https://github.com/kharchenkolab/dropEst

Pipeline for initial analysis of droplet-based single-cell RNA-seq data
https://github.com/kharchenkolab/dropEst

pipeline preprocessing scrna-seq single-cell-rna-seq

Last synced: 23 days ago
JSON representation

Pipeline for initial analysis of droplet-based single-cell RNA-seq data

• Host: GitHub
• URL: https://github.com/kharchenkolab/dropEst
• Owner: kharchenkolab
• License: gpl-3.0
• Created: 2017-07-17T20:28:48.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2022-06-22T02:15:17.000Z (about 2 years ago)
• Last Synced: 2024-05-22T18:12:52.462Z (about 1 month ago)
• Topics: pipeline, preprocessing, scrna-seq, single-cell-rna-seq
• Language: C++
• Size: 47.1 MB
• Stars: 84
• Watchers: 15
• Forks: 43
• Open Issues: 41
• Metadata Files:
  • Readme: README.rst
  • Changelog: CHANGELOG.rst
  • License: LICENSE.md

Lists

• awesome_single_cell - dropEst - [C++, R] - High-performance pipeline for initial analysis of droplet-based single-cell RNA-seq data (Drop-seq, inDrop, 10x and some others). Allows to estimate gene count matrix as well as diagnostic stats from fastq files with raw reads. Implements corrections for different noise sources. (Software packages / Other applications)

README

        
dropEst - Pipeline

==================

Pipeline for estimating molecular count matrices for droplet-based

single-cell RNA-seq measurements. If you use the pipeline in your

research, please `cite <#citation>`__ the corresponding

`paper `__. To reproduce

results from the paper, please see `this

repository `__.

Documentation

-------------

For detailed explanations, please see the `documentation `__

Particularly:

- `Installation `__

- `Integration with Velocyto `__

If you have problems with installation, please look at the `Troubleshooting `__ page and open an `issue `__ if there is nothing.

News

----

[0.8.6] - 2019-08-01

~~~~~~~~~~~~~~~~~~~~

-  Added support for Drop-seq and CEL-Seq2

See `Changelog `__ for the full list.

General processing steps

------------------------

1. **dropTag**: extraction of cell barcodes and UMIs from the library.

   Result: demultiplexed .fastq.gz files, which should be aligned to the

   reference.

2. **Alignment** of the demultiplexed files to reference genome. Result:

   .bam files with the alignment.

3. **dropEst**: building count matrix and estimation of some statistics,

   necessary for quality control. Result: .rds file with the count

   matrix and statistics. *Optionally: count matrix in MatrixMarket

   format.*

4. **dropReport** - Generating report on library quality.

5.  `dropEstR `__ - R pacakge for UMI count corrections and cell quality classification

Examples

--------

Complete examples of the pipeline can be found at

`EXAMPLES.md `__.

`Here `__

are results of processing of

`neurons\_900 `__

10x dataset.

Supported protocols

-------------------

- 10x

- CEL-Seq2

- Drop-seq

- iCLIP

- inDrop (v1-3)

- Seq-Well

- SPLiT-seq

Citation

--------

If you find this pipeline useful for your research, please consider citing the paper:

Petukhov, V., Guo, J., Baryawno, N., Severe, N., Scadden, D. T.,

Samsonova, M. G., & Kharchenko, P. V. (2018). dropEst: pipeline for

accurate estimation of molecular counts in droplet-based single-cell

RNA-seq experiments. Genome biology, 19(1), 78.

doi:10.1186/s13059-018-1449-6