trainingBLAST.R

#!/usr/bin/env Rscript

# Copyright 2018 Xiyao Long <xlong2@jhu.edu>
#  MIT license
#  Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files, to deal 
#  in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
#  the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
#  The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
#  THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS 
# FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.




#load functions
file.sources = list.files(pattern="functions.R")
sapply(file.sources,source,.GlobalEnv)
file.sources = list.files(pattern="*function.R")
sapply(file.sources,source,.GlobalEnv)



# Install required R packages if they do not exist
list.of.packages <- c("Matrix", "grid","caret","RWebLogo","MLmetrics","parallel","pryr","protr",
                      "gbm","ggplot2","ggrepel","reshape2","gridExtra","doMC","RColorBrewer",
                      "scales","e1071","argparse")
local_package_dir="~/R_libs/"
specify_R_package_diretory=FALSE
install_and_load_packages(list.of.packages,local_package_dir,specify_R_package_diretory)


suppressPackageStartupMessages(library("argparse"))

# create parser object
parser <- ArgumentParser()

initial_message="A script for performing BLAST template searching and calculate the accuracy in terms whether the class of the query is the same as the class of template. The prediction accuracy evaluation is performed in x repeats n folds scheme so that the results can be used to compare to the results of the GBM methods"




# create parser object
parser <- ArgumentParser()

initial_message="A program for performing grid search for  Gradient Boosted Model training in x repeats n folds scheme and getting the best parameter;   The implementation features parallelization for not only different models but for different data category so that better parallelization  for datasets of different data sizes are possibe. The training process is also implemented with default sparse class upsampling method to balance data with imbalanced classes"



# by default ArgumentParser will add an help option
parser$add_argument("-d", "--data ",type="character", default="data_table.tsv",  help="the data file")

parser$add_argument("-o", "--output_dir", type="character",metavar="data_dir",default="Data_dir", help="Directory to place all intermediate data files and results ")
parser$add_argument("-p","--plot_dir",help="directory to place generated plots", default="Plot_dir")
parser$add_argument( "-re", "--n_repeats",help=" the number of repeats for performing the blindBLAST prediction ", default=3, type="integer")
parser$add_argument( "-fold", "--n_folds",help=" the number of repeats for performing the blindBLAST prediction ", default=10, type ="integer" )
parser$add_argument("-c", "--n_cores",help=" number of cores available for computing",default=1,type="integer")


# get command line options, if help option encountered print help and exit,
# otherwise if options not found on command line then set defaults,
args <- parser$parse_args()






cur_d=getwd()
prefix=strsplit(cur_d,"\\/")[[1]][length(strsplit(cur_d,"\\/")[[1]])]
data(AAPAM30)
AAPAM30_index=1:20
names(AAPAM30_index)=rownames(AAPAM30)




# parameters passed to Rscript for gbm model training
total_max_core=args$n_cores

n_repeats=args$n_repeats
n_folds=args$n_folds
plot_dir=args$plot_dir
output_dir=args$output_dir
command_mk=paste(c("mkdir", output_dir),collapse=" ")
system(command_mk)

data_file=args$data
g(the_method,each_method,cluster_dis,result_dir,subsitution_matrix,subsitution_matrix_name,plot_dir,file)%=% specify_variable_names(prefix, output_dir,  plot_dir ,data_file)

data_by_loop_type_list_unduplicated=read_in_data(data_file)
system("mkdir ./tmp")
save_file_sp("data_by_loop_type_list_unduplicated",result_dir,data_file)
data_save_f=paste(c(result_dir,data_file,".rds"),collapse="")

system(paste(c("mkdir","./blast/"),collapse=" "))

each_method = "blindblast_just_get_alignment"
cluster_dis = "north"
subsitution_matrix_name = "wahtw"  #"/Volumes/lab/macbook/lab_work_data/vall_rmsd/loop_sub_matrix.csv"
subsitution_matrix = "PAM30"


# do blindBLAST template searching for each CDR loop 
overall_accuracy = list()
ten_foldcv_blindblastlist = list()
the_loop_names=names(data_by_loop_type_list_unduplicated)
left_loops=the_loop_names[!the_loop_names%in% names(ten_foldcv_blindblastlist) ]
for (loop_type in left_loops) {

  sequences=data_by_loop_type_list_unduplicated[[loop_type]][[1]]
  each_loop_length_data_feature_string = data_by_loop_type_list_unduplicated[[loop_type]][[2]]
  features=data_by_loop_type_list_unduplicated[[loop_type]][[4]]
  sequences$cluster_type = as.character(sequences$cluster_type)
  all_cases =  sequences[, c(features, "cluster_type", "identifier", "id")]
  #make folds and separates folds in and folds out
  r <- n_repeats # number of repeats
  k <- n_folds # number of folds
  g(folds.list,folds.list.out)  %=%generate_folds_foldsout(sequences,r,k)
  
  all_result_list = mclapply(1:length(folds.list), get_accuracy_per_fold_overload_final, mc.cores =  total_max_core  )  # run blindBLAST for each folds out 
  acc_result=calculate_accuracy_mean_std(all_result_list)
  overall_accuracy[[loop_type]] = c(acc_result[[1]], acc_result[[2]])
  ten_foldcv_blindblastlist[[loop_type]] = all_result_list
  
}# end of iterating all loop types



save_file("ten_foldcv_blindblastlist")
ten_foldcv_blindblastlist=readRDS(paste(c(result_dir,'/ten_foldcv_blindblastlist.rds'),collapse=""))



results_rbind_allloops=lapply(ten_foldcv_blindblastlist,function(each_r){
  if(n_repeats>1){
  result_l=chunk2(1:length(each_r),n_repeats)
  }else{
    result_l=list()
    result_l[[1]]=1:length(each_r)
  }
  #  for(each_r in ten_foldcv_blindblastlist){
  results=lapply(result_l,function(x){
    fr=do.call(rbind,each_r[x])
    fr$obs=split_vector_and_replace(fr[,1],"\\.",1,1,"")
    fr$pred=split_vector_and_replace(fr[,2],"\\.",1,1,"")
    #dim(fr[fr$obs==fr$pred,])[1]/dim(fr)[1]
    repeat_number=x[1]%/%10+1
    print(dim(fr)[1])
    print(each_r)
    fr$repeats=rep(repeat_number,dim(fr)[1])
    return(fr)
  })
  results_rbind=do.call(rbind,results)
  return(results_rbind)
  #}
  
  
})

# calculate accuracies for blindBLAST for each repeat
# calculate the std for accuracies for each repeat
blindBLAST_summ=cal_blindBLAST_accuracy(ten_foldcv_blindblastlist,n_folds)

save_file("blindBLAST_summ")
colnames(blindBLAST_summ)=c("accuracy","accuracy_std")
print(blindBLAST_summ)