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Merged
dimalvovs merged 3 commits into from
Mar 18, 2025
Merged

44 result format in sparse implementation when fulltrue #45

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9772089
add chopBy param, update test to make it fail
dimalvovs Mar 17, 2025
29e04f3
fix the output, close #44
dimalvovs Mar 17, 2025
a3d6f59
bump version
dimalvovs Mar 17, 2025
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2 changes: 1 addition & 1 deletion DESCRIPTION
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Original file line number Diff line number Diff line change
Expand Up @@ -2,7 +2,7 @@ Package: projectR
Type: Package
Title: Functions for the projection of weights from PCA, CoGAPS, NMF,
correlation, and clustering
Version: 1.23.1
Version: 1.23.2
Author: Gaurav Sharma, Charles Shin, Jared Slosberg, Loyal Goff, Genevieve Stein-O'Brien
Authors@R: c(
person("Gaurav", "Sharma", role = c("aut")),
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17 changes: 11 additions & 6 deletions R/projectR.R
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Expand Up @@ -71,6 +71,7 @@ setMethod("projectR",signature(data="matrix",loadings="matrix"),function(
#' @param NP vector of integers indicating which columns of loadings object to use. The default of NP=NA will use entire matrix.
#' @param full logical indicating whether to return the full model solution. By default only the new pattern object is returned.
#' @param model Optional arguements to choose method for projection
#' @param chopBy number of columns to chop the data into (chopping helps runnning large datasets)
#' @rdname projectR-methods
#' @aliases projectR
setMethod("projectR",signature(data="dgCMatrix",loadings="matrix"),function(
Expand All @@ -79,7 +80,8 @@ setMethod("projectR",signature(data="dgCMatrix",loadings="matrix"),function(
dataNames = NULL, # a vector with names of data rows
loadingsNames = NULL, # a vector with names of loadings rows
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.
full=FALSE, # logical indicating whether to return the full model solution. By default only the new pattern object is returned.
chopBy=1000 # number of columns to chop the data into
){

if(!is.null(NP)) {
Expand All @@ -93,7 +95,7 @@ setMethod("projectR",signature(data="dgCMatrix",loadings="matrix"),function(

chop <- function(sparsematrix) {
coln <- ncol(sparsematrix)
bins <- seq(1, coln, by = 1000)
bins <- seq(1, coln, by = chopBy)
lapply(seq_along(bins), function(i) {
start <- bins[i]
end <- ifelse(i < length(bins), bins[i + 1] - 1, coln)
Expand All @@ -110,11 +112,14 @@ setMethod("projectR",signature(data="dgCMatrix",loadings="matrix"),function(
print(w[1])

if(full==TRUE) {
if(length(projectionList)==1) {#if only one chunk
if(length(projectionList)==1) {#if only one chunk all OK
res <- projectionList[[1]]
} else {
projectionFit <- lapply(projectionList, function(x) do.call(cbind, x))
res <- projectionFit
} else {#if multiple chunks - gather pvalues and projections
pvalues <- do.call(cbind,
lapply(projectionList, function(x) x[["pval"]]))
projections <- do.call(cbind,
lapply(projectionList, function(x) x[["projection"]]))
res <- list(projection=projections, pval=pvalues)
}
} else {
res <- do.call(cbind, projectionList)
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5 changes: 4 additions & 1 deletion man/projectR-methods.Rd
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5 changes: 5 additions & 0 deletions tests/testthat/test_projectR.R
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Expand Up @@ -222,6 +222,11 @@ test_that("projection works on sparse data matrix with full=TRUE", {
expect_no_error(projectR(sparse, loadings))

pdense <- projectR(dense, loadings, full=TRUE)
#case with default number of chopBy, when it's > ncol
psparse <- projectR(sparse, loadings, full=TRUE)
expect_identical(pdense, psparse)

#case with chopBy < ncol
psparse_chunked <- projectR(sparse, loadings, full=TRUE, chopBy=10)
expect_identical(pdense, psparse_chunked)
})
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