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Fast model-free standardization and integration of single-cell transcriptomics data
Single-cell transcriptomics datasets from the same anatomical sites generated by different research labs are becoming increasingly common. However, fast and computationally inexpensive tools for standardization of cell-type annotation and data integration are still needed in order to increase resear...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Journal Experts
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9901035/ https://www.ncbi.nlm.nih.gov/pubmed/36747625 http://dx.doi.org/10.21203/rs.3.rs-2485985/v1 |
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author | Xu, Yang Kramann, Rafael McCord, Rachel Patton Hayat, Sikander |
author_facet | Xu, Yang Kramann, Rafael McCord, Rachel Patton Hayat, Sikander |
author_sort | Xu, Yang |
collection | PubMed |
description | Single-cell transcriptomics datasets from the same anatomical sites generated by different research labs are becoming increasingly common. However, fast and computationally inexpensive tools for standardization of cell-type annotation and data integration are still needed in order to increase research inclusivity. To standardize cell-type annotation and integrate single-cell transcriptomics datasets, we have built a fast model-free integration method, named MASI (Marker-Assisted Standardization and Integration). MASI first identifies putative cell-type markers from reference data through an ensemble approach. Then, it converts gene expression matrix to cell-type score matrix with the identified putative cell-type markers for the purpose of cell-type annotation and data integration. Because of integration through cell-type markers instead of model inference, MASI can annotate approximately one million cells on a personal laptop, which provides a cheap computational alternative for the single-cell community. We benchmark MASI with other well-established methods and demonstrate that MASI outperforms other methods based on speed. Its performance for both tasks of data integration and cell-type annotation are comparable or even superior to these existing methods. To harness knowledge from single-cell atlases, we demonstrate three case studies that cover integration across biological conditions, surveyed participants, and research groups, respectively. |
format | Online Article Text |
id | pubmed-9901035 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | American Journal Experts |
record_format | MEDLINE/PubMed |
spelling | pubmed-99010352023-02-07 Fast model-free standardization and integration of single-cell transcriptomics data Xu, Yang Kramann, Rafael McCord, Rachel Patton Hayat, Sikander Res Sq Article Single-cell transcriptomics datasets from the same anatomical sites generated by different research labs are becoming increasingly common. However, fast and computationally inexpensive tools for standardization of cell-type annotation and data integration are still needed in order to increase research inclusivity. To standardize cell-type annotation and integrate single-cell transcriptomics datasets, we have built a fast model-free integration method, named MASI (Marker-Assisted Standardization and Integration). MASI first identifies putative cell-type markers from reference data through an ensemble approach. Then, it converts gene expression matrix to cell-type score matrix with the identified putative cell-type markers for the purpose of cell-type annotation and data integration. Because of integration through cell-type markers instead of model inference, MASI can annotate approximately one million cells on a personal laptop, which provides a cheap computational alternative for the single-cell community. We benchmark MASI with other well-established methods and demonstrate that MASI outperforms other methods based on speed. Its performance for both tasks of data integration and cell-type annotation are comparable or even superior to these existing methods. To harness knowledge from single-cell atlases, we demonstrate three case studies that cover integration across biological conditions, surveyed participants, and research groups, respectively. American Journal Experts 2023-01-23 /pmc/articles/PMC9901035/ /pubmed/36747625 http://dx.doi.org/10.21203/rs.3.rs-2485985/v1 Text en https://creativecommons.org/licenses/by/4.0/This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/) , which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use. https://creativecommons.org/licenses/by/4.0/License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License (https://creativecommons.org/licenses/by/4.0/) |
spellingShingle | Article Xu, Yang Kramann, Rafael McCord, Rachel Patton Hayat, Sikander Fast model-free standardization and integration of single-cell transcriptomics data |
title | Fast model-free standardization and integration of single-cell transcriptomics data |
title_full | Fast model-free standardization and integration of single-cell transcriptomics data |
title_fullStr | Fast model-free standardization and integration of single-cell transcriptomics data |
title_full_unstemmed | Fast model-free standardization and integration of single-cell transcriptomics data |
title_short | Fast model-free standardization and integration of single-cell transcriptomics data |
title_sort | fast model-free standardization and integration of single-cell transcriptomics data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9901035/ https://www.ncbi.nlm.nih.gov/pubmed/36747625 http://dx.doi.org/10.21203/rs.3.rs-2485985/v1 |
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