Package: MCMC.qpcr 1.2.4

MCMC.qpcr: Bayesian Analysis of qRT-PCR Data

Quantitative RT-PCR data are analyzed using generalized linear mixed models based on lognormal-Poisson error distribution, fitted using MCMC. Control genes are not required but can be incorporated as Bayesian priors or, when template abundances correlate with conditions, as trackers of global effects (common to all genes). The package also implements a lognormal model for higher-abundance data and a "classic" model involving multi-gene normalization on a by-sample basis. Several plotting functions are included to extract and visualize results. The detailed tutorial is available here: <https://matzlab.weebly.com/uploads/7/6/2/2/76229469/mcmc.qpcr.tutorial.v1.2.4.pdf>.

Authors:Mikhail V. Matz

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MCMC.qpcr.pdf |MCMC.qpcr.html✨
MCMC.qpcr/json (API)

# Install 'MCMC.qpcr' in R:
install.packages('MCMC.qpcr', repos = c('https://z0on.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:
  • amp.eff - Amplification efficiencies and experimental Cq1
  • beckham.data - Cellular heat stress response data.
  • beckham.eff - Amplification efficiencies for beckham.data
  • coral.stress - RT-qPCR of stress response in coral Porites astreoides
  • dilutions - Data to determine amplification efficiency

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.85 score 2 stars 35 scripts 226 downloads 23 exports 36 dependencies

Last updated 5 years agofrom:0c74bcb873. Checks:9 OK. Indexed: yes.

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Exports:cq2countscq2genormcq2logdiagnostic.mcmcgetNormalizedDataHPDplotHPDplotBygeneHPDplotBygeneBygroupHPDpointsHPDsummarymcmc.converge.checkmcmc.pvalmcmc.qpcrmcmc.qpcr.classicmcmc.qpcr.lognormalnormalize.qpcroutlierSamplespadj.hpdsummarypadj.qpcrPrimEffsoftNormsummaryPlottrellisByGene

Dependencies:apeclicodacolorspacecorpcorcubaturedigestfansifarverggplot2gluegtableisobandlabelinglatticelifecyclemagrittrMASSMatrixMCMCglmmmgcvmunsellnlmepillarpkgconfigR6RColorBrewerRcpprlangscalestensorAtibbleutf8vctrsviridisLitewithr

Readme and manuals

Help Manual

Help pageTopics
Bayesian analysis of qRT-PCR dataMCMC.qpcr
amplification efficiencies and experimental Cq1 (optional column)amp.eff
Cellular heat stress response data.beckham.data
amplification efficiencies for beckham.databeckham.eff
RT-qPCR of stress response in coral Porites astreoidescoral.stress
Prepares qRT-PCR data for mcmc.qpcr analysiscq2counts
Reformats raw Ct data for geNorm analysis (non-parametric selection of stable control genes) as implemented in selectHKgenes function (package SLqPCR)cq2genorm
Prepares qRT-PCR data for mcmc.qpcr analysis using lognormal and "classic" (normalization-based) modelscq2log
Plots three diagnostic plots to check the validity of the assumptions of linear model analysis.diagnostic.mcmc
Data to determine amplification efficiencydilutions
Extracts qPCR model predictionsgetNormalizedData
Plotting fixed effects for all genes for a single combination of factorsHPDplot
Plots qPCR analysis results for individual genes.HPDplotBygene
Plots qPCR analysis results for individual genesHPDplotBygeneBygroup
HPDplot, HPDpointsHPDpoints
Summarizes and plots results of mcmc.qpcr function series.HPDsummary
MCMC diagnostic plotsmcmc.converge.check
calculates p-value based on Bayesian z-score or MCMC samplingmcmc.pval
Analyzes qRT-PCR data using generalized linear mixed modelmcmc.qpcr
Analyzes qRT-PCR data using "classic" model, based on multigene normalization.mcmc.qpcr.classic
Fits a lognormal linear mixed model to qRT-PCR data.mcmc.qpcr.lognormal
Internal function called by mcmc.qpcr.classicnormalize.qpcr
detects outlier samples in qPCR dataoutlierSamples
Adjusts p-values within an HPDsummary() object for multiple comparisonspadj.hpdsummary
Calculates adjusted p-values corrected for multiple comparisonspadj.qpcr
Determines qPCR amplification efficiencies from dilution seriesPrimEff
Accessory function to mcmc.qpcr() to perform soft normalizationsoftNorm
Wrapper function for ggplot2 to make bar and line graphs of mcmc.qpcr() resultssummaryPlot
For two-way designs, plots mcmc.qpcr model predictions gene by genetrellisByGene