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.
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.
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.
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
ggplotormatplotlibdoesn't count) - Interactive - Does it have some kind of interactive component or a GUI?
- Simulation - Does it have functions for simulating scRNA-seq data?