plotHist {sights} | R Documentation

## Histogram

### 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'))

[Package sights version 1.18.0 Index]