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).
Models single cell read count data in a hierarchical manner. SCHiRM is a model that can be applied to detect dependencies in single cell RNA sequencing (scRNA-seq) data. The software accounts for uncertainty in both input and output variables and can be extended in several ways due to its modular design. It was tested on both simulated and experimental scRNA-seq data.
Allows users to analyze and visualize RNA-Seq data. PIVOT furnishes four mains functionalities (i) a graphical interface that is able to wrap existing open source packages in a single user-interface (ii) multiple tools to manipulate datasets to perform derivation or normalization (iii) a way for allowing the compatibility between inputs and outputs from different analysis modules and, (iv) functions for automatically generate reports, publication-quality figures, and reproducible computations.
Utilizes the estimated pseudotime of the cells to find gene co-expression that involves time delay. LEAP sorts cells according to the estimated pseudotime and then computes the maximum correlation of all possible time lags. In addition, LEAP can apply a time-series inspired lag-based correlation analysis to reveal linearly dependent genetic associations.
Assists in navigating through the expression profile. SAKE is an R package that uses non-negative matrix factorization (NMF) method for unsupervised clustering. It offers (i) quality controls modules to compare total sequenced reads to total gene transcripts detected, (ii) sample correlation heatmap plot, (iii) heatmap of sample assignment from NMF run, with dark red indicating high confidence in cluster assignments, and (iv) t-distributed stochastic neighbor embedding (t-SNE) plot to compare NMF assigned groups with t-SNE projections.
A flexible statistical framework for the analysis of single-cell RNA sequencing data. MAST is suitable for supervised analyses about differential expression of genes and gene modules, as well as unsupervised analyses of model residuals, to generate hypotheses regarding co-expression of genes. MAST accounts for the bimodality of single-cell data by jointly modeling rates of expression (discrete) and positive mean expression (continuous) values. Information from the discrete and continuous parts is combined to infer changes in expression levels using gene or gene set-based statistics. Because our approach uses a generalized linear framework, it can be used to jointly estimate nuisance variation from biological and technical sources, as well as biological effects of interest.
Allows users to deduce undirected networks. NetworkInference is a standalone software which implements four algorithms for providing a fully connected, weighted network with indication about edge’s confidence. It includes various functionalities such as options for discretize or estimate the probability distribution. Moreover, the generated network is coupled to a list which attributes an edge for each pair of genes.