Deprecation warning of aggregrateAcrossCells and fitTrendVar fix

Author: MSteketee

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: StatescopeR
 Type: Package
 Title: StatescopeR framework for discovery of cell states from cell type-specific gene expression profiles inferred from bulk mRNA profiles
-Version: 1.0.0
+Version: 1.0.1
 Authors@R:c(
     person(given = "Mischa", family = "Steketee", role = c("aut", "cre"),
              email = "m.f.b.steketee@amsterdamumc.nl",

NEWS.md

@@ -1,18 +1,14 @@
 ## StatescopeR 0.99.0 (3-6-2025)
-
 -   Initial Bioconductor submission
 
 ## StatescopeR 0.99.1-0.99.6 (16-7-2025 - 26-7-2025)
-
 -   Fixing Basilisk issues
 
 ## StatescopeR 0.99.7-0.99.21 (28-7-2025 - 23-7-2025)
-
 -   Adjusting Vignette & Examples to make Check process quick enough to not
 timeout
 
 ## StatescopeR 0.99.22 (22-10-2025)
-
 - Revision after review, in short: Some documentation changes, removal of custom
 functions in favor of existing good implementations & removal of new classes in 
 favor of adding to SummarizedExperiment metadata, see 
@@ -23,7 +19,6 @@ for more details.
 -   Minimizing Vignette & Examples to make Check process quick enough to not
 timeout
 
-
 ## StatescopeR 0.99.25-0.99.31 (26-11-2025)
 -   R dependency >= 4.6.0 reflecting new Bioconductor release and minimized
 number of samples for deconvolution for quicker vignette/examples
@@ -43,3 +38,9 @@ and made vignette less minimal
 
 ## StatescopeR 0.99.36 (8-4-2026)
 -   Removed mkl dependency for mac support and removed .bbsoptions for upcoming R universe build system
+
+## StatescopeR 1.0.0 (28-4-2026)
+-   Bioconductor release!
+
+## StatescopeR 1.0.1 (20-5-2026)
+-   Deprecation warning of aggregrateAcrossCells and fitTrendVar  fix

R/BLADE_deconvolution.R

@@ -49,8 +49,8 @@
 #'     scRNAseq <- scRNAseq[, !scRNAseq$`cell type` %in% celltypes_to_remove]
 #'
 #'     ## Create pseudobulk and normalize to cp10k
-#'     pseudobulk <- aggregateAcrossCells(scRNAseq, ids = scRNAseq$individual)
-#'     normcounts(pseudobulk) <- calculateCPM(pseudobulk) / 100
+#'     pseudobulk <- scuttle::aggregateAcrossCells(scRNAseq, ids = scRNAseq$individual)
+#'     normcounts(pseudobulk) <- scuttle::calculateCPM(pseudobulk) / 100
 #'     pseudobulk <- as(pseudobulk, "SummarizedExperiment")
 #'     rownames(pseudobulk) <- rownames(scRNAseq)
 #'

R/Refinement.R

@@ -47,8 +47,8 @@
 #'     scRNAseq <- scRNAseq[, !scRNAseq$`cell type` %in% celltypes_to_remove]
 #'
 #'     ## Create pseudobulk and normalize to cp10k
-#'     pseudobulk <- aggregateAcrossCells(scRNAseq, ids = scRNAseq$individual)
-#'     normcounts(pseudobulk) <- calculateCPM(pseudobulk) / 100
+#'     pseudobulk <- scuttle::aggregateAcrossCells(scRNAseq, ids = scRNAseq$individual)
+#'     normcounts(pseudobulk) <- scuttle::calculateCPM(pseudobulk) / 100
 #'     pseudobulk <- as(pseudobulk, "SummarizedExperiment")
 #'     rownames(pseudobulk) <- rownames(scRNAseq)
 #'     ## Load signature

