diff --git a/R/shrinkBins.R b/R/shrinkBins.R
index 136f4a67..8bf41c22 100644
--- a/R/shrinkBins.R
+++ b/R/shrinkBins.R
@@ -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) {
@@ -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,
diff --git a/R/transformTFIDF.R b/R/transformTFIDF.R
index 394d0ba1..4f642da7 100644
--- a/R/transformTFIDF.R
+++ b/R/transformTFIDF.R
@@ -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) {