Extracts qPCR model predictions
Generates a table of model-derived log2-transformed transcript abundances without global sample effects (i.e., corresponding to efficiency-corrected and normalized qPCR data)
getNormalizedData(model, data, controls=NULL)
model |
qPCR model: the output of mcmc.qpcr or mcmc.qpcr.lognormal function fitted with two additional options: random="sample", pr=TRUE . These options do not change the inferences of main effects but make it possible to retain among-sample variation of expression for each gene while still subtracting the global sample effects (i.e., perform "normalization") |
data |
The dataset that was analysed to generate the model (output of cq2counts or cq2log functions) |
controls |
List of control genes; required if the mcmc.qpcr model was fit with the option normalize=TRUE |
The function returns a list of two data frames. The first one, normData, is the model-predicted log2-transformed transcript abundances table. It has one column per gene and one row per sample. The second data frame, conditions, is a table of experimental conditions corresponding to the normData table.
Mikhail V. Matz, University of Texas at 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
library(MCMC.qpcr)
# 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 model (must include random="sample", pr=TRUE options)
mm=mcmc.qpcr(
fixed="condition",
data=dd,
controls=c("nd5","rpl11"),
nitt=4000,
pr=TRUE,
random="sample"
)
# extracting model predictions
pp=getNormalizedData(mm,dd)
# here is the normalized data:
pp$normData
# and here are the corresponding conditions:
pp$conditions
# putting them together for plotting:
ppcombo=cbind(stack(pp$normData),rep(pp$conditions))
names(ppcombo)[1:2]=c("expression","gene")
# plotting boxplots of normalized data:
ggplot(ppcombo,aes(condition,expression,colour=timepoint))+
geom_boxplot()+
facet_wrap(~gene,scales="free")+
theme_bw()Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.