Hi, @xiasijian

Thanks for your report about the comparison. As the DoAbsolute is just an open-source wrapper of ABSOLUTE, there are only two reasons for explaining the minor difference.

The used versions of ABSOLUTE are different.
Preprocessing did or default options used by the DoAbsolute.

I guess you are using the same version of ABSOLUTE, so the point 2 would be the only reason.

The code related to the reason has been given as the below.

DoAbsolute/R/DoAbsolute.R

Lines 18 to 55 in 8748cfb

    
           #' More detail about how to analyze ABSOLUTE results please see [this link](http://software.broadinstitute.org/cancer/software/genepattern/analyzing-absolute-data). 
        
           #' @param Seg a `data.frame` or a file (path) contains columns 
        
           #' "Sample", "Chromosome", "Start", "End", "Num_Probes", "Segment_Mean". 
        
           #' @param Maf MAF, default is `NULL`, can provided as `data.frame` or file path. 
        
           #' @param sigma.p Provisional value of excess sample level variance used for mode search. Default: 0 
        
           #' @param max.sigma.h Maximum value of excess sample level variance (Eq. 6). Default: 0.2 
        
           #' @param min.ploidy Minimum ploidy value to consider. Solutions implying lower ploidy values will be discarded. Default: 0.5 
        
           #' @param max.ploidy Maximum ploidy value to consider. Solutions implying greater ploidy values will be discarded. Default: 10 
        
           #' @param primary.disease Primary disease of the sample. Default: `NA` 
        
           #' @param platform one of "SNP_6.0", "Illumina_WES", "SNP_250K_STY". Default: "SNP_6.0" 
        
           #' @param temp.dir directory path used to store tempory files. Default: Absolute subdirectory under `tempdir()` 
        
           #' @param clean.temp if `TRUE`, auto-clean temp dir at the end. Default: `FALSE` 
        
           #' @param results.dir directory path used to store result files. Default: work directory 
        
           #' @param max.as.seg.count Maximum number of allelic segments. Samples with a higher segment count will be flagged as 'failed'. Default: 1500 
        
           #' @param max.non.clonal Maximum genome fraction that may be modeled as non-clonal (subclonal SCNA). Solutions implying greater values will be discarded. Default: 0.05 
        
           #' @param max.neg.genome Maximum genome fraction that may be modeled as non-clonal with copy-ratio below that of clonal homozygous deletion. Solutions implying greater values will be discarded. Default: 0.005 
        
           #' @param copy.num.type The type of copy number to be handled. Either total or allelic. Total is what this package for. Default: "total" 
        
           #' @param min.mut.af Minimum mutation allelic fraction. Mutations with lower allelic fractions will be filtered out before analysis. Default: 0.1 
        
           #' @param min.no.mut Minor allele frequency file, or NULL if one is not available. This specifies the data for somatic point mutations to be used by ABSOLUTE. Default: 5 
        
           #' @param verbose if `TRUE`, print extra info. Default: `FALSE` 
        
           #' @param nThread number of cores used for computation. Default: 1L 
        
           #' @param keepAllResult if `TRUE`, clean all results, otherwise clean result directory and keep most important results. Default: `TRUE` 
        
           #' @param recover if `TRUE`, recover previous unfinished work. 
        
           #' This is helpful when program stop unexpectedly when `clean.temp` is FALSE. Default: `FALSE` 
        
           #' @author Shixiang Wang <w_shixiang@163.com> 
        
           #' @return NULL 
        
           #' @import foreach doParallel data.table utils parallel 
        
           #' @export 
        
           #' @references Carter, Scott L., et al. "Absolute quantification of somatic DNA alterations in human cancer." Nature biotechnology 30.5 (2012): 413. 
        
           #' 
        
           DoAbsolute <- function(Seg, Maf = NULL, 
        
                                  sigma.p = 0, max.sigma.h = 0.2, min.ploidy = 0.5, max.ploidy = 10, 
        
                                  primary.disease = NA, platform = c("SNP_6.0", "Illumina_WES", "SNP_250K_STY"), 
        
                                  temp.dir = file.path(tempdir(), "Absolute"), clean.temp = FALSE, 
        
                                  results.dir = getwd(), max.as.seg.count = 1500, 
        
                                  max.non.clonal = 0.05, max.neg.genome = 0.005, copy.num.type = c("total", "allelic"), 
        
                                  min.mut.af = 0.1, min.no.mut = 5, verbose = FALSE, nThread = 1L, keepAllResult = TRUE, 
        
                                  recover = FALSE) {

DoAbsolute/R/DoAbsolute.R

Lines 136 to 141 in 8748cfb

    
           Seg$Chromosome <- as.character(Seg$Chromosome) 
        
           Seg$Chromosome <- gsub(pattern = "chr", replacement = "", Seg$Chromosome, ignore.case = TRUE) 
        
