plotHist: Histogram

In sights: Statistics and dIagnostic Graphs for HTS

Description

Plot histogram of p-values or q-values for each plate or all plates together

Usage

plotHist(plotMatrix, plotRows = NULL, plotCols = NULL, plotAll = FALSE,
  plotSep = TRUE, plotName = NULL, colNames = NULL, ...)

Arguments

plotMatrix	Data frame or numeric matrix consisting only of p-values or q-values. Columns are samples, and rows are plate wells.
plotRows, plotCols	Optional integer vector. Indicate which row/column numbers from the plotMatrix should be plotted. If NULL then all rows/columns from the plotMatrix are used.
plotAll	Optional logical. Should all p-values or q-values be plotted together? Default is FALSE.
plotSep	Optional logical. If plotAll is FALSE, should plots be presented in separate windows? Default is TRUE.
plotName	Optional. Name of plotMatrix for plot title.
colNames	Optional. If plotAll is FALSE, names of plotCols for plot titles.
...	Optional. Additional parameters passed to geom_histogram.

Details

Histograms can be used to compare actual to expected p-value distributions obtained from statistical tests of replicated features. In the presence of rare biological events, the p-value distribution should be approximately uniformly distributed with somewhat more small p-values. Deviations from these patterns indicate that the activity measurements are incorrect and/or that the statistical model is incorrectly specified.

Value

Modifiable ggplot2 object or list of objects

Note

If using output from statT, statRVM, statFDR or statSights, please only select the plotCols corresponding to p-value and/or q-value columns, i.e., every 5th and/or 6th column in that output. Also, the x-axis label is derived from these column names indicating either 'p-values' or 'q-values'.

See Also

Other graphical devices: plot3d, plotAutoco, plotBox, plotHeatmap, plotIGFit, plotScatter

Examples

## load dataset
data(ex_dataMatrix)

## normalize data matrix using any method and store in new variable
ex_normMatrix <- normZ(dataMatrix = ex_dataMatrix, dataCols = 5:10)
## apply any test to normalized data and store in new variable
ex_testMatrix <- statRVM(normMatrix = ex_normMatrix,
repIndex = c(1,1,1,2,2,2))
## plot p-value data by selecting the p-value columns from test result matrix
plotHist(plotMatrix = ex_testMatrix, plotCols = c(5,10), plotName = 'Example',
colNames = c('Set_A', 'Set_B'))

sights documentation built on Nov. 8, 2020, 7:20 p.m.