Removing curl dependency

Source/DESCRIPTION → DESCRIPTION

@@ -1,12 +1,12 @@
 Package: MCPcounter
 Type: Package
 Title: Estimating tissue-infiltrating immune and other stromal subpopulations abundances using gene expression
-Version: 1.2.0
+Version: 1.2.1
 Date: 2020-02-14
 Author: Etienne Becht, Aurelien de Reynies, Florent Petitprez
 Maintainer: Etienne Becht <[email protected]>
 Description: Estimating tissue-infiltrating immune and other stromal subpopulations abundances using gene expression
 License: GPL-3
-Depends: R (>= 2.6.0), curl
+Depends: R (>= 2.6.0)
 LazyData: true
 RoxygenNote: 7.1.0

Source/R/MCPcounter.R → R/MCPcounter.R

@@ -31,8 +31,8 @@ appendSignatures=function(xp,markers){
 MCPcounter.estimate=function(
                              expression,
                              featuresType=c("affy133P2_probesets","HUGO_symbols","ENTREZ_ID","ENSEMBL_ID")[1],
-                             probesets=read.table(curl:::curl("https://raw.githubusercontent.com/ebecht/MCPcounter/master/Signatures/probesets.txt"),sep="\t",stringsAsFactors=FALSE,colClasses="character"),
-                             genes=read.table(curl:::curl("https://raw.githubusercontent.com/ebecht/MCPcounter/master/Signatures/genes.txt"),sep="\t",stringsAsFactors=FALSE,header=TRUE,colClasses="character",check.names=FALSE)
+                             probesets=read.table(system.file("extdata", "probesets.txt", package="MCPcounter"),sep="\t",stringsAsFactors=FALSE,colClasses="character"),
+                             genes=read.table(system.file("extdata", "genes.txt", package="MCPcounter"),sep="\t",stringsAsFactors=FALSE,header=TRUE,colClasses="character",check.names=FALSE)
                              ){
     ## marker.names=c("T cells","CD8 T cells","Cytotoxic lymphocytes","NK cells","B lineage","Monocytic lineage","Myeloid dendritic cells","Neutrophils","Endothelial cells","Fibroblasts")
 

README.md

@@ -3,8 +3,8 @@ This repository hosts the source code corresponding to the method described in o
 
 To install it, the easiest is to use the `R` package `devtools` and its function `install_github`. To do so, open an `R` session and enter
 
-    install.packages(c("devtools","curl")) ##Installs devtools and the MCPcounter dependancy 'curl'
+    install.packages("devtools") ##Installs devtools
     library(devtools)
-    install_github("ebecht/MCPcounter",ref="master", subdir="Source")
+    install_github("ebecht/MCPcounter",ref="master")
 
 Examples on how to run the algorithm on your data are shown in the documentation `?MCPcounter.estimate`

Source/man/MCPcounter.estimate.Rd → man/MCPcounter.estimate.Rd

@@ -3,14 +3,14 @@
 \title{MCPcounter.estimate}
 \description{this function produces a matrix with abundance estimates from an expression matrix}
 \usage{MCPcounter.estimate(expression,featuresType=c("affy133P2_probesets","HUGO_symbols","ENTREZ_ID","ENSEMBL_ID")[1],
-		probesets=read.table(curl("http://raw.githubusercontent.com/ebecht/MCPcounter/master/Signatures/probesets.txt"),sep="\t",stringsAsFactors=FALSE,colClasses="character"),
-		genes=read.table(curl("http://raw.githubusercontent.com/ebecht/MCPcounter/master/Signatures/genes.txt"),sep="\t",stringsAsFactors=FALSE,header=TRUE,colClasses="character",check.names=FALSE)
+		probesets=read.table(system.file("extdata", "probesets.txt", package="MCPcounter"),sep="\t",stringsAsFactors=FALSE,colClasses="character"),
+		genes=read.table(system.file("extdata", "genes.txt", package="MCPcounter"),sep="\t",stringsAsFactors=FALSE,header=TRUE,colClasses="character",check.names=FALSE)
 )}
 \arguments{
 \item{expression}{matrix or data.frame with features in rows and samples in columns}
 \item{featuresType}{type of identifiers for expression features. Defaults to "affy133P2_probesets" for Affymetrix Human Genome 133 Plus 2.0 probesets. Other options are "HUGO_symbols" (Official gene symbols), "ENTREZ_ID" (Entrez Gene ID)or "ENSEMBL_ID" (ENSEMBL Gene ID)}
-\item{probesets}{Data frame of probesets transcriptomic markers and corresponding cell populations. Fetched from github by a call to read.table by default, but can also be a data.frame}
-\item{genes}{Data frame of genes transcriptomic markers (HUGO symbols or ENTREZ_ID) and corresponding cell populations. Fetched from github by a call to read.table by default, but can also be a data.frame}
+\item{probesets}{Data frame of probesets transcriptomic markers and corresponding cell populations.}
+\item{genes}{Data frame of genes transcriptomic markers (HUGO symbols or ENTREZ_ID) and corresponding cell populations.}
 }
 \value{matrix with cell populations in rows and samples in columns
 }