R/deconvo_tme.R

In IOBR/IOBR: Immune Oncology Biological Research

#' Collection of tumor microenvironment cell fraction deconvolution methods.
#'
#' The methods currently supported are
#' `mcpcounter`, `epic`, `xcell`, `cibersort`, `cibersort_abs`, `ips`, `estimate`, `svr`,`lsei`,`timer`,`quantiseq`
#'
#' The object is a named vector. The names correspond to the display name of the method,
#' the values to the internal name.
#'
#' @export
tme_deconvolution_methods = c("MCPcounter"="mcpcounter",
                              "EPIC"="epic",
                              "xCell"="xcell",
                              "CIBERSORT"="cibersort",
                              "CIBERSORT Absolute"="cibersort_abs",
                              "IPS" = "ips",
                              "ESTIMATE" = "estimate",
                              "SVR" = "svr",
                              "lsei" = "lsei",
                              "TIMER" = "timer",
                              "quanTIseq" = "quantiseq")
############################################




#' Decoding immune microenvironment using xCell
#'
#' @param eset expression set with genes at row, sample ID at column
#' @param project project name used to distinguish different data sets
#' @param array transcrptomic data type
#' @return xCell with immune cell fractions
#' @export
#' @importFrom tibble rownames_to_column
#' @author Dongqiang Zeng
#' @examples
#' # Loading TCGA-STAD expresion data(raw count matrix)
#' data(eset_stad, package = "IOBR")
#' eset <- count2tpm(countMat = eset_stad, source = "local", idType = "ensembl")
#' xcell_result<-deconvo_xcell(eset = eset, project = "TCGA-STAD", arrays = FALSE)
#'
deconvo_xcell<-function(eset, project = NULL, arrays = FALSE){

  message(paste0("\n", ">>> Running ", "xCell"))
  #normalize gene expression matrix
  # if(max(eset)>100) eset<-log2(eset)
  # data("xCell.data")
  rnaseq = !arrays
  res<- xCellAnalysis(eset,rnaseq = rnaseq)
  res<-as.data.frame(t(res))
  ###########################################
  colnames(res)<-gsub(colnames(res),pattern = "\\ ",replacement = "\\_")
  colnames(res)<-gsub(colnames(res),pattern = "\\ ",replacement = "\\_")
  colnames(res)<-paste0(colnames(res),"_xCell")

  if(!is.null(project)){
    res$ProjectID<-project
    res<-res[,c(ncol(res),1:ncol(res)-1)]
  }
  res<-tibble::rownames_to_column(res,var = "ID")
  return(res)
}



#' Estimating immune microenvironment using MCP-counter
#'
#' @param eset expression set with genes at row, sample ID at column
#' @param project project name used to distinguish different data sets
#' @return MCPcounter with immune cell fractions
#' @export
#' @importFrom tibble rownames_to_column
#' @author Dongqiang Zeng
#' @examples
#' # Loading TCGA-STAD expresion data(raw count matrix)
#' data(eset_stad, package = "IOBR")
#' eset <- count2tpm(countMat = eset_stad, source = "local", idType = "ensembl")
#' mcp_result<-deconvo_mcpcounter(eset = eset, project = "TCGA-STAD")
#'
deconvo_mcpcounter<-function(eset,project = NULL){

  message(paste0("\n", ">>> Running ", "MCP-counter"))
  #normalize gene expression matrix
  # if(max(eset)>100) eset<-log2(eset)

  res<-MCPcounter.estimate(eset,
                           featuresType = "HUGO_symbols",
                           probesets= mcp_probesets,
                           genes= mcp_genes)
  res<-as.data.frame(t(res))
  ####################################
  colnames(res)<-gsub(colnames(res),pattern = "\\.",replacement = "\\_")
  colnames(res)<-gsub(colnames(res),pattern = "\\ ",replacement = "\\_")
  colnames(res)<-paste(colnames(res),"_MCPcounter",sep = "")

  if(!is.null(project)){
    res$ProjectID<-project
    res<-res[,c(ncol(res),1:ncol(res)-1)]
  }

  res<-tibble::rownames_to_column(res,var = "ID")
  return(res)
  ###################################
}





