Provides access to a variety of QC metrics for assessing the quality of RNA samples and of the intermediate stages of sample preparation and hybridization. Simpleaffy also offers fast implementations of popular algorithms for generating expression summaries and detection calls. It is designed to work alongside the core ‘affy’ package from BioConductor and provides high level functions for reading Affy .CEL files, phenotypic data, and then computing simple things with it, such as t-tests, fold changes and the like.
Permits exploration and integration of highly dimensional datasets. mixOmics proposes multivariate statistical approaches to identify similarities between two heterogeneous datasets. It summarizes information in a smaller data set and aims to highlight the biological entities that are of potential relevance with a strong focus on graphical representation. This tool assists in finding signatures of vaccine effect and allows a better understanding of immunological mechanisms activated by the intervention.
A comprehensive collection of functions for conducting meta-analyses in R. The metafor package includes functions to calculate various effect sizes or outcome measures, fit fixed, random, and mixed-effects models to such data, carry out moderator and meta-regression analyses, and create various types of meta-analytical plots. For meta-analyses of binomial and person-time data, metafor also provides functions that implement specialized methods, including the Mantel-Haenszel method, Peto's method, and a variety of suitable generalized linear (mixed-effects) models (i.e., mixed-effects logistic and Poisson regression models).
This package performs aggregation of ordered lists based on the ranks using several different algorithms: Borda count, Cross-Entropy Monte Carlo algorithm, Genetic algorithm, and a brute force algorithm.
Assists researchers in conducting two common types of analyses - meta-analysis of multiple gene expression datasets ( meta-analysis) or joint analysis of a gene expression dataset and a metabolomic dataset (integrative analysis), that have been collected under the same or comparable biological conditions. INMEX supports facile data upload, flexible data annotation, comprehensive meta-analysis approaches, as well as integrative analysis of metabolomic and transcriptomic data. With the increasing numbers of data sets that are being generated and becoming publicly available, INMEX will become a valuable tool to the research community.
Allows to manipulate and mine very large biological data collections for computational functional genomics. Sleipnir can perform common tasks: microarray processing, Bayesian and support vector machine (SVM) learning. The tool enables computational biologists to efficiently integrate thousands of genomic datasets and to rapidly mine them for biological knowledge. It can be useful for large integration tasks involving hundreds of diverse biological datasets.