detects outlier samples in qPCR data
reports samples that have too little starting material relative to others (by default, less by two standard deviations)
outlierSamples(model, data, z.cutoff = -2)
model |
qPCR model: the output of mcmc.qpcr or mcmc.qpcr.lognormal function fitted with pr=TRUE option |
data |
The dataset that was analysed to generate the model (output of cq2counts or cq2log functions) |
z.cutoff |
z-score cutoff to report an outlier sample. |
A vector containing outlier sample names.
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
# loading Cq data and amplification efficiencies
data(coral.stress)
data(amp.eff)
# extracting a subset of data
cs.short=subset(coral.stress, timepoint=="one")
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=cs.short,genecols=genecolumns,
condcols=conditions,effic=amp.eff,Cq1=37)
# fitting the model
mm=mcmc.qpcr(
fixed="condition",
data=dd,
controls=c("nd5","rpl11"),
nitt=4000, # remove this line when analyzing real data!
pr=TRUE
)
# detecting outliers
outliers=outlierSamples(mm,dd)
# removing outliers
dd=dd[!(dd$sample %in% outliers),]Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.