peakDetection: Identification of biomarkers specific to distinct phases of...

In Biopeak: Identification of Impulse-Like Gene Expression Changes in Short Genomic Series Data

Description

The peakDetection function facilitates the identification of impulse-like gene expression changes based on user-defined selection criteria. This function calls the helper functions: bgCorr(), maProcessing() and findPeaks().

Usage

peakDetection(exprmat, series, actstrength = 1.3, prominence = 1.3,
  type = "rnaseq", minexpr = 0, peakwidth = 0, sustact = 0.6,
  bgcorr = T)

Arguments

exprmat	A numeric matrix with expression series data with variables as rownames.
series	A numeric vector defining the experimental series (e.g. time-points of sample acquisition).
actstrength	Threshold for minimal activation relative to the mean expression across all time-points.
prominence	Threshold for minimal peak prominence relative to the second highest peak.
type	A character string defining the sequencing platform. Possible values are c('microarray', 'rnaseq').
minexpr	An optional threshold for minimal mean expression across all time-points for a given gene.
peakwidth	An optional definitino of the minimal number of time-points that a peak spans (based on sustact threshold).
sustact	An optional threshold for minimal peakheight relative to the main peak to be considered as sustained activation.
bgcorr	An optional logical constant (TRUE or FALSE) defining if a background noise correction is performed or not.

Value

Returns a list comprising of multiple vectors and matrices. A numeric vector with the location of each peak (peakloc), a numeric vector with the absolute height of each peak (peakheight), a character vector of gene symbols for which at least one peak has been identified (peakgenes), a numeric matrix containing time-points with sustained activation, the logical vector defining which gene index has been selected and the numeric input vector defining the time-series.

Author(s)

David Lauenstein

Examples

# Example based on the heat-shock dataset
data(heat)
heat = as.matrix(heat)
# Define series
series <- c(37,40,41,42,43)
# Run the peak detection algorithm
peakdet <- peakDetection(heat, series, type ='rnaseq', actstrength = 1.5,
prominence = 1.3, minexpr = 5000)

Biopeak documentation built on Aug. 21, 2019, 5:10 p.m.