#' Estimating immune microenvironment using EPIC: FOR RNAseq mostly
#'
#' @param eset expression set with genes at row, sample ID at column
#' @param project project name used to distinguish different data sets
#' @param tumor is input sample tumor or normal
#' @return EPIC with immune cell fractions
#' @export
#' @importFrom tibble rownames_to_column
#' @author Dongqiang Zeng
#' @examples
#' # Loading TCGA-STAD expresion data(raw count matrix)
#' data(eset_stad, package = "IOBR")
#' eset <- count2tpm(countMat = eset_stad, source = "local", idType = "ensembl")
#' epic_result<-deconvo_epic(eset = eset,project = "TCGA-STAD",tumor = TRUE)
#'
deconvo_epic<-function(eset,project = NULL,tumor){

  message(paste0("\n", ">>> Running ", "EPIC"))

  # mRNA_cell = NULL
  # if(!scale_eset) mRNA_cell = c("default"=1.)
  ###################################

  ref = ifelse(tumor, "TRef", "BRef")
  ##############################
  out <- IOBR::EPIC(bulk = eset,reference = ref,mRNA_cell = NULL, scaleExprs =TRUE)
  res<-as.data.frame((out$cellFractions))
  ####################################
  colnames(res)<-gsub(colnames(res),pattern = "\\.",replacement = "\\_")
  colnames(res)<-gsub(colnames(res),pattern = "\\ ",replacement = "\\_")
  colnames(res)<-paste(colnames(res),"_EPIC",sep = "")
  res<-as.data.frame(res)

  if(!is.null(project)){
    res$ProjectID<-project
    res<-res[,c(ncol(res),1:ncol(res)-1)]
  }
  res<-tibble::rownames_to_column(res,var = "ID")
  return(res)
  ###################################
}





#' Decoding immune microenvironment using CIBERSORT
#'
#' CIBERSORT is only freely available to academic users.
#' A license an the binary can be obtained from https://cibersort.stanford.edu.
#'
#' @param eset expression set with gene symbol at row name, sample ID at column
#' @param project project name used to distinguish different data sets
#' @param arrays logical: Runs methods in a mode optimized for microarray data.
#' @param absolute logical: Runs CIBERSORT in absolute mode
#' @param perm permutation to run CIBERSORT
#' @return cibersrot with immune cell fractions
#' @author Dongqiang Zeng
#' @export
#'
#' @examples
#' # Loading TCGA-STAD expresion data(raw count matrix)
#' data(eset_stad, package = "IOBR")
#' eset <- count2tpm(countMat = eset_stad, source = "local", idType = "ensembl")
#' cibersort_result<-deconvo_cibersort(eset = eset,project = "TCGA-STAD", arrays = FALSE, absolute = FALSE, perm = 500)

deconvo_cibersort<-function(eset, project = NULL, arrays,  perm = 1000, absolute = FALSE, abs_method = "sig.score"){

  if(absolute){
    message(paste0("\n", ">>> Running ", "CIBERSORT in absolute mode"))
  }else{
    message(paste0("\n", ">>> Running ", "CIBERSORT"))
  }

  eset<-as.data.frame(eset)
  ##############################

  # the authors reccomend to disable quantile normalizeation for RNA seq.
  # (see CIBERSORT website).
  quantile_norm = arrays
  ##############################
  res<-CIBERSORT(sig_matrix = lm22,
                 mixture_file = eset,
                 perm = perm,
                 QN = quantile_norm,
                 absolute = absolute,
                 abs_method = abs_method)
  ###############################
  colnames(res)<-gsub(colnames(res),pattern = "\\.",replacement = "\\_")
  colnames(res)<-gsub(colnames(res),pattern = "\\ ",replacement = "\\_")
  colnames(res)<-paste(colnames(res),"_CIBERSORT",sep = "")
  res<-as.data.frame(res)


  if(!is.null(project)){
    res$ProjectID<-project
    res<-res[,c(ncol(res),1:ncol(res)-1)]
  }

  res<-tibble::rownames_to_column(res,var = "ID")
  return(res)
  ###################################
}




