R/predict_PCA_graph.R

In graeberlab/small.cell.project: small.cell.project

#' Predict all files in a folder using PCA of a training dataset, file names have to be of form name_.......txt
#'
#' @param prediction_file file  with training data features rows , genes columns, features are in column 1
#' @param test_files_folder folder of test files
#' @param anno.file Annotation file, first column is sample names, 2nd is annotation
#' @param comps number of components to compute
#' @param output_folder output folder
#' @param train_string string of training data to insert in file name of predicted scores
#' @param test_string string of data being tested to insert in file name of predicted scores
#' @param rotate do you want to rotate +/- signs, default=F.
#' @param varimax.it do you want to perform varimax , default varimax=F
#' @param varimax.comp # of varimax comps , has to match # of comps
#' @param shape.palette vector with numbers reprsenting shapes according to R plotting
#' 
#' @importFrom utils read.delim 
#' 
#' @export
 predict_PCA_graph=function(prediction_file,test_files_folder,anno.file,output_folder="./",train_string="",test_pattern="",comps=2,rotate=F,varimax=F,varimax.comp=2,shape.palette=NULL) {
   all.files.short.path=list.files(test_files_folder,pattern=test_pattern,full.names=F)
   all.files.long.path=list.files(test_files_folder,pattern=test_pattern,full.names=T)
   all.names=as.character(sapply(all.files.short.path, function(x) strsplit(x,"_")[[1]][1]))
   human.info=read.delim(anno.file)
   for(i in 1:length(all.names)) {
     nam=all.names[i]
     PCA_from_file_and_predict_second_dataset(prediction_file,
                                               all.files.long.path[i],
                                               sample.names= human.info$sample, sample.type = factor(human.info$type),
                                               sample.names2 = human.info$sample,sample.type2 =  factor(human.info$type), test_string = nam,
                                               "Projection", comps = comps, scale = F, labels =F,output_folder=output_folder,train_string=train_string,TCGA=T,rotate=rotate,varimax=varimax,varimax.comp=varimax.comp,
                                              shape.palette = shape.palette)
     print(paste0("Cancer ",nam," is done!"))
   }
 }