address R CHECK warnings round 1

.travis.yml

@@ -3,4 +3,3 @@
 language: R
 sudo: false
 cache: packages
-warnings_are_errors: false

DESCRIPTION

@@ -19,6 +19,7 @@ Imports:
     e1071 (>= 1.6),
     parallel (>= 3.4),
     BiocInstaller,
+    BiocParallel,
     preprocessCore,
     Biobase,
     GSVA,
@@ -33,6 +34,7 @@ Suggests:
     pkgdown
 Remotes:
     bioc::BiocInstaller,
+    bioc::BiocParallel,
     bioc::preprocessCore,
     bioc::Biobase,
     bioc::GSVA,

Makefile

@@ -18,7 +18,7 @@ roxygenize: | .dev_deps_installed
 	Rscript -e "library(methods); library(devtools); document()"
 
 .PHONY: check
-check: | roxygenize
+check: | roxygenize docs
 	Rscript -e 'devtools::check()'
 
 .PHONY: install

NAMESPACE

@@ -17,11 +17,14 @@ export(set_cibersort_mat)
 export(timer_available_cancers)
 export(xCell.data)
 import(dplyr)
+import(limSolve)
 import(magrittr)
 import(methods)
 import(preprocessCore)
 import(readr)
 import(sva)
+importFrom(BiocParallel,bpparam)
+importFrom(EPIC,EPIC)
 importFrom(dplyr,select)
 importFrom(testit,assert)
 importFrom(tibble,as_tibble)

R/immune_deconvolution_methods.R

@@ -7,6 +7,7 @@
 #' @importFrom testit assert
 #' @import readr
 #' @importFrom tibble as_tibble
+#' @importFrom EPIC EPIC
 NULL
 
 
@@ -40,6 +41,8 @@ xCell.data = xCell::xCell.data
 #' CIBERSORT is only freely available to academic users.
 #' A license an the binary can be obtained from https://cibersort.stanford.edu.
 #'
+#' @param path path to cibersort R script.
+#'
 #' @export
 set_cibersort_binary = function(path) {
   assign("cibersort_binary", path, envir=config_env)
@@ -50,6 +53,8 @@ set_cibersort_binary = function(path) {
 #' CIBERSORT is only freely available to academic users.
 #' A license an the binary can be obtained from https://cibersort.stanford.edu.
 #'
+#' @param path path to cibersort matrix.
+#'
 #' @export
 set_cibersort_mat = function(path) {
   assign("cibersort_mat", path, envir=config_env)
@@ -75,8 +80,6 @@ set_cibersort_mat = function(path) {
 #'     'lihc', 'luad', 'lusc', 'prad', 'sarc', 'pcpg', 'paad', 'tgct',
 #'     'ucec', 'ov', 'skcm', 'dlbc', 'kirc', 'acc', 'meso', 'thca',
 #'     'uvm', 'ucs', 'thym', 'esca', 'stad', 'read', 'coad', 'chol'
-#' @param tumor ignored for this method
-#' @param arrays ignored for this method
 deconvolute_timer = function(gene_expression_matrix, indications=NULL) {
   indications = tolower(indications)
   assert("indications fit to mixture matrix", length(indications) == ncol(gene_expression_matrix))
@@ -152,7 +155,10 @@ deconvolute_cibersort = function(gene_expression_matrix,
 
 #' Annotate unified cell_type names
 #'
-#' (map the cell_types of the different methods to a common name)
+#' map the cell_types of the different methods to a common name
+#'
+#' @param result_table output of `deconvolute`
+#' @param method one of `immune_deconvolution_methods`.
 annotate_cell_type = function(result_table, method) {
   cell_type_map %>%
     filter(method_dataset == !!method) %>%

R/quantiseq.R

@@ -14,7 +14,7 @@
 #'     If TRUE, the "--rmgenes" parameter is set to "none".
 #' @param signame name of the signature matrix. Currently only `TIL10` is available.
 #' @param tumor 	specifies whether expression data are from tumor samples. If TRUE, signature genes
-#'     with high expression in tumor samples are removed (see [1]).
+#'     with high expression in tumor samples are removed.
 #'     Default: FALSE.
 #' @param mRNAscale specifies whether cell fractions must be scaled to account for cell-type-specific
 #'     mRNA content.

R/quantiseq_helper.R

@@ -3,6 +3,8 @@
 #' Source code from https://github.com/FFinotello/quanTIseq
 #'
 #' @name quantiseq_helper
+#'
+#' @import limSolve
 NULL
 
 fixMixture<-function(mix.mat, arrays=FALSE) {
@@ -146,9 +148,6 @@ quanTIseq<-function(currsig, currmix, scaling, method) {
 }
 
 DClsei<-function(b,A,G,H,scaling) {
-
-  library(limSolve)
-
   sc<-norm(A,"2")
   A<-A/sc
   b<-b/sc

R/single_cell_simulation.R

@@ -70,7 +70,7 @@ make_random_bulk = function(eset, cell_fractions, n_cells=500, combine=mean) {
 #' @param eset `Biobase::ExpressionSet` with a `cell_type` column in `pData`.
 #' @param cell_fractions n x n_cell_types dataframe with the fraction for each
 #'     sample in each row.
-#' @param n_cell number of single cells to use in each sample
+#' @param n_cells number of single cells to use in each sample
 #' @param combine callback function used to aggregate the counts.
 #'
 #' @seealso make_random_bulk

R/timer.R

@@ -4,9 +4,10 @@
 #' again is an adapted version of the original TIMER source code
 #' from http://cistrome.org/TIMER/download.html.
 #'
-#' The method is described in Li et al. Genome Biology 2016;17(1):174. [PMID 27549193].
+#' The method is described in Li et al. Genome Biology 2016;17(1):174., PMID 27549193.
 #'
 #' @import sva
+#' @importFrom BiocParallel bpparam
 #'
 #' @name timer
 NULL
@@ -108,6 +109,8 @@ RemoveBatchEffect <- function(cancer.exp, immune.exp, immune.cellType) {
 
 
 #' process batch table and check cancer types.
+#'
+#' @param args environment
 check_cancer_types <- function(args) {
   if (length(args$batch) != 0) {
     TimerINFO("Enter batch mode\n")
@@ -128,9 +131,11 @@ check_cancer_types <- function(args) {
 
 
 #' Constrained regression method implemented in Abbas et al., 2009
+#'
+#' @param XX immume expression data
+#' @param YY cancer expression data
+#' @param w ?
 GetFractions.Abbas <- function(XX, YY, w=NA){
-  ## XX is immune expression data
-  ## YY is cancer expression data
   ss.remove=c()
   ss.names=colnames(XX)
   while(T){

vignettes/immunedeconv.Rmd

@@ -1,10 +1,14 @@
 ---
 title: "Getting started with `immunedeconv`"
-output: html_document
 bibliography: references.bib
 biblio-style: apalike
 link-citations: yes
 colorlinks: yes
+output: rmarkdown::html_vignette
+vignette: >
+  %\VignetteIndexEntry{Vignette Title}
+  %\VignetteEngine{knitr::rmarkdown}
+  \usepackage[utf8]{inputenc}
 ---
 
 ```{r setup, include=FALSE}