#' Calculating immune phenotype score using IPS
#'
#' @param eset expression set with genes at row, sample ID at column
#' @param project project name used to distinguish different data sets
#' @return IPS data frame
#' @export
#' @import ggplot2
#' @import cowplot
#' @import grid
#' @author Dongqiang Zeng
#' @examples
#' # Loading TCGA-STAD expresion data(raw count matrix)
#' data(eset_stad, package = "IOBR")
#' eset <- count2tpm(countMat = eset_stad, source = "local", idType = "ensembl")
#' ips_result<-deconvo_ips(eset = eset, project = "TCGA-STAD")
#'
deconvo_ips<-function(eset, project = NULL,plot){

  message(paste0("\n", ">>> Running ", "Immunophenoscore"))
  #normalize gene expression matrix
  # if(max(eset)>100) eset<-log2(eset+1)
  ##############################
  res<-IPS_calculation(project = project,eset,plot)
  ####################################
  # colnames(res)<-gsub(colnames(res),pattern = "\\.",replacement = "\\_")
  colnames(res)<-paste(colnames(res),"_IPS",sep = "");colnames(res)
  res<-as.data.frame(res)

  if(!is.null(project)){
    res$ProjectID<-project
    res<-res[,c(ncol(res),1:ncol(res)-1)]
  }

  res<-tibble::rownames_to_column(res,var = "ID")
  return(res)
}

###########################################


#' Calculation of stromal, immune, and ESTIMATE scores
#'
#' @param eset expression set with genes at row, sample ID at column
#' @param project project name used to distinguish different data sets
#' @param platform character string indicating platform type. Defaults to "affymetrix"
#' @importFrom tibble rownames_to_column
#' @author Dongqiang Zeng
#' @return
#' @export
#'
#' @examples
#' # Loading TCGA-STAD expresion data(raw count matrix)
#' data(eset_stad, package = "IOBR")
#' eset <- count2tpm(countMat = eset_stad, source = "local", idType = "ensembl")
#' deconvo_estimate(eset)
deconvo_estimate<-function(eset, project = NULL,platform = "affymetrix"){

  message(paste0("\n", ">>> Running ", "ESTIMATE"))
  eset<-as.data.frame(eset)
  eset<-tibble::rownames_to_column(eset,var = "symbol")
  sampleData<-paste0(project,"-eset.txt")
  write.table(eset,sampleData,sep = "\t",row.names = F,quote = F)
  ########################################
  filterCommonGenes(input.f= sampleData,
                    output.f= paste0(project,"_Tumor_purity.gct"),
                    id="GeneSymbol")
  #delete-data-after-input
  file.remove(paste0(project,"-eset.txt"))
  ################################
  estimateScore(input.ds = paste0(project,"_Tumor_purity.gct"),
                output.ds= paste0(project,"_Tumor_estimate_score.gct"),
                platform= platform)
  file.remove(paste0(project,"_Tumor_purity.gct"))
  #################################
  scores=read.table(paste0(project,"_Tumor_estimate_score.gct"),skip = 2,header = T)
  file.remove(paste0(project,"_Tumor_estimate_score.gct"))
  ################################
  rownames(scores)=scores[,1]
  scores=t(scores[,3:ncol(scores)])
  colnames(scores)<-paste0(colnames(scores),"_estimate")

  if(!is.null(project)){
    scores$ProjectID<-project
    scores<-scores[,c(ncol(scores),1:ncol(scores)-1)]
  }

  scores<-tibble::rownames_to_column(as.data.frame(scores),var = "ID")
  scores$ID<-gsub(scores$ID,pattern = "\\.",replacement = "-")

  return(scores)
}



#' Estimate the fraction of cell types using defined reference genes
#'
#' @param eset expression data with matched gene id of reference
#' @param project project name used to distinguish different datasets
#' @param arrays a logical value. If TRUE, the columns of the input data will be normalized to have the same quantiles.
#' @param method deconvolution method. must be "svr" or "lsei"
#' @param perm  permutation to run svr
#' @param reference immune cell gene matrix; eg lm22, lm6 or can be generate using generateRef/generateRef_rnaseq
#' @param absolute.mode absolute.mode, default is FALSE
#' @param abs.method abs.method, default is sig.score
#' @param scale_reference  a logical value indicating whether the reference be scaled or not. If TRUE, the value in reference file will be centered and scaled in row direction.
#'
#' @author Dongqiang Zeng
#' @author Rongfang Shen
#' @return
#' @export
#'
#' @examples
#' # Loading TCGA-STAD expresion data(raw count matrix)
#' data(eset_stad, package = "IOBR")
#' eset <- count2tpm(countMat = eset_stad, source = "local", idType = "ensembl")
#' deconvo_ref(eset = eset, reference = lm22)
deconvo_ref<-function(eset, project = NULL, arrays = TRUE, method = "svr", perm = 100,
                      reference, scale_reference = TRUE, absolute.mode = FALSE, abs.method = "sig.score"){

