summaryPlot: Wrapper function for ggplot2 to make bar and line graphs of...

Description Usage Arguments Details Value Author(s) References

View source: R/summaryPlot.R

Description

This function is called automatically by HPDsummary() and also can be used separately to plot the results produced by HPDsummary() with more plotting options

Usage

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summaryPlot(data, xgroup, facet = NA, type = "bar", x.order = NA, 
whiskers = "ci", genes = NA, log.base=2)

Arguments

data

A summary table generated by HPDplot(), it is the first element in the returned list.

xgroup

Which factor will be used to form the x axis (for 2-way designs).

facet

The factor by which the plot will be split into facets (for 2-way designs).

type

Two types are supported: "bar" and "line" ("line" also has points). "bar" is more useful to plot fold-changes returned when HPDsummary() is run with the option 'relative=TRUE'. "line" is better for plotting actual inferred transcript abundances across factor levels; it is particularly good for time courses and other cases when multiple factor levels must be compared to each other. "bar" is good to plot log(fold-changes) relative to global control.

x.order

A vector giving the order of factor levels on the x-axis. If unspecified, an alphanumeric order will be used.

whiskers

The interval indicated by the whiskers. Default is "ci", the 95% credible interval; another option is "sd" - standard deviation of the posterior.

genes

Vector of gene names to plot. By default, all genes in the summary will be plotted.

log.base

Base of the logarithm to indicate on y-axis (does not affect plotting).

Details

The function invokes ggplot() functon from the ggplot2 package to plot the results either as a single panel (one-way designs) or a multi-panel (2-way designs, one panel per level of the factor specified by 'facet' argument).

Value

A ggplot object. See http://docs.ggplot2.org/0.9.2.1/theme.html for ways to modify it, such as add text, rotate labels, change fonts, etc.

Author(s)

Mikhail V. Matz, University of Texas at Austin <[email protected]>

References

Matz MV, Wright RM, Scott JG (2013) No Control Genes Required: Bayesian Analysis of qRT-PCR Data. PLoS ONE 8(8): e71448. doi:10.1371/journal.pone.0071448


MCMC.qpcr documentation built on May 30, 2017, 4:36 a.m.