Dynamic expression data, nowadays obtained using high-throughput RNA sequencing, are essential to monitor transient gene expression changes and to study the dynamics of their transcriptional activity in the cell or response to stimuli. Several methods for data selection, clustering and functional analysis are available; however, these steps are usually performed independently, without exploiting and integrating the information derived from each step of the analysis.
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.
An R package for time series RNA sequencing data that integrates gene selection, clustering and functional annotation into a single framework. FunPat exploits functional annotations by performing for each functional term, e.g. a Gene Ontology term, an integrated selection-clustering analysis to select differentially expressed genes that share, besides annotation, a common dynamic expression profile.
Recognizes genes with non-constant expression over multiple ordered conditions, and simultaneously classify them into expression paths. EBSeq-HMM is based on an empirical Bayes mixture modeling approach for analysis of ordered RNA-seq experiments. It calculates the gene-specific protein-protein (PP) associated with each possible expression path and in doing so allows for genes to be classified into distinct expression paths with a pre-specified false discovery rate (FDR).
Allows to analyze RNA-seq time series data. DyNB is a statistical method built on the negative binomial likelihood and Gaussian processes (GPs). The software can be used to study relative differentiation efficiencies between biological samples. The applicability of DyNB was demonstrated by analyzing RNA-seq time-series datasets.
Allows integrative analysis and visualization of multiple lineages over whole time-course profiles. LIGAP is a computational methodology that is widely applicable to quantify differential time-course dynamics of many types of datasets and generalizes to any number of conditions. It summarizes all the time-course measurements together with the associated uncertainty for visualization and manual assessment purposes.
Processes pathway and clustering analysis of time series RNA-seq data. TRAP exploits over-representation analysis (ORA) and signaling pathway impact analysis (SPIA) methods to evaluate pathways interactions between genes. Time points, i.e., time series are analyzed by the software to estimate a single pathway-level statistic. It enables multiple thresholds and customized pathways made of genes of interest for analyzing data.