Processing single-cell RNA-seq datasets using SingCellaR
Single-cell RNA sequencing has led to unprecedented levels of data complexity. Although several computational platforms are available, performing data analyses for multiple datasets remains a significant challenge. Here, we provide a comprehensive analytical protocol to interrogate multiple datasets...
| 主要な著者: | , , , , , |
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| フォーマット: | Journal article |
| 言語: | English |
| 出版事項: |
Cell Press
2022
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| _version_ | 1826309421231243264 |
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| author | Wang, G Wen, WX Mead, AJ Roy, A Psaila, B Thongjuea, S |
| author_facet | Wang, G Wen, WX Mead, AJ Roy, A Psaila, B Thongjuea, S |
| author_sort | Wang, G |
| collection | OXFORD |
| description | Single-cell RNA sequencing has led to unprecedented levels of data complexity. Although several computational platforms are available, performing data analyses for multiple datasets remains a significant challenge. Here, we provide a comprehensive analytical protocol to interrogate multiple datasets on SingCellaR, an analysis package in R. This tool can be applied to general single-cell transcriptome analyses. We demonstrate steps for data analyses and visualization using bespoke pipelines, in conjunction with existing analysis tools to study human hematopoietic stem and progenitor cells. For complete details on the use and execution of this protocol, please refer to Roy et al. (2021). |
| first_indexed | 2024-03-07T07:35:27Z |
| format | Journal article |
| id | oxford-uuid:c6ae6a8f-a9de-40c7-ab77-1408df1a9e56 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T07:35:27Z |
| publishDate | 2022 |
| publisher | Cell Press |
| record_format | dspace |
| spelling | oxford-uuid:c6ae6a8f-a9de-40c7-ab77-1408df1a9e562023-02-27T15:53:40ZProcessing single-cell RNA-seq datasets using SingCellaRJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:c6ae6a8f-a9de-40c7-ab77-1408df1a9e56EnglishSymplectic ElementsCell Press2022Wang, GWen, WXMead, AJRoy, APsaila, BThongjuea, SSingle-cell RNA sequencing has led to unprecedented levels of data complexity. Although several computational platforms are available, performing data analyses for multiple datasets remains a significant challenge. Here, we provide a comprehensive analytical protocol to interrogate multiple datasets on SingCellaR, an analysis package in R. This tool can be applied to general single-cell transcriptome analyses. We demonstrate steps for data analyses and visualization using bespoke pipelines, in conjunction with existing analysis tools to study human hematopoietic stem and progenitor cells. For complete details on the use and execution of this protocol, please refer to Roy et al. (2021). |
| spellingShingle | Wang, G Wen, WX Mead, AJ Roy, A Psaila, B Thongjuea, S Processing single-cell RNA-seq datasets using SingCellaR |
| title | Processing single-cell RNA-seq datasets using SingCellaR |
| title_full | Processing single-cell RNA-seq datasets using SingCellaR |
| title_fullStr | Processing single-cell RNA-seq datasets using SingCellaR |
| title_full_unstemmed | Processing single-cell RNA-seq datasets using SingCellaR |
| title_short | Processing single-cell RNA-seq datasets using SingCellaR |
| title_sort | processing single cell rna seq datasets using singcellar |
| work_keys_str_mv | AT wangg processingsinglecellrnaseqdatasetsusingsingcellar AT wenwx processingsinglecellrnaseqdatasetsusingsingcellar AT meadaj processingsinglecellrnaseqdatasetsusingsingcellar AT roya processingsinglecellrnaseqdatasetsusingsingcellar AT psailab processingsinglecellrnaseqdatasetsusingsingcellar AT thongjueas processingsinglecellrnaseqdatasetsusingsingcellar |