SWANS: A highly configurable analysis pipeline for single-cell and single-nucleus RNA-sequencing data Article Swipe
Katherine Beigel
,
Eric Wafula
,
D J Mitchell
,
Steven Pastor
,
Michelle N. Gong
,
Robert O. Heuckeroth
,
Julio C. Ricarte‐Filho
,
Aime T. Franco
,
Erin R. Reichenberger
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1101/2025.05.14.654073
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1101/2025.05.14.654073
Background Single-cell RNA sequencing (scRNA-seq) is a powerful technique that enables the analysis of gene expression at the individual cell level. Bioinformatic tools for scRNA-seq data analysis have many different options throughout the typical scRNA-seq workflow (normalization, integration, annotation, clustering, and visualization), and the choice of method(s) and parameter(s) at each stage can impact results. Results Here, we introduce SWANS (v2.0), a configurable analysis pipeline that, in a single run, can employ multiple analysis methods, resolutions, and modifiable parameters. The resulting clustering arrangements, differential gene expression results, and other quantitative measurements can be dynamically visualized and compared in a Shiny interactive report to assist in choosing a single analysis schema for annotation and downstream analysis. Once a final approach is chosen, SWANS will perform differential gene expression (DGE) analysis based on experimental conditions and gene set enrichment analysis (GSEA) in addition to creating reports that display figures and interactive tables, quality control metrics, and benchmarking information. SWANS uses Snakemake as a workflow manager, Cell Ranger for alignment and gene expression quantification, Seurat for single cell data analysis, and additional single cell R packages for quality control and downstream single cell analysis. Conclusion SWANS is a tailorable pipeline that provides options for quality control, dimensionality reduction, clustering, differential gene expression analysis, gene set enrichment analysis, and trajectory analysis. Additionally, SWANS generates a series of reports that facilitate sharing large volumes of complex data in a clear and concise manner with other investigators.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2025.05.14.654073
- https://www.biorxiv.org/content/biorxiv/early/2025/05/19/2025.05.14.654073.full.pdf
- OA Status
- green
- Cited By
- 1
- References
- 41
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- 10
- OpenAlex ID
- https://openalex.org/W4410499377
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4410499377Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2025.05.14.654073Digital Object Identifier
- Title
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SWANS: A highly configurable analysis pipeline for single-cell and single-nucleus RNA-sequencing dataWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-05-19Full publication date if available
- Authors
-
Katherine Beigel, Eric Wafula, D J Mitchell, Steven Pastor, Michelle N. Gong, Robert O. Heuckeroth, Julio C. Ricarte‐Filho, Aime T. Franco, Erin R. ReichenbergerList of authors in order
- Landing page
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https://doi.org/10.1101/2025.05.14.654073Publisher landing page
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https://www.biorxiv.org/content/biorxiv/early/2025/05/19/2025.05.14.654073.full.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://www.biorxiv.org/content/biorxiv/early/2025/05/19/2025.05.14.654073.full.pdfDirect OA link when available
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Pipeline (software), Computer science, Computational biology, Biology, Programming languageTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.scRNA-seq | 25, 35 |
| abstract_inverted_index.technique | 9 |
| abstract_inverted_index.Background | 1 |
| abstract_inverted_index.Conclusion | 192 |
| abstract_inverted_index.additional | 179 |
| abstract_inverted_index.annotation | 112 |
| abstract_inverted_index.clustering | 82 |
| abstract_inverted_index.conditions | 133 |
| abstract_inverted_index.downstream | 114, 188 |
| abstract_inverted_index.enrichment | 137, 213 |
| abstract_inverted_index.expression | 16, 86, 127, 170, 209 |
| abstract_inverted_index.facilitate | 226 |
| abstract_inverted_index.individual | 19 |
| abstract_inverted_index.modifiable | 78 |
| abstract_inverted_index.reduction, | 205 |
| abstract_inverted_index.sequencing | 4 |
| abstract_inverted_index.tailorable | 196 |
| abstract_inverted_index.throughout | 32 |
| abstract_inverted_index.trajectory | 216 |
| abstract_inverted_index.visualized | 95 |
| abstract_inverted_index.(scRNA-seq) | 5 |
| abstract_inverted_index.Single-cell | 2 |
| abstract_inverted_index.annotation, | 39 |
| abstract_inverted_index.clustering, | 40, 206 |
| abstract_inverted_index.dynamically | 94 |
| abstract_inverted_index.interactive | 101, 149 |
| abstract_inverted_index.parameters. | 79 |
| abstract_inverted_index.benchmarking | 155 |
| abstract_inverted_index.configurable | 63 |
| abstract_inverted_index.differential | 84, 125, 207 |
| abstract_inverted_index.experimental | 132 |
| abstract_inverted_index.information. | 156 |
| abstract_inverted_index.integration, | 38 |
| abstract_inverted_index.measurements | 91 |
| abstract_inverted_index.parameter(s) | 49 |
| abstract_inverted_index.quantitative | 90 |
| abstract_inverted_index.resolutions, | 76 |
| abstract_inverted_index.Additionally, | 218 |
| abstract_inverted_index.Bioinformatic | 22 |
| abstract_inverted_index.arrangements, | 83 |
| abstract_inverted_index.dimensionality | 204 |
| abstract_inverted_index.investigators. | 241 |
| abstract_inverted_index.(normalization, | 37 |
| abstract_inverted_index.quantification, | 171 |
| abstract_inverted_index.visualization), | 42 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 9 |
| citation_normalized_percentile.value | 0.80181055 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |