gExcite: a start-to-end framework for single-cell gene expression, hashing, and antibody analysis.
Journal
Bioinformatics (Oxford, England)
ISSN: 1367-4811
Titre abrégé: Bioinformatics
Pays: England
ID NLM: 9808944
Informations de publication
Date de publication:
04 05 2023
04 05 2023
Historique:
received:
13
12
2022
revised:
28
03
2023
medline:
21
7
2023
pubmed:
24
5
2023
entrez:
23
5
2023
Statut:
ppublish
Résumé
Recently, CITE-seq emerged as a multimodal single-cell technology capturing gene expression and surface protein information from the same single cells, which allows unprecedented insights into disease mechanisms and heterogeneity, as well as immune cell profiling. Multiple single-cell profiling methods exist, but they are typically focused on either gene expression or antibody analysis, not their combination. Moreover, existing software suites are not easily scalable to a multitude of samples. To this end, we designed gExcite, a start-to-end workflow that provides both gene and antibody expression analysis, as well as hashing deconvolution. Embedded in the Snakemake workflow manager, gExcite facilitates reproducible and scalable analyses. We showcase the output of gExcite on a study of different dissociation protocols on PBMC samples. gExcite is open source available on github at https://github.com/ETH-NEXUS/gExcite_pipeline. The software is distributed under the GNU General Public License 3 (GPL3).
Identifiants
pubmed: 37220897
pii: 7176365
doi: 10.1093/bioinformatics/btad329
pmc: PMC10229235
pii:
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© The Author(s) 2023. Published by Oxford University Press.