move modelGeneVar to internal function, switch scran to suggest

DESCRIPTION

@@ -29,14 +29,15 @@ Imports:
     Matrix,
     MASS,
     abind,
-    scran,
     SummarizedExperiment,
     methods,
     MatrixGenerics,
     BiocGenerics,
     BiocSingular,
     BiocParallel
 Suggests: 
+    scran,
+    scater,
     knitr,
     UpSetR,
     gridExtra,

R/stabMap.R

@@ -324,11 +324,13 @@ stabMap <- function(assay_list,
               " variance ranking"
             )
           }
-          genevars <- scran::modelGeneVar(
-            assay_list[[reference_dataset]][features, ]
-          )
-          genevars_sorted <- genevars[order(genevars$bio, decreasing = TRUE), ]
-          features <- rownames(genevars_sorted)[seq_len(maxFeatures)]
+          # genevars <- scran::modelGeneVar(
+          #   assay_list[[reference_dataset]][features, ]
+          # )
+          # genevars_sorted <- genevars[order(genevars$bio, decreasing = TRUE), ]
+          # features <- rownames(genevars_sorted)[seq_len(maxFeatures)]
+          # replacement below
+          features = selectFeatures(assay_list[[reference_dataset]], features, maxFeatures)
         }
 
         labels_train <- labels_list[[reference_dataset]]
@@ -476,16 +478,21 @@ stabMap <- function(assay_list,
                 )
               }
 
-              genevars <- scran::modelGeneVar(
-                assay_list[[path_current[1]]][features_current, ],
-                min.mean = 0
-              )
-              genevars_sorted <- genevars[
-                order(genevars$bio, decreasing = TRUE),
-              ]
-              features_current <- rownames(
-                genevars_sorted
-              )[seq_len(maxFeatures)]
+              # genevars <- scran::modelGeneVar(
+              #   assay_list[[path_current[1]]][features_current, ],
+              #   min.mean = 0
+              # )
+              # genevars_sorted <- genevars[
+              #   order(genevars$bio, decreasing = TRUE),
+              # ]
+              # features_current <- rownames(
+              #   genevars_sorted
+              # )[seq_len(maxFeatures)]
+              # replacement below
+
+              features_current = selectFeatures(assay_list[[path_current[1]]],
+                                                features_current,
+                                                maxFeatures)
             }
 
             coef <- stats::lm.fit(

R/stabMap_utils.R

@@ -0,0 +1,31 @@
+#' selectFeatures
+#'
+#' For a given assay and set of features, perform variance ranking and select
+#' a subset of features
+#'
+#' @param assay An assay matrix rows are features, columns are cells
+#' @param features Character vector of the current features that are selected
+#' @param maxFeatures Integer of the number of maxFeatures to select
+#'
+#' @return A character vector of the selected features according to variance
+#' ranking.
+#'
+#' @keywords internal
+selectFeatures = function(assay, features, maxFeatures) {
+
+  if (!requireNamespace("scran", quietly = TRUE)) {
+    stop("Install 'scran' select features when maxFeatures is selected")
+  }
+
+  genevars <- scran::modelGeneVar(
+    assay[features, ],
+    min.mean = 0
+  )
+  genevars_sorted <- genevars[
+    order(genevars$bio, decreasing = TRUE),
+  ]
+  features_selected <- rownames(
+    genevars_sorted
+  )[seq_len(maxFeatures)]
+  return(features_selected)
+}

vignettes/stabMap_PBMC_Multiome.Rmd

@@ -24,9 +24,10 @@ knitr::opts_chunk$set(
 
 ```{r setup, message=FALSE, warning=FALSE}
 library(StabMap)
-library(scran)
 library(magrittr)
 
+library(scater)
+library(scran)
 library(SingleCellMultiModal)
 library(gridExtra)
 ```