MCMC.qpcr (version 1.2.4)

trellisByGene: For two-way designs, plots mcmc.qpcr model predictions gene by gene

Description

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

Usage

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)

Arguments

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

Value

A ggplot2 type object

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

Examples

# NOT RUN {
# 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")
# }