  if (length(intersect(rownames(eset), rownames(reference))) == 0){
    stop("None identical gene between eset and reference had been found.
         Check your eset using: intersect(rownames(eset), rownames(reference))")
  }
  # recomend to disable quantile normalization for RNA seq.
  quantile_norm = arrays
  ##############################

  if(method=="svr"){
    message(paste0("\n", ">>> Running ", "cell estimation in SVR mode"))

   if(absolute.mode) message(paste0("\n", ">>> Running ", "SVR in absolute mode"))

   eset<-as.data.frame(eset)
   # the authors recomend to disable quantile normalization for RNA seq.
   # (see CIBERSORT website).
   quantile_norm = arrays
   ##############################
   res<-CIBERSORT(sig_matrix = reference,
                  mixture_file = eset,
                  perm = perm,
                  QN = quantile_norm,
                  absolute = absolute.mode,
                  abs_method = abs.method)
   ###############################

  }else if(method=="lsei"){

    message(paste0("\n", ">>> Running ", "cell estimation in lsei mode"))
    eset <- as.matrix(eset)
    reference <- as.matrix(reference)
    # quantile normalized
    if (arrays){
      roweset <- rownames(eset)
      coleset <- colnames(eset)
      eset <- normalize.quantiles(eset)
      rownames(eset) <- roweset
      colnames(eset) <- coleset
    }
    # scale_reference
    if (scale_reference){
      reference <- (reference - mean(reference))/sd(as.vector(reference))
    }
    Ymedian <- max(median(eset),1)
    # common eset
    common <- intersect(rownames(eset), rownames(reference))
    eset <- eset[match(common, rownames(eset)), ]
    reference <- reference[match(common, rownames(reference)), ]
    # deconvolution
    output <- matrix()
    # message(paste0("\n", ">>> Running ", "cell estimation in lsei mode"))
    Numofx <- ncol(reference)
    AA <- reference
    EE <- rep(1, Numofx)
    FF <- 1
    GG <- diag(nrow=Numofx)
    HH <- rep(0, Numofx)
    out.all <- c()
    itor <- 1
    samples <- ncol(eset)
    while (itor <= samples){
        BB <- eset[, itor]
        BB <- (BB - mean(BB))/sd(BB)
        out <- lsei(AA, BB, EE, FF, GG, HH)
        out.all <- rbind(out.all, out$X)
        itor <- itor + 1
      }
    rownames(out.all) <- colnames(eset)
    res <- out.all
  }

  ###############################
  colnames(res)<-gsub(colnames(res),pattern = "\\.",replacement = "\\_")
  colnames(res)<-gsub(colnames(res),pattern = "\\ ",replacement = "\\_")
  colnames(res)<-paste0(colnames(res),"_CIBERSORT")
  res<-as.data.frame(res)

  if(!is.null(project)){
    res$ProjectID<-project
    res<-res[,c(ncol(res),1:ncol(res)-1)]
  }

  res<-tibble::rownames_to_column(res,var = "ID")
  return(res)

}


#' Deconvoluting using the TIMER technique
#' Unlike the other methods, TIMER needs the specification of the cancer type for each sample.
#'
#' @param eset  gene matrix
#' @param project default is NULL, project name used to distinguish different data sets
#' @param indications a n-vector giving and indication string (e.g. 'brca') for each sample. Accepted indications are 'kich', 'blca', 'brca', 'cesc', 'gbm', 'hnsc', 'kirp', 'lgg','lihc', 'luad', 'lusc', 'prad', 'sarc', 'pcpg', 'paad', 'tgct','ucec', 'ov', 'skcm', 'dlbc', 'kirc', 'acc', 'meso', 'thca','uvm', 'ucs', 'thym', 'esca', 'stad', 'read', 'coad', 'chol'
#'
#' @return
#' @export
#'
#' @examples
#' #' # Loading TCGA-STAD expresion data(raw count matrix)
#' data(eset_stad, package = "IOBR")
#' eset <- count2tpm(countMat = eset_stad, source = "local", idType = "ensembl")
#' deconvo_timer(eset = eset, project = "stad")
deconvo_timer = function(eset,project = NULL,indications = NULL) {

