peakDetection {Biopeak} | R Documentation |
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().
peakDetection(exprmat, series, actstrength = 1.3, prominence = 1.3, type = "rnaseq", minexpr = 0, peakwidth = 0, sustact = 0.6, bgcorr = T)
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. |
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.
David Lauenstein
# 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)