#' Run python implementation of LIONESS
#'
#' \strong{LIONESS}(Linear Interpolation to Obtain Network Estimates for Single Samples) is a method to estimate sample-specific regulatory networks.
#' \href{https://pubmed.ncbi.nlm.nih.gov/30981959/}{[(LIONESS publication)])}.
#'
#' @param expr_file Character string indicating the file path of expression values file, with each gene(in rows) across samples(in columns).
#' @param motif_file An optional character string indicating the file path of a prior transcription factor binding motifs dataset.
#' When this argument is not provided, analysis will continue with Pearson correlation matrix.
#' @param ppi_file An optional character string indicating the file path of protein-protein interaction edge dataset.
#' Also, this can be generated with a list of proteins of interest by \code{\link{sourcePPI}}.
#'
#' @param computing 'cpu' uses Central Processing Unit (CPU) to run PANDA; 'gpu' use the Graphical Processing Unit (GPU) to run PANDA. The default value is "cpu".
#'
#' @param precision 'double' computes the regulatory network in double precision (15 decimal digits); 'single' computes the regulatory network in single precision (7 decimal digits) which is fastaer, requires half the memory but less accurate. The default value is 'double'.
#' @param save_tmp 'TRUE' saves middle data like expression matrix and normalized networks; 'FALSE' deletes the middle data. The default value is 'TURE'.
#' @param modeProcess 'legacy' refers to the processing mode in netZooPy<=0.5, 'union': takes the union of all TFs and genes across priors and fills the missing genes in the priors with zeros; 'intersection': intersects the input genes and TFs across priors and removes the missing TFs/genes. Default values is 'union'.
#' @param remove_missing Only when modeProcess='legacy': remove_missing='TRUE' removes all unmatched TF and genes; remove_missing='FALSE' keeps all tf and genes. The default value is FALSE.
#'
#' @param start_sample Numeric indicating the start sample number, The default value is 1.
#' @param end_sample Numeric indicating the end sample number. The default value is 'None' meaning no end sample, i.e. print out all samples.
#' @param save_single_network Boolean vector, "TRUE" wirtes out the single network in npy/txt/mat formats, directory and format are specifics by params "save_dir" and "save_fmt". The default value is 'FALSE'
#' @param save_dir Character string indicating the folder name of output lioness networks for each sample by defined.
#' The default is a folder named "lioness_output" under current working directory. This paramter is valid only when save_single_network = TRUE.
#' @param save_fmt Character string indicating the format of lioness network of each sample. The dafault is "npy". The option is txt, npy, or mat. This paramter is valid only when save_single_network = TRUE.
#'
#' @return A data frame with columns representing each sample, rows representing the regulator-target pair in PANDA network generated by \code{\link{pandaPy}}.
#' Each cell filled with the related score, representing the estimated contribution of a sample to the aggregate network.
#'
#' @examples
#'
#' # refer to the input datasets files of control in inst/extdat as example
#' control_expression_file_path <- system.file("extdata", "expr10_reduced.txt", package = "netZooR",
#' mustWork = TRUE)
#' motif_file_path <- system.file("extdata", "chip_reduced.txt", package = "netZooR", mustWork = TRUE)
#' ppi_file_path <- system.file("extdata", "ppi_reduced.txt", package = "netZooR", mustWork = TRUE)
#'
#' # Run LIONESS algorithm
#' \donttest{
#' control_lioness_result <- lionessPy(expr_file = control_expression_file_path,
#' motif_file = motif_file_path, ppi_file = ppi_file_path,
#' modeProcess="union",start_sample=1, end_sample=1, precision="single")
#' }
#' @import reticulate
#' @export
lionessPy <- function(expr_file, motif_file=NULL, ppi_file=NULL, computing="cpu", precision="double", save_tmp=TRUE, modeProcess="union", remove_missing=FALSE, start_sample=1, end_sample="None", save_single_network=FALSE, save_dir="lioness_output", save_fmt='npy'){
if(missing(expr_file)){
stop("Please provide the gene expression value with option e, e.g. e=\"expression.txt\"")
}else{
expr.str <- paste("\'", expr_file, "\'", sep = '')
}
if(is.null(motif_file)){
motif.str <- 'None'
message("Prior motif network is not provided, analysis continues with Pearson correlation matrix.")
