CNV_infer: CNV_infer function

In alimahdipour/sciCNV: sciCNV

CNV_infer function

Description

To infer copy number variation (sciCNV) at single-cell resolution

Usage

CNV_infer(
  ss.expr,
  mean.ctrl,
  gen.Loc,
  resolution,
  baseline_adj = FALSE,
  baseline = 0,
  chr.n,
  P12,
  mat.fab
)

Arguments

ss.expr	scRNA-seq based expression for each test/control cell
mean.ctrl	The average gene expression of control cells
resolution	Adjusts the resolution, nrow(MSC)/(50*sharpness), used for the sciCNV-curve calculation. Default sharpness is =1.0 (best sharpnesses range between 0.6-1.4).
baseline_adj	The baseline adjustment is only applied to test cells if it is TRUE. Default is FALSE.
baseline	An optional correction to adjust the CNV zero setpoint (copy number gain =0) which is otherwise the median CNV of all genes.
chr.n	List of chromosome numbers associated with the list of genes
P12	The variable which is associated to resolution, is automatically calculated and is used for CNV calling.
mat.fab	is a merged matrix of FF, AW, BD vectors per cell. These vectors are used to calculate the relative expression of test to control.

Value

The output is the sciCNV curve of each single-cell across entire genome

Note

Please see the reference and supplmental materials described in the README file for additional information.

Author(s)

Ali Mahdipour-Shirayeh, Princess Margaret Cancer centre, University of Toronto

Examples

iCNV_percell <- CNV_infer(ss.expr=norm_expr_percell, mean.ctrl, gen.Loc, resolution=50, chr.n, P12, mat.fab)

alimahdipour/sciCNV documentation built on Oct. 16, 2022, 12:56 p.m.