           Seg$Chromosome <- gsub(pattern = "X", replacement = "23", Seg$Chromosome, ignore.case = TRUE) 
        
           if (verbose) cat("-> Keeping only chr 1-23 for CNV data...\n") 
        
           autosome <- as.character(seq(1, 23)) 
        
           Seg <- Seg[Chromosome %in% autosome, ]

DoAbsolute/R/DoAbsolute.R

Line 203 in 8748cfb

maf <- maf[(t_alt_count / (t_ref_count + t_alt_count)) >= min.mut.af, ]

	#' More detail about how to analyze ABSOLUTE results please see [this link](http://software.broadinstitute.org/cancer/software/genepattern/analyzing-absolute-data).
	#' @param Seg a `data.frame` or a file (path) contains columns
	#' "Sample", "Chromosome", "Start", "End", "Num_Probes", "Segment_Mean".
	#' @param Maf MAF, default is `NULL`, can provided as `data.frame` or file path.
	#' @param sigma.p Provisional value of excess sample level variance used for mode search. Default: 0
	#' @param max.sigma.h Maximum value of excess sample level variance (Eq. 6). Default: 0.2
	#' @param min.ploidy Minimum ploidy value to consider. Solutions implying lower ploidy values will be discarded. Default: 0.5
	#' @param max.ploidy Maximum ploidy value to consider. Solutions implying greater ploidy values will be discarded. Default: 10
	#' @param primary.disease Primary disease of the sample. Default: `NA`
	#' @param platform one of "SNP_6.0", "Illumina_WES", "SNP_250K_STY". Default: "SNP_6.0"
	#' @param temp.dir directory path used to store tempory files. Default: Absolute subdirectory under `tempdir()`
	#' @param clean.temp if `TRUE`, auto-clean temp dir at the end. Default: `FALSE`
	#' @param results.dir directory path used to store result files. Default: work directory
	#' @param max.as.seg.count Maximum number of allelic segments. Samples with a higher segment count will be flagged as 'failed'. Default: 1500
	#' @param max.non.clonal Maximum genome fraction that may be modeled as non-clonal (subclonal SCNA). Solutions implying greater values will be discarded. Default: 0.05
	#' @param max.neg.genome Maximum genome fraction that may be modeled as non-clonal with copy-ratio below that of clonal homozygous deletion. Solutions implying greater values will be discarded. Default: 0.005
	#' @param copy.num.type The type of copy number to be handled. Either total or allelic. Total is what this package for. Default: "total"
	#' @param min.mut.af Minimum mutation allelic fraction. Mutations with lower allelic fractions will be filtered out before analysis. Default: 0.1
	#' @param min.no.mut Minor allele frequency file, or NULL if one is not available. This specifies the data for somatic point mutations to be used by ABSOLUTE. Default: 5
	#' @param verbose if `TRUE`, print extra info. Default: `FALSE`
	#' @param nThread number of cores used for computation. Default: 1L
	#' @param keepAllResult if `TRUE`, clean all results, otherwise clean result directory and keep most important results. Default: `TRUE`
	#' @param recover if `TRUE`, recover previous unfinished work.
	#' This is helpful when program stop unexpectedly when `clean.temp` is FALSE. Default: `FALSE`
	#' @author Shixiang Wang <w_shixiang@163.com>
	#' @return NULL
	#' @import foreach doParallel data.table utils parallel
	#' @export
	#' @references Carter, Scott L., et al. "Absolute quantification of somatic DNA alterations in human cancer." Nature biotechnology 30.5 (2012): 413.
	#'
	DoAbsolute <- function(Seg, Maf = NULL,
	sigma.p = 0, max.sigma.h = 0.2, min.ploidy = 0.5, max.ploidy = 10,
	primary.disease = NA, platform = c("SNP_6.0", "Illumina_WES", "SNP_250K_STY"),
	temp.dir = file.path(tempdir(), "Absolute"), clean.temp = FALSE,
	results.dir = getwd(), max.as.seg.count = 1500,
	max.non.clonal = 0.05, max.neg.genome = 0.005, copy.num.type = c("total", "allelic"),
	min.mut.af = 0.1, min.no.mut = 5, verbose = FALSE, nThread = 1L, keepAllResult = TRUE,
	recover = FALSE) {

	Seg$Chromosome <- as.character(Seg$Chromosome)
	Seg$Chromosome <- gsub(pattern = "chr", replacement = "", Seg$Chromosome, ignore.case = TRUE)
	Seg$Chromosome <- gsub(pattern = "X", replacement = "23", Seg$Chromosome, ignore.case = TRUE)
	if (verbose) cat("-> Keeping only chr 1-23 for CNV data...\n")
	autosome <- as.character(seq(1, 23))
	Seg <- Seg[Chromosome %in% autosome, ]

Different results between absoluted and doabsoluted? #26

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