summaryPlot: Wrapper function for ggplot2 to make bar and line graphs of...
In MCMC.qpcr: Bayesian Analysis of qRT-PCR Data

Description Usage Arguments Details Value Author(s) References

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

1 2	summaryPlot(data, xgroup, facet = NA, type = "bar", x.order = NA, whiskers = "ci", genes = NA, log.base=2)

`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).

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).

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.

Mikhail V. Matz, University of Texas at Austin <matz@utexas.edu>

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 March 31, 2020, 5:22 p.m.