Sciviewer enables interactive visual interrogation of single-cell RNA-Seq data from the Python programming environment Article Swipe
Dylan Kotliar
,
Andrés Colubri
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1093/bioinformatics/btab689
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1093/bioinformatics/btab689
Motivation Visualizing two-dimensional embeddings (such as UMAP or tSNE) is a useful step in interrogating single-cell RNA sequencing (scRNA-Seq) data. Subsequently, users typically iterate between programmatic analyses (including clustering and differential expression) and visual exploration (e.g. coloring cells by interesting features) to uncover biological signals in the data. Interactive tools exist to facilitate visual exploration of embeddings such as performing differential expression on user-selected cells. However, the practical utility of these tools is limited because they don’t support rapid movement of data and results to and from the programming environments where most of the data analysis takes place, interrupting the iterative process. Results Here, we present the Single-cell Interactive Viewer (Sciviewer), a tool that overcomes this limitation by allowing interactive visual interrogation of embeddings from within Python. Beyond differential expression analysis of user-selected cells, Sciviewer implements a novel method to identify genes varying locally along any user-specified direction on the embedding. Sciviewer enables rapid and flexible iteration between interactive and programmatic modes of scRNA-Seq exploration, illustrating a useful approach for analyzing high-dimensional data. Availability and implementation Code and examples are provided at https://github.com/colabobio/sciviewer.
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- Language
- en
- Landing Page
- https://doi.org/10.1093/bioinformatics/btab689
- OA Status
- green
- Cited By
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- References
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- Related Works
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- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W3201917654Canonical identifier for this work in OpenAlex
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https://doi.org/10.1093/bioinformatics/btab689Digital Object Identifier
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Sciviewer enables interactive visual interrogation of single-cell RNA-Seq data from the Python programming environmentWork title
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articleOpenAlex work type
- Language
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enPrimary language
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2021Year of publication
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2021-09-30Full publication date if available
- Authors
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Dylan Kotliar, Andrés ColubriList of authors in order
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https://doi.org/10.1093/bioinformatics/btab689Publisher landing page
- Open access
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://pmc.ncbi.nlm.nih.gov/articles/PMC10186091/pdf/btab689.pdfDirect OA link when available
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Python (programming language), Computer science, Embedding, Cluster analysis, Interrogation, Source code, Programming language, Data mining, Artificial intelligence, History, ArchaeologyTop concepts (fields/topics) attached by OpenAlex
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3Total citation count in OpenAlex
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2025: 1, 2024: 1, 2023: 1Per-year citation counts (last 5 years)
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| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | Text |
| best_oa_location.license_id | |
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| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | Bioinformatics |
| best_oa_location.landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/10186091 |
| primary_location.id | doi:10.1093/bioinformatics/btab689 |
| primary_location.is_oa | False |
| primary_location.source.id | https://openalex.org/S52395412 |
| primary_location.source.issn | 1367-4803, 1367-4811 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | 1367-4803 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Bioinformatics |
| primary_location.source.host_organization | https://openalex.org/P4310311648 |
| primary_location.source.host_organization_name | Oxford University Press |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310311648, https://openalex.org/P4310311647 |
| primary_location.source.host_organization_lineage_names | Oxford University Press, University of Oxford |
| primary_location.license | |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Bioinformatics |
| primary_location.landing_page_url | https://doi.org/10.1093/bioinformatics/btab689 |
| publication_date | 2021-09-30 |
| publication_year | 2021 |
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