updated R functions to support SingleCellExperiment objects

Author: nlawlor

R/fast_iasva.R

@@ -10,9 +10,10 @@
 #'  the next iteration to find further hidden factors.  
 #' @importFrom irlba irlba
 #' @importFrom SummarizedExperiment SummarizedExperiment assay
+#' @importFrom SingleCellExperiment SingleCellExperiment
 #'
-#' @param Y A SummarizedExperiment class containing read counts where
-#' rows represent genes and columns represent samples.
+#' @param Y A SummarizedExperiment or SingleCellExperiment class containing
+#' read counts where rows represent genes and columns represent samples.
 #' @param X  A design matrix of known variables (e.g., patient ID, gender).
 #' @param intercept If intercept = FALSE, the linear 
 #' intercept is not included in the model.
@@ -51,11 +52,12 @@
 fast_iasva <- function(Y, X, intercept = TRUE, num.sv = NULL, pct.cutoff = 1,
                        num.tsv = NULL, tol = 1e-10, verbose = FALSE) {
   # error handling
-  stopifnot(class(Y)[1] == "SummarizedExperiment", is.numeric(tol),
+  stopifnot(class(Y)[1] == "SummarizedExperiment" | class(Y) == "SingleCellExperiment",
+            is.numeric(tol),
             is.matrix(X))
 
   # transpose the read counts
-  Y <- t(assay(Y))
+  Y <- as.matrix(t(assay(Y)))
   message("fast IA-SVA running...")
   if (min(Y) < 0) {
     Y <- Y + abs(min(Y))

R/find_markers.R

@@ -6,9 +6,10 @@
 #'  and returns a read counts matrix of the markers.
 #' @importFrom stats lm p.adjust
 #' @importFrom SummarizedExperiment SummarizedExperiment assay
+#' @importFrom SingleCellExperiment SingleCellExperiment
 #'  
-#' @param Y A SummarizedExperiment class containing read counts where
-#' rows represent genes and columns represent samples.
+#' @param Y A SummarizedExperiment or SingleCellExperiment class containing
+#' read counts where rows represent genes and columns represent samples.
 #' @param iasva.sv  matrix of estimated surrogate variables,
 #'  one column for each surrogate variable. 
 #' @param method multiple testing adjustment method, default = "BH".
@@ -37,13 +38,14 @@
 find_markers <- function(Y, iasva.sv, method = "BH", sig.cutoff = 0.05,
                          rsq.cutoff = 0.3, verbose = FALSE) {
   # error handling
-  stopifnot(class(Y)[1] == "SummarizedExperiment", is.numeric(sig.cutoff),
+  stopifnot(class(Y)[1] == "SummarizedExperiment" | class(Y) == "SingleCellExperiment",
+            is.numeric(sig.cutoff),
             is.numeric(rsq.cutoff), is.matrix(iasva.sv),
             method %in% c("holm", "hochberg", "hommel", "bonferroni",
                           "BH", "BY", "fdr", "none"))
 
   # transpose the read counts
-  Y <- t(assay(Y))
+  Y <- as.matrix(t(assay(Y)))
   if (min(Y) < 0) {
     Y <- Y + abs(min(Y))
   }

R/iasva.R

@@ -9,8 +9,9 @@
 #'  next iteration to find further hidden factors.  
 #'  
 #' @importFrom SummarizedExperiment SummarizedExperiment assay
-#' @param Y A SummarizedExperiment class containing read counts where
-#' rows represent genes and columns represent samples.
+#' @importFrom SingleCellExperiment SingleCellExperiment
+#' @param Y A SummarizedExperiment or SingleCellExperiment class containing
+#' read counts where rows represent genes and columns represent samples.
 #' @param X  A model matrix of known variables
 #'  including the primary variables of interest. 
 #' @param intercept If intercept = FALSE, the linear intercept
@@ -57,7 +58,8 @@ iasva <- function(Y, X, intercept = TRUE, num.sv = NULL, permute = TRUE,
                   num.p = 100, sig.cutoff = 0.05, threads = 1,
                   num.sv.permtest = NULL, tol = 1e-10, verbose = FALSE) {
   # error handling
-  stopifnot(class(Y)[1] == "SummarizedExperiment", is.numeric(tol),
+  stopifnot(class(Y)[1] == "SummarizedExperiment" | class(Y) == "SingleCellExperiment",
+            is.numeric(tol),
             is.matrix(X), is.numeric(num.p), is.numeric(sig.cutoff),
             is.numeric(threads))
   message("IA-SVA running...")

R/iasva_unit.R

@@ -2,17 +2,19 @@
 #' @importFrom irlba irlba
 #' @importFrom BiocParallel bplapply MulticoreParam
 #' @importFrom SummarizedExperiment SummarizedExperiment assay
+#' @importFrom SingleCellExperiment SingleCellExperiment
 
 iasva_unit <- function(Y, X, intercept = TRUE, permute = TRUE, num.p = 100,
                        threads = 1, num.sv.permtest = NULL,
                        tol = 1e-10, verbose = FALSE) {
   # error handling
-  stopifnot(class(Y)[1] == "SummarizedExperiment", is.numeric(tol),
+  stopifnot(class(Y)[1] == "SummarizedExperiment" | class(Y) == "SingleCellExperiment",
+            is.numeric(tol),
             is.matrix(X), is.numeric(num.p),
             is.numeric(threads))
 
   # transpose the read counts
-  Y <- t(assay(Y))
+  Y <- as.matrix(t(assay(Y)))
   if (min(Y) < 0) {
     Y <- Y + abs(min(Y))
   }

R/study_R2.R

@@ -9,9 +9,10 @@
 #' @importFrom stats .lm.fit cutree dist hclust lm p.adjust resid
 #' @importFrom cluster silhouette
 #' @importFrom SummarizedExperiment SummarizedExperiment assay
+#' @importFrom SingleCellExperiment SingleCellExperiment
 #'
-#' @param Y A SummarizedExperiment class containing read counts where
-#' rows represent genes and columns represent samples.
+#' @param Y A SummarizedExperiment or SingleCellExperiment class containing
+#' read counts where rows represent genes and columns represent samples.
 #' @param iasva.sv  matrix of estimated surrogate variables,
 #'  one column for each surrogate variable. 
 #' @param selected.svs list of SVs that are selected for the
@@ -43,7 +44,7 @@
 study_R2 <- function(Y, iasva.sv, selected.svs = 2,
                      no.clusters = 2, verbose = FALSE) {
   # error handling
-  stopifnot(class(Y)[1] == "SummarizedExperiment", 
+  stopifnot(class(Y)[1] == "SummarizedExperiment" | class(Y) == "SingleCellExperiment",
             is.numeric(selected.svs),
             is.matrix(iasva.sv), is.numeric(no.clusters))
   C.scores <- matrix(0, 0, 0)