https://github.com/hms-dbmi/scde

R package for analyzing single-cell RNA-seq data
https://github.com/hms-dbmi/scde

analysis bioinformatics heterogenity ngs r single-cell transcriptomics

Last synced: about 2 months ago
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R package for analyzing single-cell RNA-seq data

• Host: GitHub
• URL: https://github.com/hms-dbmi/scde
• Owner: hms-dbmi
• License: other
• Created: 2015-05-06T16:53:57.000Z (over 9 years ago)
• Default Branch: main
• Last Pushed: 2024-02-29T05:49:35.000Z (10 months ago)
• Last Synced: 2024-08-02T16:45:39.567Z (5 months ago)
• Topics: analysis, bioinformatics, heterogenity, ngs, r, single-cell, transcriptomics
• Language: R
• Homepage: http://pklab.med.harvard.edu/scde
• Size: 14 MB
• Stars: 171
• Watchers: 25
• Forks: 64
• Open Issues: 42
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGELOG.md
  • License: license.txt

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README

        
[![](https://img.shields.io/badge/release%20version-2.27.1-green.svg)](https://www.bioconductor.org/packages/scde)

# Overview of SCDE

The `scde` package implements a set of statistical methods for analyzing single-cell RNA-seq data. `scde` fits individual error models for single-cell RNA-seq measurements. These models can then be used for assessment of differential expression between groups of cells, as well as other types of analysis. The `scde` package also contains the `pagoda` framework which applies pathway and gene set overdispersion analysis to identify aspects of transcriptional heterogeneity among single cells. 

  

The overall approach to the differential expression analysis is detailed in the following publication:  

["Bayesian approach to single-cell differential expression analysis" (Kharchenko PV, Silberstein L, Scadden DT, Nature Methods, doi:10.1038/nmeth.2967)](http://www.nature.com/nmeth/journal/v11/n7/abs/nmeth.2967.html)

The overall approach to pathways and gene set overdispersion analysis is detailed in the following publication:

["Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis" (Fan J, Salathia N, Liu R, Kaeser G, Yung Y, Herman J, Kaper F, Fan JB, Zhang K, Chun J, and Kharchenko PV, Nature Methods, doi:10.1038/nmeth.3734)](http://www.nature.com/nmeth/journal/vaop/ncurrent/full/nmeth.3734.html)

**For additional installation information, tutorials, and more, please visit [the SCDE website ☞](http://hms-dbmi.github.io/scde/) and [the Bioconductor package page](https://bioconductor.org/packages/release/bioc/html/scde.html)**

**Note:** We recommend that users also refer to the package [pagoda2](https://github.com/kharchenkolab/pagoda2). While we do continue to maintain the [Bioconductor package scde](https://bioconductor.org/packages/release/bioc/html/scde.html), we don't have the bandwidth to address all bugs and feature requests reported in this repo. 

# Sample analyses and images

## Single cell error modeling

  

    

      

    

    

      scde fits individual error models for single cells using counts derived from single-cell RNA-seq data to estimate drop-out and amplification biases on gene expression magnitude.

    

  

## Differential expression analysis

  

    

      

    

    

      

             lb   mle     ub    ce     Z    cZ

Dppa5a    8.075 9.965 11.541 8.075 7.160 5.968

Pou5f1    5.357 7.208  9.178 5.357 7.160 5.968

Gm13242   5.672 7.681  9.768 5.672 7.159 5.968

Tdh       5.829 8.075 10.281 5.829 7.159 5.968

Ift46     5.435 7.366  9.217 5.435 7.150 5.968

      


      scde compares groups of single cells and tests for differential expression, taking into account variability in the single cell RNA-seq data due to drop-out and amplification biases in order to identify more robustly differentially expressed genes. 

    

  


## Pathway and gene set overdispersion analysis

  

    

       

    

    

      scde contains pagoda routines that characterize aspects of transcriptional heterogeneity in populations of single cells using pre-defined gene sets as well as 'de novo' gene sets derived from the data. Significant aspects are used to cluster cells into subpopulations. A graphical user interface can be deployed to interactively explore results. See examples from the PAGODA publication here. See analysis of the PBMC data from 10x Genomics here.

    

  

    

---

    

`scde` is maintained by [Jean Fan](https://github.com/jefworks) and [Evan Biederstedt](https://github.com/evanbiederstedt) of the [Kharchenko Lab](http://pklab.med.harvard.edu/) at the [Department of Biomedical Informatics at Harvard Medical School](https://github.com/hms-dbmi).

---

# Contributing

We welcome any bug reports, enhancement requests, and other contributions. To submit a bug report or enhancement request, please use the [`scde` GitHub issues tracker](https://github.com/hms-dbmi/scde/issues). For more substantial contributions, please fork this repo, push your changes to your fork, and submit a pull request with a good commit message. For more general discussions or troubleshooting, please consult the [`scde` Google Group](http://hms-dbmi.github.io/scde/help.html).  

## Citation

If you find `scde` useful for your publication, please cite:

```

Kharchenko P, Fan J, Biederstedt E (2023). scde: Single Cell Differential Expression. 

R package version 2.27.1, http://pklab.med.harvard.edu/scde.

```