pipelineCNA: pipelineCNA Executes the entire SCEVAN pipeline that...

View source: R/pipelineCNA.R

pipelineCNAR Documentation

pipelineCNA Executes the entire SCEVAN pipeline that classifies tumour and normal cells from the raw count matrix, infer the clonal profile of cancer cells and looks for possible sub-clones in the tumour cell matrix automatically analysing the specific and shared alterations of each subclone and a differential analysis of pathways and genes expressed in each subclone.

Description

pipelineCNA Executes the entire SCEVAN pipeline that classifies tumour and normal cells from the raw count matrix, infer the clonal profile of cancer cells and looks for possible sub-clones in the tumour cell matrix automatically analysing the specific and shared alterations of each subclone and a differential analysis of pathways and genes expressed in each subclone.

Usage

pipelineCNA(
  count_mtx,
  sample = "",
  par_cores = 20,
  norm_cell = NULL,
  SUBCLONES = TRUE,
  beta_vega = 0.5,
  ClonalCN = FALSE,
  plotTree = FALSE,
  AdditionalGeneSets = NULL,
  SCEVANsignatures = TRUE
)

Arguments

count_mtx

raw count matrix

sample

sample name (optional)

par_cores

number of cores (default 20)

norm_cell

vector of normal cells if already known (optional)

SUBCLONES

find subclones (default TRUE)

beta_vega

specifies beta parameter for segmentation, higher beta for more coarse-grained segmentation. (default 0.5)

ClonalCN

clonal profile inference from tumour cells (optional)

plotTree

find subclones (optional)

AdditionalGeneSets

list of additional signatures of normal cell types (optional)

SCEVANsignatures

FALSE if you only want to use only the signatures specified in AdditionalGeneSets (default TRUE)

Examples

res_pip <- pipelineCNA(count_mtx)

AntonioDeFalco/SCEVAN documentation built on June 23, 2022, 11:08 a.m.