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. The package implements a lognormal model for higher-abundance data and a classic multi-gene normalization model. Mikhail V. Matz of The University of Texas at Austin authored this package, which includes several plotting functions for result visualization.

Use Cases

• Perform Bayesian gene expression analysis based on lognormal-Poisson error models.
• Incorporate control genes as Bayesian priors or global effect trackers as described.
• Apply multi-gene normalization on a per-sample basis using the classic model.
• Visualize qPCR analysis results using the included plotting functions.

Strengths

• Implements Bayesian analysis using MCMC, a method explicitly mentioned.
• Offers multiple modeling approaches: lognormal-Poisson, lognormal, and classic normalization.
• Includes a detailed tutorial document for user guidance.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Last update date is unknown; freshness unverified.

Provenance

Source

Mikhail V. Matz, The University of Texas at Austin

Collection Method

Likely contains experimental qRT-PCR data analyzed with the described statistical models.