MCMC.qpcr (version 1.2.4)

getNormalizedData: Extracts qPCR model predictions

Description

Generates a table of model-derived log2-transformed transcript abundances without global sample effects (i.e., corresponding to efficiency-corrected and normalized qPCR data)

Usage

getNormalizedData(model, data, controls=NULL)

Arguments

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

Value

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.

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 {
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()
# }