sciraRegAct: Infer TF activity from single cell gene expression profile

In WangNing0420/SCIRA: Single Cell Inference of Regulatory Activity

Description

sciraRegAct calculates TF activity scores in user input data set. It could be a single cell gene expression dataset.

Usage

sciraRegAct(data, regnet, norm = c("c", "z"), ncores = 4)

Arguments

data	A gene expression data matrix, with rows referring to genes and columns to samples.
regnet	A matrix, the regulatory network inferred from sciraInfNet function.
norm	A character indicating the method used to normalize your input data set, "c" for "centering"; "z" for "z-score normalization".
ncores	A numeric, the number of cores to use. See mclapply.

Details

sciraRegAct is one of the two main functions in SCIRA package. It takes the output regulatory network from sciraInfNet as input, and computes the activity of all TFs in this network from user provided data.

The data matrix should be single cell gene expression data, with rows are genes and columns are samples. Duplicated row names are not allowed, so you should average the these rows before running sciraRegAct.

Note that it's very important that you use the same gene identifier through out the whole analysis.

Value

A matrix of TF activity score with rows referring to TFs, columns to samples.

Examples

# estimate TF activity
# single cell RNA-seq dataset
data("scLiver")
# Lungnet
data("netLiver")
TFact <- sciraRegAct(scLiver,regnet=netLiver,norm="z",ncores=1)

WangNing0420/SCIRA documentation built on Aug. 5, 2019, 4:11 p.m.