plotCtCategory: Summarising the feature categories for Ct values.

In HTqPCR: Automated analysis of high-throughput qPCR data

Description

This function will provide a summary of the featureCategory for a qPCRset. Focus can either be on categories across samples, or across features.

Usage

plotCtCategory(q, cards = TRUE, by.feature = FALSE, stratify, col, xlim, main, ...)

Arguments

q	object of class qPCRset.
cards	integers, the number of the cards (samples) to plot.
by.feature	logical, should the categories be summarised for features rather than samples. See details.
stratify	character string, either "type" or "class" indicating if the categories should be stratified by featureType or featureClass of q. Ignored if by.features is TRUE.
col	vector with the colours to use for the categories. Default is green for "OK", yellow for "Unreliable" and red for "Undetermined". See details.
xlim	vector, the limits of the x-axis. If by.feature is FALSE, this can be used to adjust the size of the barplot to fit in the colour legend.
main	character string, the title of the plot.
...	further arguments passed to barplot or heatmap.

Details

This function is for generating two different types of plot. If by.feature=FALSE the number of each featureCategory will be counted for each card, and a barplot is made. If however by.feature=TRUE, then the categories for each feature across the selected cards will be clustered in a heatmap.

The colours given in col correspond to all the unique categories present in the entire featureCategory of q, even categories not represented for the samples selected by cards. Categories are sorted alphabetically, and colours assigned accordingly.

For by.feature=TRUE the plot can be modified extensively using calls to the underlying heatmap function, such as setting cexRow to adjust the size of row labels.

Value

A figure is produced on the current graphics device.

Author(s)

Heidi Dvinge

See Also

setCategory, and heatmap for the underlying plotting function for by.feature=TRUE.

Examples

# Load example preprocessed data
data(qPCRpros)
# Plot categories for samples
plotCtCategory(qPCRpros)
plotCtCategory(qPCRpros, cards=1:3, stratify="class")
# Categories for features
plotCtCategory(qPCRpros, by.feature=TRUE)

Example output

Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: ‘BiocGenerics’

The following objects are masked from ‘package:parallel’:

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from ‘package:stats’:

    IQR, mad, sd, var, xtabs

The following objects are masked from ‘package:base’:

    anyDuplicated, append, as.data.frame, basename, cbind, colnames,
    dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,
    grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,
    order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
    rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,
    union, unique, unsplit, which.max, which.min

Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.

Loading required package: RColorBrewer
Loading required package: limma

Attaching package: ‘limma’

The following object is masked from ‘package:BiocGenerics’:

    plotMA

HTqPCR documentation built on Nov. 8, 2020, 6:51 p.m.