Main release 19

.Rbuildignore

@@ -1,3 +1,5 @@
+^renv$
+^renv\.lock$
 ^.*\.Rproj$
 ^\.Rproj\.user$
 ^LICENSE\.md$
@@ -11,3 +13,7 @@
 ^_pkgdown\.yml$
 ^pkgdown$
 ^\.github$
+^.lintr
+^renv
+^renv.lock
+^.editorconfig

.gitignore

@@ -8,3 +8,8 @@ APL.Rproj
 /Meta/
 inst/doc
 docs
+
+/renv/
+renv.lock
+.Rprofile
+.editorconfig

.lintr

@@ -0,0 +1,4 @@
+linters: linters_with_defaults(
+    indentation_linter(indent = 4L),
+    commented_code_linter = NULL
+  )

DESCRIPTION

@@ -1,15 +1,14 @@
 Package: APL
 Type: Package
 Title: Association Plots
-Version: 1.7.1
+Version: 1.9.0
 Authors@R:
     c(person(given = "Clemens",
         family = "Kohl",
         role = c("cre", "aut"),
         email = "kohl.clemens@gmail.com"),
     person(given = "Elzbieta",
-        family = "Gralinska",
-        role = c("aut"),
+        family = "Gralinska", role = c("aut"),
         email = "gralinska@molgen.mpg.de"),
     person(given = "Martin",
         family = "Vingron",
@@ -50,7 +49,8 @@ Imports:
     utils,
     org.Hs.eg.db,
     org.Mm.eg.db,
-    rlang
+    rlang,
+    Matrix
 Depends: R (>= 4.2)
 Suggests: 
     BiocStyle,
@@ -70,5 +70,12 @@ Collate:
     'import_packages.R'
     'plot.R'
     'utils-pipe.R'
+Config/reticulate:
+  list(
+    packages = list(
+      list(package = "numpy"),
+      list(package = "torch")
+    )
+  )
 SystemRequirements: python, pytorch, numpy
 URL: https://vingronlab.github.io/APL/

R/CA.R

@@ -19,9 +19,9 @@ NULL
 #' grand total of the original matrix "tot"
 #' as well as row and column masses "rowm" and "colm" respectively.
 #'
-comp_std_residuals <- function(mat){
+comp_std_residuals <- function(mat) {
 
-  if (!is(mat, "matrix")){
+  if (!is(mat, "matrix") & !is(mat, "dgCMatrix")) {
     mat <- as.matrix(mat)
   }
   stopifnot(
@@ -31,8 +31,8 @@ comp_std_residuals <- function(mat){
 
   tot <- sum(mat)
   P <- mat/tot               # proportions matrix
-  rowm <- rowSums(P)          # row masses
-  colm <- colSums(P)          # column masses
+  rowm <- Matrix::rowSums(P)          # row masses
+  colm <- Matrix::colSums(P)          # column masses
 
   E <- rowm %o% colm      # expected proportions
   S <-  (P - E) / sqrt(E)         # standardized residuals
@@ -47,10 +47,10 @@ comp_std_residuals <- function(mat){
 #' @param obj A matrix.
 #' @return Input matrix with rows & columns consisting of only 0 removed.
 rm_zeros <- function(obj){
-  stopifnot(is(obj, "matrix"))
+  stopifnot(is(obj, "matrix") | is(obj, "dgCMatrix"))
 
-  no_zeros_rows <- rowSums(obj) > 0
-  no_zeros_cols <- colSums(obj) > 0
+  no_zeros_rows <- Matrix::rowSums(obj) > 0
+  no_zeros_cols <- Matrix::colSums(obj) > 0
   if (sum(!no_zeros_rows) != 0){
     ## Delete genes with only zero values across all columns
     warning("Matrix contains rows with only 0s. ",
@@ -218,11 +218,18 @@ run_cacomp <- function(obj,
   # S <- (diag(1/sqrt(r)))%*%(P-r%*%t(c))%*%(diag(1/sqrt(c)))
   # message("Running singular value decomposition ...")
 
-  if (python == TRUE){
+  if (python == TRUE) {
+
+    # python implementation currently can only handle dense matrices of the base class.
+    if (!is(S, "matrix")) {
+      S <- as.matrix(S)
+    }
+
     svd_torch <- NULL
     # require(reticulate)
     # source_python('./python_svd.py')
     reticulate::source_python(system.file("python/python_svd.py", package = "APL"))
+
     SVD <- svd_torch(S)
     # SVD <- svd_linalg_torch(S)
     names(SVD) <- c("U", "D", "V")
@@ -244,8 +251,8 @@ run_cacomp <- function(obj,
   if(inertia == TRUE){
     #calculate inertia
     SVD$tot_inertia <- sum(SVD$D^2)
-    SVD$row_inertia <- rowSums(S^2)
-    SVD$col_inertia <- colSums(S^2)
+    SVD$row_inertia <- Matrix::rowSums(S^2)
+    SVD$col_inertia <- Matrix::colSums(S^2)
   }
 
   SVD$row_masses <- rowm
@@ -393,6 +400,34 @@ setMethod(f = "cacomp",
 
 })
 
+#' @rdname cacomp
+#' @export
+setMethod(f = "cacomp",
+          signature=(obj="dgCMatrix"),
+          function(obj,
+                   coords = TRUE,
+                   princ_coords = 3,
+                   python = FALSE,
+                   dims = NULL,
+                   top = 5000,
+                   inertia = TRUE,
+                   rm_zeros = TRUE,
+                   ...){
+
+    caobj <- run_cacomp(obj = obj,
+                        coords = coords,
+                        princ_coords = princ_coords,
+                        python = python,
+                        dims = dims,
+                        top = top,
+                        inertia = inertia,
+                        rm_zeros = rm_zeros,
+                        ...)
+
+    return(caobj)
+
+})
+
 
 #' Correspondance Analysis for Seurat objects
 #'