}else{
motif.str <- paste("\'", motif_file,"\'", sep = '')
}
if(is.null(ppi_file)){
ppi.str <- 'None'
message("No PPI provided.") }
else{ ppi.str <- paste("\'", ppi_file, "\'", sep = '') }
# computing variable
if(computing=="gpu"){
computing.str <- "computing='gpu'"
} else{ computing.str <- "computing='cpu'"}
# precision variable
if(precision == "single" ){
precision.str <- "precision='single'"
} else{ precision.str <- "precision='double'"}
# save_memory variable always is "save_memory=False"
savememory.str <- "save_memory=False"
# save_tmp variable
if(save_tmp==FALSE){
savetmp.str <- "save_tmp= False"
} else{ savetmp.str <- "save_tmp=True" }
# when pre-processing mode is legacy
if(modeProcess == "legacy"){
if(remove_missing == TRUE){
message("Use the legacy mode to pre-process the input dataset and keep only the matched TFs or Genes")
mode.str <- "modeProcess ='legacy', remove_missing = True"
} else{ message("Use the legacy mode (netZooPy version <= 0.5) to pre-process the input dataset and keep all the unmatched TFs or Genes")
mode.str <- "modeProcess ='legacy', remove_missing = False"}
}
# when pre-processing mode is union
else if(modeProcess == "union"){
mode.str <- "modeProcess ='union'"
}
else if(modeProcess == "intersection"){
mode.str <- "modeProcess ='intersection'"
}
# source the pandaPy and lionessPy from GitHub raw website.
pandapath <- system.file("extdata", "panda.py", package = "netZooR", mustWork = TRUE)
lionesspath <- system.file("extdata", "lioness.py", package = "netZooR", mustWork = TRUE)
reticulate::source_python(pandapath,convert = TRUE)
reticulate::source_python(lionesspath,convert = TRUE)
# run py code to create an instance named "panda_ob" of Panda Class
pandaObj.str <- paste("panda_obj=Panda(", expr.str, ",", motif.str,",", ppi.str, ",", computing.str, ",", precision.str, ",", savememory.str, ",", savetmp.str, "," , "keep_expression_matrix=True", ",", mode.str, ")", sep ='')
py_run_string(pandaObj.str,local = FALSE, convert = TRUE)
# assign "panda_network" with the output PANDA network
py_run_string("panda_network=panda_obj.export_panda_results",local = FALSE, convert = TRUE)
panda_net <- py$panda_network
# re-assign data type of cloumn.
panda_net$tf <- as.character(panda_net$tf)
panda_net$gene <- as.character(panda_net$gene)
# rename first two columns
names(panda_net)[names(panda_net) == "tf"] <- "TF"
names(panda_net)[names(panda_net) == "gene"] <- "Gene"
if( length(intersect(panda_net$Gene, panda_net$TF))>0){
panda_net$TF <- paste('reg_', panda_net$TF, sep='')
panda_net$Gene <- paste('tar_', panda_net$Gene, sep='')
message("Rename the content of first two columns with prefix 'reg_' and 'tar_' as there are some duplicate node names between the first two columns" )
