HPDplotBygeneBygroup: Plots qPCR analysis results for individual genes
In MCMC.qpcr: Bayesian Analysis of qRT-PCR Data

Description Usage Arguments Value Author(s) References

For a specified gene, makes overlayed plots such as produced by HPDplotBygene()

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HPDplotBygeneBygroup(model, gene, group1, group2, group3 = NULL, 
interval = "ci", colors = c("coral", "cyan3", "grey50"), 
symbols = c(19, 17, 15), jitter = 0.16, yscale = "log2", ...)

`model`	model object produced by mcmc.qpcr()
`gene`	name of the gene to plot
`group1`	Combination of factors defining the first group (see HPDplotBygene() for details).
`group2`	Combination of factors defining the second group.
`group3`	(optional) Combination of factors defining the third group.
`interval`	'ci' (default) will plot 95% credible limits of the posterior distribution, 'sd' will plot the mean plus/minus one standard deviation of the posterior.
`colors`	Colors to use for different groups (see ?par -> col).
`symbols`	Symbols to use for different groups (see ?par -> pch).
`jitter`	Jitter distance between groups.
`yscale`	Scale on which to represent the data. In all mcmc.qpcr models the model scale is natural logarithm, which I prefer to translate into log2 or log10 (if the differences are orders of magnitude) for better human readability. The default is 'log2'; other options are 'log10' and 'native' (no rescaling of the model data). There is also a beta-option 'proportion', which is not useful for qPCR. It was added to cannibalize HPDplotBygene function for plotting results of the model operating with arcsin-square root transfromed proportions. With yscale="proportions", the plot will be on the original proportion scale but the tukey-like differences will still be reported on the asin(sqrt()) transformed scale.
`...`	additional parameters for HPDplotBygene() function, such as pval (see HPDplotBygene() help)

Generates a point-whiskers plot, lists pairwise mean differenes between all conditions, calculates and lists pairwise p-values (not corrected for multiple testing).

Mikhal V. Matz, UT 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.