  indications = tolower(indications)
  checkmate:: assert("indications fit to mixture matrix", length(indications) == ncol(eset))
  args = new.env()
  args$outdir = tempdir()
  args$batch = tempfile()
  lapply(unique(indications), function(ind) {
    tmp_file = tempfile()
    tmp_mat = eset[, indications == ind, drop=FALSE] %>% as_tibble(rownames = "gene_symbol")
    readr:: write_tsv(tmp_mat, tmp_file)
    cat(paste0(tmp_file, ",", ind, "\n"), file=args$batch, append=TRUE)
  })
  # reorder results to be consistent with input matrix
  results <- deconvolute_timer.default(args)[, make.names(colnames(eset))]


  colnames(results) <- colnames(eset)
  results<-as.data.frame(t(results))
  colnames(results)<-paste(colnames(results),"_TIMER",sep = "")
  colnames(results)<-gsub(colnames(results),pattern = "\\.",replacement = "\\_")
  colnames(results)<-gsub(colnames(results),pattern = "\\ ",replacement = "\\_")

  if(!is.null(project)){
    results$project<-project
    results<-results[,c(ncol(results),1:ncol(results)-1)]
  }

  results<-tibble::rownames_to_column(results,var = "ID")

  return(results)
}



#' Deconvoluting micrornvironment using the quanTIseq technique
#'
#' @param eset  gene expression data
#' @param tumor logistic variable
#' @param arrays logistic variable, is data generated from microarray
#' @param scale_mrna logistic variable
#' @param project default is NULL, project name used to distinguish different data sets
#'
#' @return
#' @export
#'
#' @examples
#' #' # Loading TCGA-STAD expresion data(raw count matrix)
#' data(eset_stad, package = "IOBR")
#' eset <- count2tpm(countMat = eset_stad, source = "local", idType = "ensembl")
#' deconvo_quantiseq(eset = eset, project = "stad", tumor = TRUE, arrays = FALSE, scale_mrna = FALSE)
deconvo_quantiseq = function(eset, project = NULL, tumor, arrays, scale_mrna) {


  res = deconvolute_quantiseq.default(mix.mat = eset, tumor=tumor, arrays=arrays, mRNAscale = scale_mrna)

  res<-as.data.frame(res)
  rownames(res)<-NULL
  res<-tibble::column_to_rownames(res,var = "Sample")

  colnames(res)<-paste(colnames(res),"_quantiseq",sep = "")

  colnames(res)<-gsub(colnames(res),pattern = "\\.",replacement = "\\_")
  colnames(res)<-gsub(colnames(res),pattern = "\\ ",replacement = "\\_")

  if(!is.null(project)){
    res$ProjectID<-project
    res<-res[,c(ncol(res),1:ncol(res)-1)]
  }

  res<-tibble::rownames_to_column(res,var = "ID")

