HTqPCR: high-throughput analysis and visualization of quantitative real-time PCR data in R. Motivation: Quantitative real-time polymerase chain reaction (qPCR) is routinely used for RNA expression profiling, validation of microarray hybridization data and clinical diagnostic assays. Although numerous statistical tools are available in the public domain for the analysis of microarray experiments, this is not the case for qPCR. Proprietary software is typically provided by instrument manufacturers, but these solutions are not amenable to the tandem analysis of multiple assays. This is problematic when an experiment involves more than a simple comparison between a control and treatment sample, or when many qPCR datasets are to be analyzed in a high-throughput facility. Results: We have developed HTqPCR, a package for the R statistical computing environment, to enable the processing and analysis of qPCR data across multiple conditions and replicates. Availability:HTqPCR and user documentation can be obtained through Bioconductor or at http://www.ebi.ac.uk/bertone/software.
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References in zbMATH (referenced in 2 articles )
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- von Stechow, Louise (ed.); Delgado, Alberto Santos (ed.): Computational cell biology. Methods and protocols (2018)
- Dvinge, Heidi; Bertone, Paul: \textithtqpcr: high-throughput analysis and visualization of quantitative real-time PCR data in R (2009) ioport