copykat: copycat main_func.

In navinlabcode/copykat: COPYKAT: Inference of genomic copy number and subclonal structure of human tumors from high-throughput single cell RNA-seq data

copycat main_func.

Description

copycat main_func.

Usage

copykat(
  rawmat = rawdata,
  id.type = "S",
  cell.line = "no",
  ngene.chr = 5,
  LOW.DR = 0.05,
  UP.DR = 0.1,
  win.size = 25,
  norm.cell.names = "",
  KS.cut = 0.1,
  sam.name = "",
  distance = "euclidean",
  output.seg = "FALSE",
  plot.genes = "TRUE",
  genome = "hg20",
  n.cores = 1
)

Arguments

rawmat	raw data matrix; genes in rows; cell names in columns.
id.type	gene id type: Symbol or Ensemble.
cell.line	if the data are from pure cell line,put "yes"; if cell line data are a mixture of tumor and normal cells, still put "no".
ngene.chr	minimal number of genes per chromosome for cell filtering.
LOW.DR	minimal population fractions of genes for smoothing.
UP.DR	minimal population fractions of genes for segmentation.
win.size	minimal window sizes for segmentation.
norm.cell.names	a vector of normal cell names.
KS.cut	segmentation parameters, input 0 to 1; larger looser criteria.
sam.name	sample name.
distance	distance methods include euclidean, and correlation converted distance include pearson and spearman.
output.seg	TRUE or FALSE, output seg file for IGV visualization
plot.genes	TRUE or FALSE, output heatmap of CNVs with genename labels
genome	hg20 or mm10, current version only work for human or mouse genes
n.cores	number of cores for parallel computing.

Value

1) aneuploid/diploid prediction results; 2) CNA results in 220KB windows; 3) heatmap; 4) hclustering object.

Examples

test.ck <- copykat(rawmat=rawdata,id.type="S", ngene.chr=5, win.size=25, KS.cut=0.1,sam.name="test", distance="euclidean", norm.cell.names="", n.cores=4, output.seg="FALSE")

test.pred <- test.ck$prediction

navinlabcode/copykat documentation built on June 29, 2024, 2:31 a.m.