Scdrake: a reproducible and scalable pipeline for scRNA-seq data analysis

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68378050%3A_____%2F23%3A00574419" target="_blank" >RIV/68378050:_____/23:00574419 - isvavai.cz</a>

Výsledek na webu

<a href="https://academic.oup.com/bioinformaticsadvances/article/3/1/vbad089/7220500?login=true" target="_blank" >https://academic.oup.com/bioinformaticsadvances/article/3/1/vbad089/7220500?login=true</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1093/bioadv/vbad089" target="_blank" >10.1093/bioadv/vbad089</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Scdrake: a reproducible and scalable pipeline for scRNA-seq data analysis

Popis výsledku v původním jazyce

Motivation: While the workflow for primary analysis of single-cell RNA-seq (scRNA-seq) data is well established, the secondary analysis of the feature-barcode matrix is usually done by custom scripts. There is no fully automated pipeline in the R statistical environment, which would follow the current best programming practices and requirements for reproducibility. Results: We have developed scdrake, a fully automated workflow for secondary analysis of scRNA-seq data, which is fully implemented in the R language and built within the drake framework. The pipeline includes quality control, cell and gene filtering, normalization, detection of highly variable genes, dimensionality reduction, clustering, cell type annotation, detection of marker genes, differential expression analysis and integration of multiple samples. The pipeline is reproducible and scalable, has an efficient execution, provides easy extendability and access to intermediate results and outputs rich HTML reports. Scdrake is distributed as a Docker image, which provides a straightforward setup and enhances reproducibility.

Název v anglickém jazyce

Scdrake: a reproducible and scalable pipeline for scRNA-seq data analysis

Popis výsledku anglicky

Motivation: While the workflow for primary analysis of single-cell RNA-seq (scRNA-seq) data is well established, the secondary analysis of the feature-barcode matrix is usually done by custom scripts. There is no fully automated pipeline in the R statistical environment, which would follow the current best programming practices and requirements for reproducibility. Results: We have developed scdrake, a fully automated workflow for secondary analysis of scRNA-seq data, which is fully implemented in the R language and built within the drake framework. The pipeline includes quality control, cell and gene filtering, normalization, detection of highly variable genes, dimensionality reduction, clustering, cell type annotation, detection of marker genes, differential expression analysis and integration of multiple samples. The pipeline is reproducible and scalable, has an efficient execution, provides easy extendability and access to intermediate results and outputs rich HTML reports. Scdrake is distributed as a Docker image, which provides a straightforward setup and enhances reproducibility.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10608 - Biochemistry and molecular biology

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Bioinformatics Advances

ISSN

2635-0041

e-ISSN

2635-0041

Svazek periodika

3

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

3

Strana od-do

vbad089

Kód UT WoS článku

001124263600123

EID výsledku v databázi Scopus

2-s2.0-85166430412