getSwitchCorrect: Detect switching genes

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

Description

For each gene, time-resolved RNA-seq measurements are analyzed for occurence of switches (up or down)

Usage

getSwitchCorrect(dataset = mydata, experimentStepDetection = "WT",
  pValueSwitch = 0.05, cores = 1, mytimes = times,
  chooseFirst = TRUE)

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
pValueSwitch	Numeric, A threshold for counting cells as being invaded or not. When cells move towards negative z-direction, threshold should be negative.
cores	Numeric, Number of cores for parallelization, default 1 for no parallelization
mytimes	Numeric vector, Time points of the time-resolved RNA-seq data
chooseFirst	boolean, if TRUE (default), the earliest time point is chosen for which a switch could be detected, if FALSE, the time point with the best likelihood for the one-step model is chosen

Value

Data.frame containing gene names and results of switch detection, information about switch time point and direction

Author(s)

Marcus Rosenblatt, [e-mail]

Examples

data(MZsox)
mydata <- MZsox[seq(1,nrow(MZsox), by=10),]
resultSwitch <- getSwitch(dataset = mydata,
experimentStepDetection = "WT",
cores = 1,
mytimes = c(2.5,3,3.5,4,4.5,5,5.5,6))

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