getSwitchNew: Detect switching genes (new version)

In marcusrosenblatt/RNAsense: Analysis of Time-Resolved RNA-Seq Data

Description

For each gene and for each time point, RNA-seq count data is analyzed for fold changes between two experimental conditions. This functions bases on functions from the R package NBPSeq package for fold change analysis

Usage

getSwitchNew(dataset = mydata, experimentStepDetection = "WT",
  cores = 1, mytimes = times, pValueSwitch = 0.01)

Arguments

dataset	Object of class SummarizedExperiment, output of SummarizedExperiment, as assays use a numeric matrix with your RNAseq count data, rows correspond to different genes, columns correspond to different experiments, as rowData provide a DataFrame with columns name (geneID) and genename (the gene names), as colData provide a DataFrame with columns condition, time and replicate
experimentStepDetection	Character, Name of condition for which switch detection is performed
cores	Numeric, Number of cores for parallelization, default 1 for no parallelization
mytimes	Numeric vector, Time points of the time-resolved RNA-seq data
pValueSwitch	Numeric, pValue for switch detection

Value

Data.frame containing gene names, log fold change and p-values calculated from NBPSeq, each gene appears as often as available time points

Author(s)

Marcus Rosenblatt, [e-mail]

Examples

data(MZsox)
mydata <- MZsox[seq(1,nrow(MZsox), by=20),]
resultFC <- getSwitchNew(dataset = mydata,
experimentStepDetection = "WT",
cores = 1,
mytimes = c(2.5,3,3.5,4,4.5,5,5.5,6),
pValueSwitch=0.01)

marcusrosenblatt/RNAsense documentation built on Dec. 7, 2020, 5:26 p.m.