tbluejc / scrna-tools

Table of software for the analysis of single-cell RNA-seq data.

scRNA-tools

A database of software tools for the analysis of single-cell RNA-seq data. To make it into the database software must be available for download and public use somewhere (CRAN, Bioconductor, PyPI, Conda, Github, Bitbucket, a private website etc). To view the database head to https://www.scRNA-tools.org.

Purpose

This database is designed to be an overview of the currently available scRNA-seq analysis software, it is unlikely to be 100% complete or accurate but will be updated as new software becomes available. If you notice a problem or would like to add something please make a pull request or open an issue.

Citation

If you find the scRNA-tools database useful for your work please cite our preprint: "Zappia L, Phipson B, Oshlack A. Exploring the single-cell RNA-seq analysis landscape with the scRNA-tools database, bioRxiv (2017), DOI: 10.1101/206573"

Structure

The main tools table has the following columns:

• Name
• Platform - Programming language or platform where it can be used
• DOI - Publication DOI
• PubDate - Publication date. Preprints are marked with PREPRINT and will be updated when published.
• Code - URL for publicly available code.
• Description
• License - Software license
• FUNCTION COLUMNS (Described below)
• Added - Date when the entry added.
• Updated - Date when the entry was last updated.

Function columns

The function columns are TRUE/FALSE columns indicating if the software has a particular function. These are designed to be used as filters, for example when looking for software to accomplish a particular task. They are also the most likely to be inaccurate as software is frequently updated and it is hard to judge all the functions a package has without making significant use of it. The function columns ask the following questions of the software:

• Assembly - Can it perform assembly?
• Alignment - Does it align reads to a reference?
• UMIs - Does it handle Unique Molecular Identifiers?
• Quantification - Does it quantify expression from reads?
• QualityControl - Does it perform some type of quality control of cells?
• Normalisation - Does it perform some type of normalisation?
• Imputation - Can it impute missing dropout values?
• Integration - Does it combine scRNA-seq datasets or other single-cell data types?
• GeneFiltering - Does it perform some filtering of genes?
• Clustering - Does it perform clustering of cells?
• Classification - Does it classify cells based on a reference dataset?
• Ordering - Does it order cells along a (pseudotime) trajectory?
• DifferentialExpression - Does it do some kind of differential expression?
• MarkerGenes - Does it identify or mark use of cell type markers?
• ExpressionPatterns - Can it find genes with interesting patterns over (psuedo) time?
• VariableGenes - Does it identify highly variable genes?
• GeneSets - Does it test or make use of annotated gene sets?
• GeneNetworks - Does it find co-regulated gene networks?
• CellCycle - Does it identify or correct for the cell cycle or cell cycle (or similar) genes?
• DimensionalityReduction - Can it perform some type of dimensionality reduction?
• Transformation - Does it transform between expression values and some over measure?
• Modality - Does it identify or make use of modality in expression?
• AlternativeSplicing - Does it identify alternatice splicing?
• RareCells - Does it identify rare cells types?
• StemCells - Does it identify stem cells in a population?
• Variants - Does it detect or make use of variants?
• Haplotypes - Does it make use of haplotypes or perform phasing?
• AlleleSpecific - Does it detect allele specific expression?
• Visualisation - Does it do some kind of visualisation? (showing how to make a plot using ggplot or matplotlib doesn't count)
• Interactive - Does it have some kind of interactive component or a GUI?
• Simulation - Does it have functions for simulating scRNA-seq data?