| trellisByGene {MCMC.qpcr} | R Documentation |
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
Author(s)
Mikhal V. Matz, UT Austin, matz@utexas.edu
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
# 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")