HPDplot: Plotting fixed effects for all genes for a single combination...
In MCMC.qpcr: Bayesian Analysis of qRT-PCR Data

Description Usage Arguments Details Value Author(s) References Examples

Calculates and plots posterior means with 95% credible intervals for specified fixed effects (or their combination) for all genes

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HPDplot(model, factors, factors2 = NULL, ylimits = NULL, 
hpdtype = "w", inverse = FALSE, jitter = 0, plot = TRUE, grid = TRUE, 
zero = TRUE, ...)

`model`	The output of mcmc.qpcr function.
`factors`	A vector of names of fixed effects of interest; see details.
`factors2`	A second vector of fixed effect names to be subtracted from the first; see details.
`ylimits`	Y-limits for the plot such as c(-3,6); autoscale by default.
`hpdtype`	Specify hpdtype="l" to plot the upper and lower 95% credible limits as a continuous dashed line across all genes. This is useful to compare credible intervals among several models on the same plot. By default (hpdtype="w") the limits are plotted as whiskers around each point.
`inverse`	Plot the inverse of the result.
`jitter`	For hpdtype="w", shifts the plotted values and whiskers by the specified distance along the x axis (reasonable jitter values are 0.15 or -0.15, for example). This helps plot several results (different models or factor combinations) on the same plot (use HPDpoints to add to existing plot)
`plot`	if plot = FALSE the function returns a table of calculated posterior modes, means, upper and lower 95% credible limits (all on log(2) scale), and two types of p-values: derived from Bayesian z-scores, and derived directly from MCMC sample. All such outputs for a given experiment should be concatenated with rbind and processed by padj.qpcr() function to adjust the p-values for multiple comparisons (disregarding the entries corresponding to control genes)
`grid`	Whether to draw vertical grid lines to separate genes.
`zero`	Whether to draw a horizontal line at 0.
`...`	Various plot() options; such as col (color of lines and symbols), pch (type of symbol), main (plot title) etc.

Use summary(MCMCglmm object) first to see what fixed effect names are actually used in the output. For example, if summary shows:

gene1:conditionheat
gene2:conditionheat
....
gene1:timepointtwo
gene2:timepointtwo
....
gene1:conditionheat:timepointtwo
gene2:conditionheat:timepointtwo

, it is possible to specify factors="conditionheat" to plot only the effects of the heat.

If a vector of several fixed effect names is given, for example: factors=c("timepointtwo","treatmentheat:timepointtwo") the function will plot the posterior mean and credible interval for the sum of these effects.

If a second vector is also given, for example,
factors=c("f1","f2"), factors2=c("f3","f4")
the function will plot the difference between the sums of these two groups of factors. This is useful for pairwise analysis of differences in complicated designs.

A plot or a table (plot = F).

Use the function HPDpoints() if you need to add graphs to already existing plot.

Mikhail 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

# loading Cq data and amplification efficiencies
data(coral.stress) 
data(amp.eff) 
# extracting a subset of data 
cs.short=subset(coral.stress, timepoint=="one")

genecolumns=c(5,6,16,17) # specifying columns corresponding to genes of interest
conditions=c(1:4) # specifying columns containing factors  

# calculating molecule counts and reformatting:
dd=cq2counts(data=cs.short,genecols=genecolumns,
condcols=conditions,effic=amp.eff,Cq1=37) 

# fitting the model
mm=mcmc.qpcr(
	fixed="condition",
	data=dd,
	controls=c("nd5","rpl11"),
	nitt=3000,burnin=2000 # remove this line when analyzing real data!
)

# plotting log2(fold change) in response to heat stress for all genes
HPDplot(model=mm,factors="conditionheat",main="response to heat stress")