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edgeR / empirical analysis of DGE in R
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Allows differential expression analysis of digital gene expression data. edgeR implements a range of statistical methodology based on the negative binomial distributions, including empirical Bayes estimation, exact tests, generalized linear models and quasi likelihood tests. The package and methods are general, and can work on other sources of count data, such as barcoding experiments and peptide counts.
limma / Linear Models for Microarray Data
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Provides an integrated solution for analysing data from gene expression experiments. limma contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. It also contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions: (i) it can perform both differential expression and differential splicing analyses of RNA-seq data; (ii) the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences.
AltAnalyze
An easy-to-use application for microarray, RNA-Seq and metabolomics analysis. For splicing sensitive platforms (RNA-Seq or Affymetrix Exon, Gene and Junction arrays), AltAnalyze will assess alternative exon (known and novel) expression along protein isoforms, domain composition and microRNA targeting. In addition to splicing-sensitive platforms, AltAnalyze provides comprehensive methods for the analysis of other data (RMA summarization, batch-effect removal, QC, statistics, annotation, clustering, network creation, lineage characterization, alternative exon visualization, gene-set enrichment and more).
svaseq
Uses surrogate variable analysis (sva) for estimating unwanted noise and unmodeled artifacts by (i) identifying the part of the genomic data only affected by artifacts and (ii) estimating the artifacts with principal components or singular vectors of the subset of the data matrix. svaseq contains functions for removing batch effects and other unwanted variation in high-throughput experiment. It was specifically created for count data or Fragments Per Kilobase Of Exon Per Million Fragments Mapped (FPKM) from sequencing experiments based on appropriate data transformation.
exploBATCH
Evaluates or diagnoses batch effect(s) in genomic data at the level of individual principal components (PCs). exploBATCH is a batch evaluation and correction approach based on probabilistic principal component and covariates analysis (PPCCA). The software includes two methods: (i) findBATCH that evaluates and detects the presence of significant batch effects and (ii) correctBATCH for batch correction. The software was evaluated using evaluate examples from breast and colorectal cancer and normal sample gene expression profiles.
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