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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).


Makes analysis more broadly accessible to researchers. Granatum is a web browser based scRNAseq analysis pipeline that conveniently walks the users through various steps of scRNA-seq analysis. It has a comprehensive list of modules, including plate merging and batch effect removal, outlier sample removal, gene filtering, gene expression normalization, cell clustering, differential gene expression analysis, pathway/ontology enrichment analysis, protein network interaction visualization, and pseudo-time cell series construction.

Cell-centric statistics

An analytic framework that models transcriptome dynamics through the analysis of aggregated cell-cell statistical distances within biomolecular pathways. Within an elaborate case study of circulating tumor cells derived from prostate cancer patients, we develop analytic methods of aggregated distances to identify five differentially expressed pathways associated to therapeutic resistance. Our aggregation analyses perform comparably to Gene Set Enrichment Analysis (GSEA) and better than differentially expressed genes followed by gene set enrichment. However, these methods were not designed to inform on differential pathway expression for a single cell. As such, our framework culminates with the novel aggregation method, cell-centric statistics (CCS).

MAST / Model-based Analysis of Single-cell Transcriptomics

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.


Produces tree structures outlining the hierarchical relationship between single-cell samples, while identifying latent groups of genes that can provide biological insights. CellTree can infer complex underlying hierarchical structures in cell populations from expression data alone, and also provide biological backing for the model it creates. The package can provide reasonable default values for most of the parameters used by the model inference, visualisation and analysis algorithms, making it possible for an unfamiliar user of the software to quickly evaluate a new dataset in a few simple lines of R code.


A software package for two-dimensional visualization of single cell data, which utilizes a plethora of projection methods and provides a way to systematically investigate the biological relevance of these low dimensional representations by incorporating domain knowledge. Annotated gene sets (referred to as gene 'signatures') are incorporated so that features in the projections can be understood in relation to the biological processes they might represent. FastProject provides a novel method of scoring each cell against a gene signature so as to minimize the effect of missed transcripts as well as a method to rank signature-projection pairings so that meaningful associations can be quickly identified. Additionally, FastProject is written with a modular architecture and designed to serve as a platform for incorporating and comparing new projection methods and gene selection algorithms.

PIVOT / Platform for Interactive analysis and Visualization Of Transcriptomics 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.

Sake / Single-cell RNA-Seq Analysis and Klustering Evaluation

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.