SuperCellCyto: enabling efficient analysis of large scale cytometry datasets
Abstract Advancements in cytometry technologies have enabled quantification of up to 50 proteins across millions of cells at single cell resolution. Analysis of cytometry data routinely involves tasks such as data integration, clustering, and dimensionality reduction. While numerous tools exist, man...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2024-04-01
|
| Series: | Genome Biology |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13059-024-03229-3 |
| _version_ | 1797209297611915264 |
|---|---|
| author | Givanna H. Putri George Howitt Felix Marsh-Wakefield Thomas M. Ashhurst Belinda Phipson |
| author_facet | Givanna H. Putri George Howitt Felix Marsh-Wakefield Thomas M. Ashhurst Belinda Phipson |
| author_sort | Givanna H. Putri |
| collection | DOAJ |
| description | Abstract Advancements in cytometry technologies have enabled quantification of up to 50 proteins across millions of cells at single cell resolution. Analysis of cytometry data routinely involves tasks such as data integration, clustering, and dimensionality reduction. While numerous tools exist, many require extensive run times when processing large cytometry data containing millions of cells. Existing solutions, such as random subsampling, are inadequate as they risk excluding rare cell subsets. To address this, we propose SuperCellCyto, an R package that builds on the SuperCell tool which groups highly similar cells into supercells. SuperCellCyto is available on GitHub ( https://github.com/phipsonlab/SuperCellCyto ) and Zenodo ( https://doi.org/10.5281/zenodo.10521294 ). |
| first_indexed | 2024-04-24T09:52:28Z |
| format | Article |
| id | doaj.art-a9bd3be5af234d1f83222096912f9606 |
| institution | Directory Open Access Journal |
| issn | 1474-760X |
| language | English |
| last_indexed | 2024-04-24T09:52:28Z |
| publishDate | 2024-04-01 |
| publisher | BMC |
| record_format | Article |
| series | Genome Biology |
| spelling | doaj.art-a9bd3be5af234d1f83222096912f96062024-04-14T11:18:00ZengBMCGenome Biology1474-760X2024-04-0125112710.1186/s13059-024-03229-3SuperCellCyto: enabling efficient analysis of large scale cytometry datasetsGivanna H. Putri0George Howitt1Felix Marsh-Wakefield2Thomas M. Ashhurst3Belinda Phipson4The Walter and Eliza Hall Institute of Medical Research and The Department of Medical Biology, The University of MelbournePeter MacCallum Cancer Centre and The Sir Peter MacCallum, Department of Oncology, The University of MelbourneCentenary Institute of Cancer Medicine and Cell Biology, The University of SydneySydney Cytometry Core Research Facility and School of Medical Sciences, The University of SydneyThe Walter and Eliza Hall Institute of Medical Research and The Department of Medical Biology, The University of MelbourneAbstract Advancements in cytometry technologies have enabled quantification of up to 50 proteins across millions of cells at single cell resolution. Analysis of cytometry data routinely involves tasks such as data integration, clustering, and dimensionality reduction. While numerous tools exist, many require extensive run times when processing large cytometry data containing millions of cells. Existing solutions, such as random subsampling, are inadequate as they risk excluding rare cell subsets. To address this, we propose SuperCellCyto, an R package that builds on the SuperCell tool which groups highly similar cells into supercells. SuperCellCyto is available on GitHub ( https://github.com/phipsonlab/SuperCellCyto ) and Zenodo ( https://doi.org/10.5281/zenodo.10521294 ).https://doi.org/10.1186/s13059-024-03229-3CytometryCytofDimensionality reductionComputational analysisCITEseqSupercell |
| spellingShingle | Givanna H. Putri George Howitt Felix Marsh-Wakefield Thomas M. Ashhurst Belinda Phipson SuperCellCyto: enabling efficient analysis of large scale cytometry datasets Genome Biology Cytometry Cytof Dimensionality reduction Computational analysis CITEseq Supercell |
| title | SuperCellCyto: enabling efficient analysis of large scale cytometry datasets |
| title_full | SuperCellCyto: enabling efficient analysis of large scale cytometry datasets |
| title_fullStr | SuperCellCyto: enabling efficient analysis of large scale cytometry datasets |
| title_full_unstemmed | SuperCellCyto: enabling efficient analysis of large scale cytometry datasets |
| title_short | SuperCellCyto: enabling efficient analysis of large scale cytometry datasets |
| title_sort | supercellcyto enabling efficient analysis of large scale cytometry datasets |
| topic | Cytometry Cytof Dimensionality reduction Computational analysis CITEseq Supercell |
| url | https://doi.org/10.1186/s13059-024-03229-3 |
| work_keys_str_mv | AT givannahputri supercellcytoenablingefficientanalysisoflargescalecytometrydatasets AT georgehowitt supercellcytoenablingefficientanalysisoflargescalecytometrydatasets AT felixmarshwakefield supercellcytoenablingefficientanalysisoflargescalecytometrydatasets AT thomasmashhurst supercellcytoenablingefficientanalysisoflargescalecytometrydatasets AT belindaphipson supercellcytoenablingefficientanalysisoflargescalecytometrydatasets |