MALDIrppa (version 1.0.2)

peakPatterns: Display peak presence/absence patterns

Description

This function displays the patterns of peak presence and absence in an intensity matrix as generated from intensityMatrix.

Usage

peakPatterns(x, abs.lab = NA, barplot = TRUE,
             axis.lab = c("m/z", "Index"), bar.col = "red3",
             cell.col = c("white", "dodgerblue"), grid = FALSE,
             grid.col = "black", grid.lty = "dotted", cex.axis = 0.5,
             cex.lab = 0.5, ...)

Arguments

x

A matrix, data.frame or a list of MassPeaks objects.

abs.lab

Unique label used to denote peak absence in x (NA, default).

barplot

Logical value indicating whether a barplot of relative peak frequency across samples is displayed (TRUE, default).

axis.lab

Vector of axis labels in the c("x", "y") format.

bar.col

Colour of the bars in the barplot.

cell.col

Vector of colours for the table cells (format c("col.absence","col.presence")).

grid

Logical value indicating whether gridlines are added (FALSE, default).

grid.col

Colour of the gridlines ("black", default).

grid.lty

Style of the gridlines ("dotted", default. See lty in par).

cex.axis

Axis tick labels scaling factor relative to default.

cex.lab

Axis labels scaling factor relative to default.

…

Other arguments.

Details

The peak presence/absence patterns are displayed by rows from the first (top) to the last (bottom) sample in the data set x over the range of common m/z points. Positive peaks are by default represented by coloured cells whereas zero or absent peaks are left blank. A barplot on the top margins shows the relative frequency of a peak at each m/z point across samples.

See Also

See intensityMatrix.

Examples

# NOT RUN {
# Load example data

data(spectra) # list of MassSpectra class objects
data(type) # metadata

# Some pre-processing

sc.results <- screenSpectra(spectra,meta=type)
spectra <- sc.results$fspectra # filtered mass spectra
type <- sc.results$fmeta    # filtered metadata  
spectra <- transformIntensity(spectra, method = "sqrt")
spectra <- wavSmoothing(spectra)
spectra <- removeBaseline(spectra)
peaks <- detectPeaks(spectra)
peaks <- alignPeaks(peaks, minFreq = 0.8)

# Display patterns across all data

peakPatterns(peaks)

# Check results within isolate 280

peakPatterns(peaks[type$Isolate=="280"])

# }