Skip to contents

spatialDWLS

Cell type deconvolution of spatial transcriptomics spots with dampened weighted least squares.

spatialDWLS estimates what fraction of each spot is made up of each cell type, given a mean-expression reference built from single-cell data. Each spot’s result is a composition: the proportions of a solved spot sum to one.

The method (Dong and Yuan 2021) runs in two rounds:

  1. Cluster round. For each cluster of spots, cell types enriched in it (PAGE z-score above cutoff) become candidates, and every spot is solved against them.
  2. Spot round. Per spot, cell types whose round-1 proportion is below 1 / n_cell are dropped and the spot is solved again against the rest.

Each solve is DWLS (Tsoucas et al. 2019): non-negative weighted least squares whose weights are iterated and capped by a cross-validated dampening constant.

This package is the standalone engine. It works on plain matrices, so it has no dependency on Giotto; Giotto’s runDWLSDeconv() calls it.

Website: https://drieslab.github.io/spatialDWLS/, with two worked examples: plain matrices and through Giotto.

Installation 

# install.packages("remotes")
remotes::install_github("drieslab/spatialDWLS")

Usage 

library(spatialDWLS)

# reference: mean normalised (not logged) expression per cell type
sig <- dwls_signature(sc_norm, sign_gene = markers, cell_type = sc_labels)

# spots: log-normalised expression, features x spots; one cluster per spot
prop <- dwls(spot_lognorm, sig, clusters = spot_clusters, n_cell = 10)

prop is a cell types x spots matrix. Spots with no signal on the signature features cannot be solved and are NA (na_as_zero = TRUE returns 0).

  • Sparse input stays sparse; only the signature features are densified, one cluster at a time.
  • Parallel: pass a BiocParallel param, e.g. BPPARAM = BiocParallel::MulticoreParam(8). Results are identical to the serial run.
  • Larger than memory: dwls_stream() takes a read_window(i, features) callback and reads spots one window at a time. This is how Giotto’s disk-backed stores (GiottoDisk) run it.
  • Reproducible: the dampening constant’s cross-validation is seeded internally; your session’s RNG state is left untouched.

Citation

Dong R, Yuan GC (2021). SpatialDWLS: accurate deconvolution of spatial transcriptomic data. Genome Biology 22, 145. https://doi.org/10.1186/s13059-021-02362-7

Tsoucas D, Dong R, Chen H, Zhu Q, Guo G, Yuan GC (2019). Accurate estimation of cell-type composition from gene expression data. Nature Communications 10, 2975. https://doi.org/10.1038/s41467-019-10802-z