fixed order for R 4.1

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: imsig
 Type: Package
 Title: Immune Cell Gene Signatures for Profiling the Microenvironment of Solid Tumours
-Version: 1.0.1
+Version: 1.1.0
 Author: Ajit Johnson Nirmal
 Maintainer: Ajit Johnson Nirmal <[email protected]>
 Description: Estimate the relative abundance of tissue-infiltrating immune subpopulations abundances using gene expression data. 

R/imsig.R

@@ -2,14 +2,16 @@
 #' @description Estimates the relative abundance of immune cells across patients/samples.
 #' @param exp Dataframe of transcriptomic data (natural scale) containing genes as rows and samples as columns. Note: Gene names should be set as row names and duplicates are not allowed. Missing values are not allowed within the expression matrix. Check example- head(example_data): \code{\link{example_data}}.
 #' @param r Use a value between 0 and 1. Default is 0.6. This is a user defined correlation cut-off to perform feature selection (\code{\link{feature_select}}). Feature selection aids to enrich the prediction of relative abundance of immune cells by filtering off poorly correlated ImSig genes. To get an idea of what cut-off to use check the results of (\code{\link{gene_stat}}) and choose a cut-off that displays high median correlation and maintains a high proportion of genes after feature selection.
+#' @param sort Sort the samples based on abundance of a particular cell type. `Set sort = FALSE` if you wish not to apply sorting. By default the function sorts by abundance of T cells. The cell type of interest for sorting can be controlled by the `sort_by` parameter.
+#' @param sort_by Can be used to sort the samples by predicted abundance of a particular cell type. All other cell types follow this sorting. By default it is sorted by `T cells`
 #' @return Relative abundance of immune cells across samples. Returns a dataframe.
 #' @examples
 #' cell_abundance = imsig (exp = example_data, r = 0.7)
 #' head(cell_abundance)
 #' @seealso \code{\link{feature_select}}, \code{\link{example_data}}
 #' @export
 
-imsig <- function(exp, r = 0.6){
+imsig <- function(exp, r = 0.6, sort=TRUE, sort_by='T cells'){
   exp <- pp_exp(exp)
   sig <- pp_sig(exp)
   fg <- feature_select(exp, r)
@@ -24,7 +26,7 @@ imsig <- function(exp, r = 0.6){
     colnames(e_avg) <- i
     cc <- cbind(cc, e_avg)
   }
-  cc <- cc[order(data.frame(cc$'T cells')),]
+  if (sort == TRUE){cc <- cc[sort.list(data.frame(cc[,sort_by])),]}
   return(cc)
 }
 

R/imsig_survival.R

@@ -23,7 +23,7 @@ imsig_survival <- function(exp, cli, time = 'time', status= 'status', r = 0.6){
   cell_cli <- cell_cli[,-1]
   HR <- data.frame()
   for (i in 1: length(levels(sig$cell))){
-    cell_ordered <- cell_cli[order(cell_cli[,i]),]
+    cell_ordered <- cell_cli[sort.list(cell_cli[,i]),]
     cell_ordered$group <- ifelse(cell_ordered[,i] <= median(cell_cli[,i]), "low", "high")
     cox <- coxph(Surv(time, status) ~ group, cell_ordered)
     x <- summary(cox)[8]

README.md

@@ -7,7 +7,7 @@ The basic **principle** behind *ImSig* analysis is that for a given immune cell
 ### Citation
 Immune Cell Gene Signatures for Profiling the Microenvironment of Solid Tumors <br>
 Ajit J. Nirmal, Tim Regan, Barbara B. Shih, David A. Hume, Andrew H. Sims and Tom C. Freeman <br>
-Cancer Immunol Res November 1 2018 (6) (11) 1388-1400; DOI: 10.1158/2326-6066.CIR-18-0342 <br>
+[Cancer Immunol Res](https://cancerimmunolres.aacrjournals.org/content/6/11/1388) November 1 2018 (6) (11) 1388-1400; DOI: 10.1158/2326-6066.CIR-18-0342 <br>
 
 # Requirements
 ### Mandatory
@@ -28,8 +28,10 @@ The survival metadata can be imported into R using something similar to the basi
 
 #### Install package
 
-**`install.packages("imsig")`**    
-**`library("imsig")`**
+```
+install.packages("imsig")  
+library("imsig")
+```
 
 #### To use the latest developmental version