`SPOTlight`: Spatial Transcriptomics Deconvolution

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Documentation for package ‘SPOTlight’ version 1.13.1

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Package NEWS.

Help Pages

.filter	.init_nmf <- function(x, groups, mgs, n_top = NULL, gene_id = "gene", group_id = "cluster", weight_id = "weight") # check validity of input arguments if (is.null(n_top)) n_top <- max(table(mgs[[group_id]])) stopifnot( is.character(gene_id), length(gene_id) == 1, is.character(group_id), length(group_id) == 1, is.character(weight_id), length(weight_id) == 1, c(gene_id, group_id, weight_id) is.numeric(n_top), length(n_top) == 1, round(n_top) == n_top) ng <- nrow(x) nc <- ncol(x) names(ks) <- ks <- unique(groups) # subset 'n_top' features mgs <- split(mgs, mgs[[group_id]]) mgs <- lapply(mgs, function(df) o <- order(df[[weight_id]], decreasing = TRUE) n <- ifelse(nrow(df) < n_top, nrow(df), n_top) df[o, ][seq_len(n), ] ) # subset unique features mgs <- lapply(ks, function(k) g1 <- mgs[[k]][[gene_id]] g2 <- unlist(lapply(mgs[ks != k], '[[', gene_id)) mgs[[k]][!g1 ) # W is of dimension (#groups)x(#features) with W(i,j) # equal to weight if j is marker for i, and ~0 otherwise W <- vapply(ks, function(k) w <- numeric(ng) + 1e-12 names(w) <- rownames(x) ws <- mgs[[k]][[weight_id]] w[mgs[[k]][[gene_id]]] <- ws return(w) , numeric(ng)) # there is no need to initialize H tp <- paste0("topic_", seq_len(length(ks))) dimnames(W) <- list(rownames(x), tp) return(W) Filter features from expression matrix
data	Synthetic single-cell, mixture and marker data
getMGS	Synthetic single-cell, mixture and marker data
mockSC	Synthetic single-cell, mixture and marker data
mockSP	Synthetic single-cell, mixture and marker data
plotCorrelationMatrix	Plot Correlation Matrix
plotHeatmap	Plot group interactions
plotImage	Plot JP(E)G/PNG/Raster/RGB images
plotInteractions	Plot group interactions
plotNetwork	Plot group interactions
plotSpatialScatterpie	Spatial scatterpie
plotTopicProfiles	Plot NMF topic profiles
runDeconvolution	Run Deconvolution using NNLS model
SPOTlight	Deconvolution of mixture using single-cell data
trainNMF	train NMF model