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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.
Allows the analysis of multiple time course transcriptomics data. maSigPro is a regression based approach to find genes for which there are significant gene expression profile differences between experimental groups in time course microarray and RNA-Seq experiments. The software incorporates a clustering function to visualize genes with similar profiles. maSigPro was initially developed for microarrays and later updated to model count data. It includes Iso-maSigPro, a functionality to study differential isoform usage in time course RNA-seq experiments.
Analyzes expression dynamics in high throughput profiling experiments with ordered conditions. Trendy provides statistical analyses and summaries of feature-specific and global expression dynamics. For each gene, this application fits a set of segmented regression models with varying numbers of breakpoints. It also included an R/Shiny application to visualize and explore expression dynamics for specific genes and the ability to extract genes containing user-defined patterns of expression.
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