Analyzing paleolimnological data with R

An R package for analyzing and modeling ranking data

BACKGROUND In medical informatics, psychology, market research and many other fields, researchers often need to analyze and model ranking data. However, there is no statistical software that provides tools for the comprehensive analysis of ranking data. Here, we present pmr, an R package for analyzing and modeling ranking data with a bundle of tools. The pmr package enables descriptive statisti...

Basic4Cseq: an R/Bioconductor package for analyzing 4C-seq data

SUMMARY Basic4Cseq is an R/Bioconductor package for basic filtering, analysis and subsequent near-cis visualization of 4C-seq data. The package processes aligned 4C-seq raw data stored in binary alignment/map (BAM) format and maps the short reads to a corresponding virtual fragment library. Functions are included to create virtual fragment libraries providing chromosome position and further inf...

Novel R Pipeline for Analyzing Biolog Phenotypic Microarray Data

Data produced by Biolog Phenotype MicroArrays are longitudinal measurements of cells' respiration on distinct substrates. We introduce a three-step pipeline to analyze phenotypic microarray data with novel procedures for grouping, normalization and effect identification. Grouping and normalization are standard problems in the analysis of phenotype microarrays defined as categorizing bacterial r...

unifiedWMWqPCR: analyzing RT-qPCR data in R with the unified Wilcoxon–Mann–Whitney test

Most conventional statistical tests for analyzing RT-qPCR data require normalization before differential expression can be assessed. This normalization can have a substantial effect on the interpretation and validity of the statistical test, but this effect is often ignored. Therefore the uWMW test, as proposed in [1], extends the Wilcoxon–Mann–Whitney test so that the normalization is incorpor...

Analyzing metagenomic data with metagenomeSeq