trellisByGene: For two-way designs, plots mcmc.qpcr model predictions gene...
In MCMC.qpcr: Bayesian Analysis of qRT-PCR Data

Description Usage Arguments Value Author(s) References Examples

For each gene, plots model-predicted values and 95% credible intervals.

trellisByGene(modelSummary,xFactor,groupFactor,
nrow=2,lineWidth=0.4,whiskerWidth=0.2,pointSize=2.5,
facetScales="free_y",ylab="log(abundance)",
legendPos="bottom",posDodge=0.3)

`modelSummary`	two-way design model summary produced by HPDsummary()
`xFactor`	factor to form the x-axis
`groupFactor`	factor to form separate lines on the plot
`nrow`	number of rows in the resulting trellis plot
`lineWidth`	line width, passed as 'lwd' to geom_errorbar function (ggplot2)
`whiskerWidth`	width of the line denoting 95% CI margin, passed as 'width' to geom_errorbar function (ggplot2)
`pointSize`	passed as 'size' to geom_point function of ggplot2
`facetScales`	passed as 'scales' to facet_wrap function of ggplot2
`ylab`	y-axis label
`legendPos`	passed as 'legend.position' to theme function of ggplot2
`posDodge`	position dodge, increase for more jitter

A ggplot2 type object

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

# loading Cq data and amplification efficiencies
data(coral.stress) 
data(amp.eff) 

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=coral.stress,genecols=genecolumns,
condcols=conditions,effic=amp.eff,Cq1=37) 

# fitting the 2-way model
mm=mcmc.qpcr(
	fixed="condition+timepoint+condition:timepoint",
	data=dd,
	nitt=4000 # remark this line to analyze real data!
)

# summarizing results
ss=HPDsummary(mm,data=dd,summ.plot=FALSE)

# plotting predicted means and 95% CIs gene by gene
trellisByGene(ss,xFactor="condition",groupFactor="timepoint")