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Deconvolve spots into cell type proportions

Source: R/dwls.R
dwls.Rd

Runs both rounds of spatialDWLS on an expression matrix. Each spot's result is a composition: the proportions of a solved spot sum to one.

Usage

dwls(
  expr,
  sign_matrix,
  clusters,
  n_cell = 50,
  cutoff = 2,
  logbase = 2,
  na_as_zero = FALSE,
  BPPARAM = NULL,
  chunk_size = 256L
)

Arguments

expr

log-scaled, normalised expression: features (rows) by spots (columns), as a base matrix or a Matrix (sparse is kept sparse). Spot IDs are taken from the column names.

sign_matrix

mean expression per cell type: signature features (rows) by cell type (columns), on the reverse-logged scale, e.g. from dwls_signature(). Not a binary marker matrix.

clusters

cluster of every spot. Either named by spot ID (matched against colnames(expr); extra entries, such as the clusters of a whole dataset when expr is a subset, are ignored) or unnamed in the column order of expr. Spots are grouped by cluster to pick candidate cell types in round 1. Every spot needs a non-NA cluster.

n_cell

numeric. expected number of cells per spot. In round 2 a cell type is kept for a spot only when its round-1 proportion is at least 1 / n_cell.

cutoff

numeric. PAGE enrichment z-score a cell type must reach in at least one spot of a cluster to be a candidate for that cluster. The two highest-scoring types are always candidates.

logbase

numeric. log base of expr.

na_as_zero

logical. spots that cannot be solved (no signal on the signature features) are NA by default; TRUE returns them as 0.

BPPARAM

a BiocParallel::BiocParallelParam for the per-cluster plans and per-spot solves, or NULL (default) to run serially.

chunk_size

integer. spots per parallel task.

Value

a numeric matrix, cell types (rows) by spots (columns).

See also

dwls_stream() for expression read in windows.

Examples

set.seed(1)
genes <- paste0("g", 1:30)
sig <- matrix(1, 30, 3, dimnames = list(genes, c("A", "B", "C")))
sig[1:10, "A"] <- sig[11:20, "B"] <- sig[21:30, "C"] <- 40

# 20 spots mixing A and B, 20 spots of C alone
mix <- cbind(rbind(a <- runif(20, 0.3, 0.7), 1 - a, 0),
             matrix(c(0, 0, 1), 3, 20))
counts <- matrix(rpois(30 * 40, sig %*% mix * 5), 30,
                 dimnames = list(genes, paste0("s", 1:40)))
clusters <- rep(c("mixed", "pure"), each = 20)

prop <- dwls(log2(counts + 1), sig, clusters, n_cell = 5)
round(prop[, c(1:3, 21:23)], 2)
#>     s1   s2   s3 s21 s22 s23
#> A 0.42 0.44 0.52   0   0   0
#> B 0.58 0.56 0.48   0   0   0
#> C 0.00 0.00 0.00   1   1   1