  return(res)
}



#' Deconvoluting Tumor microenvironment on a transcriptomic dataset
#'
#' @param eset A gene expression matrix
#'   Either: A numeric matrix or data.frame with HGNC gene symbols as row names and sample identifiers as column names. In both cases.
#' @param project project name used to distinguish different data sets, default is NULL
#' @param method a string specifying the method.
#' Supported methods are `mcpcounter`, `epic`, `xcell`, `cibersort`, `cibersort_abs`, `ips`, `quantiseq`, `estimate`,`timer`, `svr`,`lsei`，`timer`, `quantiseq`.
#' @param tumor logical. use a signature matrix/procedure optimized for tumor samples,
#'   if supported by the method. Currently affects `EPIC`
#' @param arrays Runs methods in a mode optimized for microarray data.
#'   Currently affects `CIBERSORT`, `svr` and `xCell`.
#' @param perm  set permutations for statistical analysis (≥100 permutations recommended).
#' Currently affects `CIBERSORT` and `svr_ref`
#' @param reference immune cell gene matrix; eg lm22, lm6 or can be generate using generateRef/generateRef_rnaseq
#' @param scale_reference a logical value indicating whether the reference be scaled or not. If TRUE, the value in reference file will be centered and scaled in row direction. Currently affects `svr` and `lsei` method
#' @param platform character string indicating platform type. Defaults to "affymetrix"
#' Currently affects `ESTIMATE` method
#' @param plot Currently affects `IPS` method
#' @param scale_mrna  logical. If FALSE, disable correction for mRNA content of different cell types.
#'   This is supported by methods that compute an absolute score (EPIC and quanTIseq)
#' @param group_list tumor type list of samples
#' @param absolute.mode Run CIBERSORT or svr in absolute mode (default = FALSE)
#' @param abs.method if absolute is set to TRUE, choose method: 'no.sumto1' or 'sig.score'
#' @param ... arguments passed to the respective method
#'
#' @return `data.frame` with `ID` as first column and other column with the
#'     calculated cell fractions for each sample.
#' @author Dongqiang Zeng
#' @author Rongfang Shen
#' @references 1. Newman, A. M., Liu, C. L., Green, M. R., Gentles, A. J., Feng, W., Xu, Y., … Alizadeh, A. A. (2015). Robust enumeration of cell subsets from tissue expression profiles. Nature Methods, 12(5), 453–457.
#' 2. Vegesna R, Kim H, Torres-Garcia W, …, Verhaak R. (2013). Inferring tumour purity and stromal and immune cell admixture from expression data. Nature Communications 4, 2612.
#' 3. Finotello, F., Mayer, C., Plattner, C., Laschober, G., Rieder, D., Hackl, H., …, Sopper, S. (2019). Molecular and pharmacological modulators of the tumor immune contexture revealed by deconvolution of RNA-seq data. Genome medicine, 11(1), 34.
#' 4. Li, B., Severson, E., Pignon, J.-C., Zhao, H., Li, T., Novak, J., … Liu, X. S. (2016). Comprehensive analyses of tumor immunity: implications for cancer immunotherapy. Genome Biology, 17(1), 174.
#' 5. P. Charoentong et al., Pan-cancer Immunogenomic Analyses Reveal Genotype-Immunophenotype Relationships and Predictors of Response to Checkpoint Blockade. Cell Reports 18, 248-262 (2017).
#' 6. Becht, E., Giraldo, N. A., Lacroix, L., Buttard, B., Elarouci, N., Petitprez, F., … de Reyniès, A. (2016). Estimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression. Genome Biology, 17(1), 218.
#' 7. Aran, D., Hu, Z., & Butte, A. J. (2017). xCell: digitally portraying the tissue cellular heterogeneity landscape. Genome Biology, 18(1), 220.
#' 8. Racle, J., de Jonge, K., Baumgaertner, P., Speiser, D. E., & Gfeller, D. (2017). Simultaneous enumeration of cancer and immune cell types from bulk tumor gene expression data. ELife, 6, e26476.
#' @name deconvo_tme
#' @export deconvo_tme
#' @examples
#' # Loading TCGA-STAD expression data(raw count matrix)
#' data(eset_stad, package = "IOBR")
#' eset <- count2tpm(countMat = eset_stad, source = "local", idType = "ensembl")
#' deconvo_tme(eset = eset, arrays = FALSE, method = "cibersort")
#' # Absolute mode
#' deconvo_tme(eset = eset, arrays = FALSE, method = "cibersort", absolute.mode = TRUE)
deconvo_tme = function(eset,
                       project = NULL,
                       method = tme_deconvolution_methods,
                       arrays = FALSE,
                       tumor = TRUE,
                       perm = 1000,
                       reference,
                       scale_reference,
                       plot = FALSE,
                       scale_mrna,
                       group_list = NULL,
                       platform = "affymetrix",
                       absolute.mode = FALSE,
                       abs.method = "sig.score",
                       ...) {

  # message(paste0("\n", ">>> Running ", method))

  # run selected method
  res = switch(method,
               xcell = deconvo_xcell(eset, project ,arrays = arrays, ...),

               mcpcounter = deconvo_mcpcounter(eset, project, ...),

               epic = deconvo_epic(eset, project ,tumor = tumor, ...),

               cibersort = deconvo_cibersort(eset, project, absolute = absolute.mode, arrays = arrays, perm = perm, ...),

               cibersort_abs = deconvo_cibersort(eset, project, absolute = TRUE, abs_method = abs.method, arrays = arrays, perm = perm, ...),

               ips = deconvo_ips(eset,project,plot = plot, ...),

               quantiseq = deconvo_quantiseq(eset,project,tumor=tumor, arrays=arrays, scale_mrna=scale_mrna, ...),

               estimate = deconvo_estimate(eset,project,platform, ...),

               timer = deconvo_timer(eset,project, indications = group_list, ...),

               svr = deconvo_ref(eset, project, reference = reference, arrays = arrays, method = "svr", absolute.mode = absolute.mode, abs.method = abs.method,perm,...),

               lsei = deconvo_ref(eset, project, reference = reference, arrays = arrays, method = "lsei",scale_reference,perm,...) )

  res<-tibble::as_tibble(res)
  return(res)
}