cq2log {MCMC.qpcr}

Prepares qRT-PCR data for mcmc.qpcr analysis using lognormal and "classic" (normalization-based) models

Description

Log-transforms and efficiency-corrects Cq values, converting them to natural logs fo relative abundances, and stacks the dataset

Usage

cq2log(data, genecols, condcols, effic, noamp = 38, stacked=TRUE)

Arguments

data

Raw qRT-PCR dataset, one Cq column per gene, plus columns containing factors. The Cq columns, in addition to the proper Cq values, may contain NA (missing data) and -1, which means no amplification observed (i.e., zero target molecules at the start of qPCR reaction). Column headers are either gene names or factor names. Any number of fixed factors is allowed; any number of random factors that are gene-specific scalars (such as effect of genotype, or block) Must have a column called "sample", denoting individual cDNA preps. Technical replicates should not be averaged, they should be represented as independent rows with the same sample ID.

genecols

columns containing Cq data

condcols

columns corresponding to factors, including "sample" factor

effic

The PCR efficiency data for each of the analyzed genes. This is data frame with two columns: gene name (must exactly match the headers of gene columns in Cq data table!) and efficiency (fold- amplification per PCR cycle, determined from qPCR of serial dilutions; see PrimEff() function )

noamp

Value to replace instances of no amplification with. These instances would be coded by -1 in the data table. Specify 'noamp=NA' if you want to disregard them, but by default they will be converted into an arbitrarily low value, 38

stacked

Logical: whether to return stacked data for mcmc.qpcr modeling, or the originally-formatted table.

Details

The models that process cq2log output are expected to work well for datasets that don't have too many Cq values above 30 and don't have instances of no amplification. For examples, see cq2counts function.

Value

Returns a dataframe with a single response variable column ("count", even though it is actually a log-transformed relative abundance value), gene column ("gene") and several columns containing factors.

Author(s)

Mikhail V. Matz, UT Austin

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