Skip to content

use sparse count matrices #119

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
jamespeapen merged 3 commits into from
Feb 17, 2026
Merged

use sparse count matrices #119

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
c9eded2
fix(transformTFIDF): return a sparse matrix instead of dense
jamespeapen Nov 24, 2025
3ad2002
fix(shrinkBins): cbind assay and means to preserve sparsity
jamespeapen Nov 3, 2025
c58ac5d
chore(air): format style
jamespeapen Nov 3, 2025
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
13 changes: 7 additions & 6 deletions R/shrinkBins.R
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -61,11 +61,12 @@ shrinkBins <- function(
}

# get the prior means
prior.means <- prior.means %||% getGlobalMeans(
obj = original.x,
targets = targets,
assay = assay
)
prior.means <- prior.means %||%
getGlobalMeans(
obj = original.x,
targets = targets,
assay = assay
)

is.atac_or_rna <- assay %in% c("atac", "rna")
input.fun <- if (jse) {
Expand All @@ -84,7 +85,7 @@ shrinkBins <- function(

# bin the input
bin.mat <- getBinMatrix(
mat = as.matrix(cbind(input.assay, prior.means)),
mat = cbind(input.assay, prior.means),
genloc = rowRanges(x),
chr = chr,
res = res,
Expand Down
5 changes: 1 addition & 4 deletions R/transformTFIDF.R
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -41,10 +41,7 @@ transformTFIDF <- function(mat, scale.factor = 1e5, binarize = FALSE) {
tf <- t(t(mat.capped) / Matrix::colSums(mat.capped)) # compute term-frequency
tf@x <- log1p(tf@x * scale.factor) # scale
idf <- log(1 + ncol(mat.capped) / Matrix::rowSums(mat.capped)) # inverse-document frequency smooth
tfidf <- .tfidf(tf, idf) # transform

# cast back to a matrix since things like UMAP don't like sparse matrices
as.matrix(tfidf)
.tfidf(tf, idf) # transform
}

.binarize <- function(v) {
Expand Down