R/create_signature.R

@@ -62,7 +62,7 @@ create_signature <- function(scRNAseq, hvg_genes = FALSE, n_hvg_genes = 3000L,
     ## Correct Omega by mean-var trend
     new_omega <- omega[, 0] ## make df with same # rows
     for (ct in colnames(omega)) {
-        trend <- fitTrendVar(mu[, ct], omega[, ct])$trend
+        trend <- scran::fitTrendVar(mu[, ct], omega[, ct])$trend
         new_omega[, ct] <- trend(mu[, ct])
     }
 

inst/scripts/example_Statescope.R

@@ -38,12 +38,12 @@ true_fractions <- gather_true_fractions(scRNAseq, ids = scRNAseq$individual,
 save(true_fractions, file = 'inst/extdata/example_true_fractions.RData')
 
 ## Normalize (cp10k) and logtransform scRNAseq
-cpm(scRNAseq) <- calculateCPM(scRNAseq)
+cpm(scRNAseq) <- scuttle::calculateCPM(scRNAseq)
 logcounts(scRNAseq) <- log1p(cpm(scRNAseq)/100)
 
 ## Create pseudobulk and normalize to cp10k (logging is done within Statescope)
-pseudobulk <- aggregateAcrossCells(scRNAseq, ids = scRNAseq$individual)
-normcounts(pseudobulk) <- calculateCPM(pseudobulk)/100
+pseudobulk <- scuttle::aggregateAcrossCells(scRNAseq, ids = scRNAseq$individual)
+normcounts(pseudobulk) <- scuttle::calculateCPM(pseudobulk)/100
 pseudobulk = as(pseudobulk, "SummarizedExperiment")
 rownames(pseudobulk) = rownames(scRNAseq)
 

man/BLADE_deconvolution.Rd

@@ -20,8 +20,8 @@ test_that("BLADE deconvolution works properly with prior on simulation data", {
     scRNAseq <- scRNAseq[, !scRNAseq$`cell type` %in% celltypes_to_remove]
 
     ## Create pseudobulk and normalize to cp10k
-    pseudobulk <- aggregateAcrossCells(scRNAseq, ids = scRNAseq$individual)
-    normcounts(pseudobulk) <- calculateCPM(pseudobulk) / 100
+    pseudobulk <- scuttle::aggregateAcrossCells(scRNAseq, ids = scRNAseq$individual)
+    normcounts(pseudobulk) <- scuttle::calculateCPM(pseudobulk) / 100
     pseudobulk <- as(pseudobulk, "SummarizedExperiment")
     rownames(pseudobulk) <- rownames(scRNAseq)
 

man/Refinement.Rd

@@ -105,7 +105,7 @@ StatescopeR takes the following inputs: 1) scRNAseq reference/signature 2) bulk
 
 ```{r 'Prepare StatescopeR input'}
 ## Normalize (cp10k) and logtransform scRNAseq
-cpm(scRNAseq) <- calculateCPM(scRNAseq)
+cpm(scRNAseq) <- scuttle::calculateCPM(scRNAseq)
 logcounts(scRNAseq) <- log1p(cpm(scRNAseq) / 100)
 
 ## Create scRNAseq reference/signature with 50 hvg for quick example
@@ -120,8 +120,8 @@ selected_genes <- select_genes(scRNAseq, 30L, 50L,
 )
 
 ## Create pseudobulk and normalize to cp10k (logging is done within Statescope)
-pseudobulk <- aggregateAcrossCells(scRNAseq, ids = scRNAseq$individual)
-normcounts(pseudobulk) <- calculateCPM(pseudobulk) / 100
+pseudobulk <- scuttle::aggregateAcrossCells(scRNAseq, ids = scRNAseq$individual)
+normcounts(pseudobulk) <- scuttle::calculateCPM(pseudobulk) / 100
 pseudobulk <- as(pseudobulk, "SummarizedExperiment")
 rownames(pseudobulk) <- rownames(scRNAseq)