are reflective of cellular co-localization, new computational methods are needed to characterize the molecular changes resulting from cell-cell interaction at a genome-wide scale.…”