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gExcite: a start-to-end framework for single-cell gene expression, hashing, and antibody analysis
SUMMARY: 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 profi...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10229235/ https://www.ncbi.nlm.nih.gov/pubmed/37220897 http://dx.doi.org/10.1093/bioinformatics/btad329 |
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author | Grob, Linda Bertolini, Anne Carrara, Matteo Lischetti, Ulrike Tastanova, Aizhan Beisel, Christian Levesque, Mitchell P Stekhoven, Daniel J Singer, Franziska |
author_facet | Grob, Linda Bertolini, Anne Carrara, Matteo Lischetti, Ulrike Tastanova, Aizhan Beisel, Christian Levesque, Mitchell P Stekhoven, Daniel J Singer, Franziska |
author_sort | Grob, Linda |
collection | PubMed |
description | SUMMARY: 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. AVAILABILITY AND IMPLEMENTATION: 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). |
format | Online Article Text |
id | pubmed-10229235 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-102292352023-05-31 gExcite: a start-to-end framework for single-cell gene expression, hashing, and antibody analysis Grob, Linda Bertolini, Anne Carrara, Matteo Lischetti, Ulrike Tastanova, Aizhan Beisel, Christian Levesque, Mitchell P Stekhoven, Daniel J Singer, Franziska Bioinformatics Applications Note SUMMARY: 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. AVAILABILITY AND IMPLEMENTATION: 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). Oxford University Press 2023-05-23 /pmc/articles/PMC10229235/ /pubmed/37220897 http://dx.doi.org/10.1093/bioinformatics/btad329 Text en © The Author(s) 2023. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Applications Note Grob, Linda Bertolini, Anne Carrara, Matteo Lischetti, Ulrike Tastanova, Aizhan Beisel, Christian Levesque, Mitchell P Stekhoven, Daniel J Singer, Franziska gExcite: a start-to-end framework for single-cell gene expression, hashing, and antibody analysis |
title | gExcite: a start-to-end framework for single-cell gene expression, hashing, and antibody analysis |
title_full | gExcite: a start-to-end framework for single-cell gene expression, hashing, and antibody analysis |
title_fullStr | gExcite: a start-to-end framework for single-cell gene expression, hashing, and antibody analysis |
title_full_unstemmed | gExcite: a start-to-end framework for single-cell gene expression, hashing, and antibody analysis |
title_short | gExcite: a start-to-end framework for single-cell gene expression, hashing, and antibody analysis |
title_sort | gexcite: a start-to-end framework for single-cell gene expression, hashing, and antibody analysis |
topic | Applications Note |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10229235/ https://www.ncbi.nlm.nih.gov/pubmed/37220897 http://dx.doi.org/10.1093/bioinformatics/btad329 |
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