mimic the usual call, cover full=TRUE

R/projectR.R

@@ -78,39 +78,58 @@ setMethod("projectR",signature(data="dgCMatrix",loadings="matrix"),function(
   loadings, # a matrix of continous values to be projected with unique rownames
   dataNames = NULL, # a vector with names of data rows
   loadingsNames = NULL, # a vector with names of loadings rows
-  NP=NA, # vector of integers indicating which columns of loadings object to use. The default of NP=NA will use entire matrix.
+  NP=NULL, # vector of integers indicating which columns of loadings object to use. The default of NP=NA will use entire matrix.
   full=FALSE # logical indicating whether to return the full model solution. By default only the new pattern object is returned.
   ){
 
-  #columns of dgcMatrix are LHS for stats::lm, and columns of loadings are the
-  #dense RHS (predictors). sometimes dgcMatrix is too big to fit RAM, so we
-  #just project in chunks lm models as supported by stats::lm/limma::lmFit
+  if(!is.null(NP)) {
+    loadings<-loadings[,NP]
+  }
+
+  #match genes in data sets
+  if(is.null(dataNames)){
+    dataNames <- rownames(data)
+  }
+  if(is.null(loadingsNames)){
+    loadingsNames <- rownames(loadings)
+  }
+
+  dataM<-geneMatchR(data1=data, data2=loadings, data1Names=dataNames, data2Names=loadingsNames, merge=FALSE)
+  print(paste(as.character(dim(dataM[[2]])[1]),'row names matched between data and loadings'))
+  print(paste('Updated dimension of data:',as.character(paste(dim(dataM[[2]]), collapse = ' '))))
 
   print("dgCMatrix detected, projecting in chunks.")
+  #columns of dgcMatrix are LHS for stats::lm, and columns of loadings are the
+  #dense RHS (predictors). sometimes dgcMatrix is too big to fit RAM, so we
+  #just fit chunks of lm models as supported by stats::lm/limma::lmFit
 
   chop <- function(sparsematrix) {
-    NCOL <- ncol(sparsematrix)
-    bins <- seq(1, NCOL, by = 1000)
+    coln <- ncol(sparsematrix)
+    bins <- seq(1, coln, by = 1000)
     lapply(seq_along(bins), function(i) {
       start <- bins[i]
-      end <- ifelse(i < length(bins), bins[i + 1] - 1, NCOL)
+      end <- ifelse(i < length(bins), bins[i + 1] - 1, coln)
       return(start:end)
     })
   }
-  #capture print statement that projectR generated when geneMatchR is called
-  warns <- invisible(capture.output(
-    projectionList <- lapply(chop(data), function(i) {
-      projectR(as.matrix(data[,i]), loadings, dataNames, loadingsNames, NP, full)
+  #discard print statements projectR generates each time a chunk is called
+  invisible(capture.output(
+    projectionList <- lapply(chop(dataM[[2]]), function(i) {
+      projectR(as.matrix(dataM[[2]][,i]), dataM[[1]], full=full)
     })
   ))
 
-  projectionPatterns <- do.call(cbind, projectionList)
-
-  if(full==TRUE){
-      projectionFit <- list('projection'=projectionPatterns, 'pval'=pval.matrix)
-      return(projectionFit)
+  if(full==TRUE) {
+      if(length(projectionList)==1) {#if only one chunk
+        res <- projectionList[[1]]
+      } else {
+        projectionFit <- lapply(projectionList, function(x) do.call(cbind, x))
+        res <- projectionFit
+      }
+  } else {
+    res <- do.call(cbind, projectionList)
   }
-  else{return(projectionPatterns)}
+  return(res)
 })
 
 

tests/testthat/test_projectR.R

@@ -200,16 +200,30 @@ test_that("results are correctly formatted for P value mode",{
 
 })
 
-test_that("projection works on sparse data matrix",{
+test_that("projection works on sparse data matrix", {
   dense <- as.matrix(p.RNAseq6l3c3t)
   sparse <- as(p.RNAseq6l3c3t, "sparseMatrix")
   loadings <- CR.RNAseq6l3c3t@featureLoadings
 
   expect_true("dgCMatrix" %in% class(sparse))
-  expect_no_error(projectR(data, loadings))
+  expect_no_error(projectR(sparse, loadings))
 
   pdense <- projectR(dense, loadings)
   psparse <- projectR(sparse, loadings)
   expect_identical(projectR(dense, loadings),
                    projectR(sparse, loadings))
+})
+
+test_that("projection works on sparse data matrix with full=TRUE", {
+  dense <- as.matrix(p.RNAseq6l3c3t)
+  sparse <- as(p.RNAseq6l3c3t, "sparseMatrix")
+  loadings <- CR.RNAseq6l3c3t@featureLoadings
+
+  expect_true("dgCMatrix" %in% class(sparse))
+  expect_no_error(projectR(sparse, loadings))
+
+  pdense <- projectR(dense, loadings, full=TRUE)
+  psparse <- projectR(sparse, loadings, full=TRUE)
+  expect_identical(projectR(dense, loadings),
+                   projectR(sparse, loadings))
 })