In ShixiangWang/VSHunter: Capture Variation Signatures from Genomic Data

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  eval = FALSE
)

# devtools::load_all()
# library(flexmix)
# library(QDNAseq)
# library(NMF)
if(!require("VSHunter")){
  devtools::load_all()
}

The goal of VSHunter is to capture variation signature from genomic data. For now, we decode copy number pattern from absolute copy number profile. This package collects R code from paper Copy number signatures and mutational processes in ovarian carcinoma and tidy them as a open source R package for bioinformatics community.

Before you use this tool, you have to obtain absolute copy number profile for samples via software like ABSOLUTE v2, QDNASeq etc..

Procedure

1. summarise copy-number profile using a number of different feature distributions:
   • Sgement size
   • Breakpoint number (per ten megabases)
   • change-point copy-number
   • Breakpoint number (per chromosome arm)
   • Length of segments with oscillating copy-number
2. apply mixture modelling to breakdown each feature distribution into mixtures of Gaussian or mixtures of Poisson distributions using the flexmix package.
3. generate a sample-by-component matrix representing the sum of posterior probabilities of each copy-number event being assigned to each component.
4. use NMF package to factorise the sample-by-component matrix into a signature-by-sample matrix and component-by signature-matrix.

knitr::include_graphics(path = "https://media.springernature.com/m685/springer-static/image/art%3A10.1038%2Fs41588-018-0179-8/MediaObjects/41588_2018_179_Fig1_HTML.png")

Installation

You can install UCSCXenaTools from github with:

# install.packages("devtools")
devtools::install_github("ShixiangWang/VSHunter", build_vignettes = TRUE)

Load package.

library(VSHunter)

Usage

Load example data:

load(system.file("extdata/example_cn_list.RData", package = "VSHunter"))

tcga_segTabs is a list contain absolute copy number profile for multiple samples, each sample is a data.frame in the list.

Obtain CNV summary info.

tcga_frac = cnv_getLengthFraction(tcga_segTabs)

Derive feature distributions

tcga_features = cnv_derivefeatures(CN_data = tcga_segTabs, cores = 1, genome_build = "hg19")

Fit model components

tcga_components = cnv_fitMixModels(CN_features = tcga_features, cores = 4)

Generate a sample-by-component matrix

Generate a sample-by-component matrix representing the sum of posterior probabilities of each copy-number event being assigned to each component.

tcga_sample_component_matrix = cnv_generateSbCMatrix(tcga_features, tcga_components, cores = 4)

Choose optimal number of signatures.

tcga_sig_choose = cnv_chooseSigNumber(tcga_sample_component_matrix, nrun = 10, cores = 4)

Do not test a randomise data (save time).

tcga_sig_choose2 = cnv_chooseSigNumber(tcga_sample_component_matrix, nrun = 10, cores = 4, testRandom = FALSE)

Extract signatures

tcga_signatures = cnv_extractSignatures(tcga_sample_component_matrix, nsig = 3, cores = 4)

Auto-capture signatures

Function cnv_autoCaptureSignatures() finish three steps (choose best number of signatures, extract signatures and quantify exposure) above in an antomated way. The arguments of this function are same as cnv_chooseSigNumber().

tcga_results = cnv_autoCaptureSignatures(tcga_sample_component_matrix, nrun=10, cores = 4)

The result object is a list which contains all results need fro downstream analysis, include NMF result related to best rank value, signature matrix, absolute and relative exposure (contribution) and best rank survey etc..

CNV signature calling pipeline

This feature is implemented in cnv_pipe() function.

Visualization

Visualize CNV distribution by normalized CN length or chromosome.

cnv_plotDistributionProfile(tcga_frac)

cnv_plotDistributionProfile(tcga_frac, mode = "cd")

cnv_plotDistributionProfile(tcga_frac, mode = "cd" , fill = TRUE)

Plot functions:

cnv_plotDistributionProfile()
cnv_plotFeatureDistribution()
cnv_plotMixComponents()
cnv_plotSignatures()

Citation

• Macintyre, Geoff, et al. "Copy number signatures and mutational processes in ovarian carcinoma." Nature genetics 50.9 (2018): 1262.

If you wanna thank my work for this package, you can also cite (and inlucde link of this package - https://github.com/ShixiangWang/VSHunter):

• Wang, Shixiang, et al. "APOBEC3B and APOBEC mutational signature as potential predictive markers for immunotherapy response in non-small cell lung cancer." Oncogene (2018).

ShixiangWang/VSHunter documentation built on June 27, 2019, 4:56 p.m.