DoAbsolute
The goal of DoAbsolute is to automate ABSOLUTE calling for multiple samples in parallel way.
ABSOLUTE is a famous
software developed by Broad Institute, however, the RunAbsolute
function is designed for computing one sample each time and set no
default values. DoAbsolute helps user set default parameters
according to ABSOLUTE
documentation,
provides a uniform interface to input data easily and runs
RunAbsolute parallelly.
More detail about how to analyze ABSOLUTE results please see this link.
Installation
You can install the released version of DoAbsolute with:
# Option 1: GitHub
devtools::install_github("ShixiangWang/DoAbsolute")
# Option 2: r-universe
#
# Enable repository from shixiangwang
options(repos = c(
shixiangwang = 'https://shixiangwang.r-universe.dev',
CRAN = 'https://cloud.r-project.org'))
install.packages('DoAbsolute')Install ABSOLUTE, the version provided by DoAbsolute is 1.0.6. You can find available versions at https://software.broadinstitute.org/cancer/cga/absolute_download. Users of DoAbsolute all should accept LICENCE from Firehorse.
install.packages("numDeriv")
path_to_file = system.file("extdata", "ABSOLUTE_1.0.6.tar.gz", package = "DoAbsolute", mustWork = T)
install.packages(path_to_file, repos = NULL, type="source")NOTE: the builtin ABSOLUTE package is modified for fitting current R version and reducing some errors (this may be described in NEWS.md). If you want to use the raw package without modification, you can find it here. Remember the raw package (v1.0.6) is only working under R4.2.
Example
This is a basic example which shows you how to run DoAbsolute using example data from ABSOLUTE documentation.
Load package.
library(DoAbsolute)example_path = system.file("extdata", package = "DoAbsolute", mustWork = T)
library(data.table)
# Load Test Data ----------------------------------------------------------
# segmentation file
seg_normal = file.path(example_path, "SNP6_blood_normal.seg.txt")
seg_solid = file.path(example_path, "SNP6_solid_tumor.seg.txt")
seg_metastatic = file.path(example_path, "SNP6_metastatic_tumor.seg.txt")
# MAF file
maf_solid = file.path(example_path, "solid_tumor.maf.txt")
maf_metastatic = file.path(example_path, "metastatic_tumor.maf.txt")
# read data
seg_normal = fread(seg_normal)
seg_solid = fread(seg_solid)
seg_metastatic = fread(seg_metastatic)
maf_solid = fread(maf_solid)
maf_metastatic = fread(maf_metastatic)
# merge data
Seg = Reduce(rbind, list(seg_normal, seg_solid, seg_metastatic))
Maf = Reduce(rbind, list(maf_solid, maf_metastatic))
Seg$Sample = substr(Seg$Sample, 1, 15)
Maf$Tumor_Sample_Barcode = substr(Maf$Tumor_Sample_Barcode, 1, 15)
# test function
DoAbsolute(Seg = Seg, Maf = Maf, platform = "SNP_6.0", copy.num.type = "total",
results.dir = "test", nThread = 2, keepAllResult = TRUE, verbose = TRUE)Citation
Wang, Shixiang, et al. "The predictive power of tumor mutational burden
in lung cancer immunotherapy response is influenced by patients' sex."
International journal of cancer (2019).
Reference:
- Carter, Scott L., et al. “Absolute quantification of somatic DNA alterations in human cancer.” Nature biotechnology 30.5 (2012): 413.