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Aggregate genes using the same method as cells #742

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merged 4 commits into from
Dec 20, 2024
Merged

Aggregate genes using the same method as cells #742

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7a79ab1
Aggregate genes using the same method as cells
rfriedman22 Nov 28, 2024
2a27eab
Handle cases where a gene is represented by multiple groups
rfriedman22 Dec 20, 2024
f02532f
Need to use `match` when setting both rows and cols of the sparse Matrix
rfriedman22 Dec 20, 2024
b04f2a8
Remove a browser call
rfriedman22 Dec 20, 2024
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42 changes: 17 additions & 25 deletions R/cluster_genes.R
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Original file line number Diff line number Diff line change
Expand Up @@ -479,33 +479,25 @@ aggregate_gene_expression <- function(cds,
if (any(short_name_mask)) {
geneids <- as.character(gene_group_df[[1]])
geneids[short_name_mask] <- row.names(fData(cds))[match(
geneids[short_name_mask], fData(cds)$gene_short_name)]
geneids[short_name_mask], fData(cds)$gene_short_name
)]
gene_group_df[[1]] <- geneids
}

# gene_group_df = gene_group_df[row.names(fData(cds)),]

# FIXME: this should allow genes to be part of multiple groups. group_by
# over the second column with a call to colSum should do it.
gene_groups = unique(gene_group_df[,2])
agg_gene_groups = lapply(gene_groups, function(gene_group){
genes_in_group = unique(gene_group_df[gene_group_df[,2] == gene_group,1])
gene_expr_mat = agg_mat[genes_in_group,]
if (length(dn <- dim(gene_expr_mat)) < 2L)
return(NA)
if (gene_agg_fun == "mean"){
res = Matrix::colMeans(agg_mat[genes_in_group,])
}else if (gene_agg_fun == "sum"){
res = Matrix::colSums(agg_mat[genes_in_group,])
}
return(res)
})

agg_mat_colnames = colnames(agg_mat)
agg_mat = do.call(rbind, agg_gene_groups)
row.names(agg_mat) = gene_groups
agg_mat = agg_mat[is.na(agg_gene_groups) == FALSE, , drop=FALSE]
colnames(agg_mat) = agg_mat_colnames

unique_gene_ids <- unique(gene_group_df[, 1])
agg_mat <- agg_mat[unique_gene_ids, , drop = FALSE]
gene_groups <- unique(gene_group_df[, 2])
X <- Matrix::sparseMatrix(
i = match(gene_group_df[, 2], gene_groups),
j = match(gene_group_df[, 1], unique_gene_ids),
x = 1,
dims = c(length(gene_groups), length(unique_gene_ids)),
)
agg_mat <- X %*% agg_mat
if (gene_agg_fun == "mean") {
agg_mat <- agg_mat / Matrix::rowSums(X)
}
row.names(agg_mat) <- gene_groups
}

if (is.null(cell_group_df) == FALSE){
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