}
# create an instance named "lioness_obj" of Lioness Class.
# when save_single_network = TRUE
if(save_single_network==TRUE){
py_run_string(paste("lioness_obj = Lioness(panda_obj,computing='", computing, "' , ","start=", start_sample," , ", "end=" ,end_sample, " , ", "save_dir='", save_dir, "' , " , "save_fmt='" , save_fmt, "' , " , "save_single = True )",sep = "" ))
}else if(save_single_network==FALSE){
py_run_string(paste("lioness_obj = Lioness(panda_obj,computing='", computing, "' , ","start=", start_sample," , ", "end=" ,end_sample, " , ", "save_single = False )",sep = "" ))
}
# retrieve the "total_lioness_network" attribute of instance "lionesss_obj"
py_run_string("lioness_network = lioness_obj.export_lioness_results",local = FALSE, convert = TRUE)
# convert the python variable "lionesss_network" to a data.frame in R environment.
lioness_net <- py$lioness_network
# cbind the first two columns of PANDA output with LIONESS output.
lioness_net$tf <- panda_net$TF
lioness_net$gene <- panda_net$Gene
lioness_output <- lioness_net
return(lioness_output)
}
#' Compute LIONESS (Linear Interpolation to Obtain Network Estimates for Single Samples)
#'
#'
#' @param motif A motif dataset, a data.frame, matrix or exprSet containing 3 columns.
#' Each row describes an motif associated with a transcription factor (column 1) a
#' gene (column 2) and a score (column 3) for the motif.
#' @param expr A mandatory expression dataset, as a genes (rows) by samples (columns) data.frame
#' @param ppi A Protein-Protein interaction dataset, a data.frame containing 3 columns.
#' Each row describes a protein-protein interaction between transcription factor 1(column 1),
#' transcription factor 2 (column 2) and a score (column 3) for the interaction.
#' @param network.inference.method String specifying choice of network inference method. Default is "panda".
#' Options include "pearson".
#' @param ncores int specifying the number of cores to be used. Default is 1.
#' (Note: constructing panda networks can be memory-intensive, and the number of cores should take into consideration available memory.)
#' @param ... additional arguments for panda analysis
#' @keywords keywords
#' @importFrom matrixStats rowSds
#' @importFrom matrixStats colSds
#' @importFrom Biobase assayData
#' @importFrom reshape melt.array
#' @importFrom pandaR panda
#' @importFrom parallel mclapply
#' @export
#' @return A list of length N, containing objects of class "panda"
#' corresponding to each of the N samples in the expression data set.\cr
#' "regNet" is the regulatory network\cr
#' "coregNet" is the coregulatory network\cr
#' "coopNet" is the cooperative network
#' @examples
#' data(pandaToyData)
#' lionessRes <- lioness(expr = pandaToyData$expression[,1:3], motif = pandaToyData$motif, ppi = pandaToyData$ppi,hamming=1,progress=FALSE)
#' @references
#' Kuijjer, M.L., Tung, M., Yuan, G., Quackenbush, J. and Glass, K., 2015.
#' Estimating sample-specific regulatory networks. arXiv preprint arXiv:1505.06440.
#' Kuijjer, M.L., Hsieh, PH., Quackenbush, J. et al. lionessR: single sample network inference in R. BMC Cancer 19, 1003 (2019). https://doi.org/10.1186/s12885-019-6235-7
lioness = function(expr, motif = NULL, ppi = NULL, network.inference.method = "panda", ncores = 1, ...){
N <- ncol(expr)
if(ncores < 1){
print('Setting number of cores to 1.')
ncores <- 1
}
if(network.inference.method == "panda")
{
fullnet <- panda(motif, expr, ppi, ...)
return(mclapply(seq_len(N), function(i) {
print(paste("Computing network for sample ", i))
N * fullnet@regNet - (N - 1) * panda(motif, expr[, -i],
ppi, ...)@regNet
}, mc.cores = ncores))
}
if(network.inference.method == "pearson")
{
fullnet <- cor(t(expr))
return(mclapply(seq_len(N), function(i) {
print(paste("Computing network for sample ", i))
N * fullnet - (N - 1) * cor(t(expr[,-i]))
}, mc.cores = ncores))
}
if(!(network.inference.method %in% c("panda","pearson")))
{
stop(paste(c("The method",network.inference.method,"is not yet supported."),collapse=" "))
geterrmessage()
}
}
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