Reference-free cell type deconvolution of multi-cellular pixel-resolution spatially resolved transcriptomics data
Identifying cell-type-specific spatial patterns in ST data is critical for understanding tissue organization but current methods rely on external references. Here the authors develop a reference-free method to effectively recover cell-type transcriptional profiles and proportions.
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2022-04-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-022-30033-z |
| _version_ | 1828370724593598464 |
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| author | Brendan F. Miller Feiyang Huang Lyla Atta Arpan Sahoo Jean Fan |
| author_facet | Brendan F. Miller Feiyang Huang Lyla Atta Arpan Sahoo Jean Fan |
| author_sort | Brendan F. Miller |
| collection | DOAJ |
| description | Identifying cell-type-specific spatial patterns in ST data is critical for understanding tissue organization but current methods rely on external references. Here the authors develop a reference-free method to effectively recover cell-type transcriptional profiles and proportions. |
| first_indexed | 2024-04-14T06:41:03Z |
| format | Article |
| id | doaj.art-8169b376adb24eae99a8f90a6c88b8d9 |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-04-14T06:41:03Z |
| publishDate | 2022-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-8169b376adb24eae99a8f90a6c88b8d92022-12-22T02:07:19ZengNature PortfolioNature Communications2041-17232022-04-0113111310.1038/s41467-022-30033-zReference-free cell type deconvolution of multi-cellular pixel-resolution spatially resolved transcriptomics dataBrendan F. Miller0Feiyang Huang1Lyla Atta2Arpan Sahoo3Jean Fan4Center for Computational Biology, Whiting School of Engineering, Johns Hopkins UniversityCenter for Computational Biology, Whiting School of Engineering, Johns Hopkins UniversityCenter for Computational Biology, Whiting School of Engineering, Johns Hopkins UniversityCenter for Computational Biology, Whiting School of Engineering, Johns Hopkins UniversityCenter for Computational Biology, Whiting School of Engineering, Johns Hopkins UniversityIdentifying cell-type-specific spatial patterns in ST data is critical for understanding tissue organization but current methods rely on external references. Here the authors develop a reference-free method to effectively recover cell-type transcriptional profiles and proportions.https://doi.org/10.1038/s41467-022-30033-z |
| spellingShingle | Brendan F. Miller Feiyang Huang Lyla Atta Arpan Sahoo Jean Fan Reference-free cell type deconvolution of multi-cellular pixel-resolution spatially resolved transcriptomics data Nature Communications |
| title | Reference-free cell type deconvolution of multi-cellular pixel-resolution spatially resolved transcriptomics data |
| title_full | Reference-free cell type deconvolution of multi-cellular pixel-resolution spatially resolved transcriptomics data |
| title_fullStr | Reference-free cell type deconvolution of multi-cellular pixel-resolution spatially resolved transcriptomics data |
| title_full_unstemmed | Reference-free cell type deconvolution of multi-cellular pixel-resolution spatially resolved transcriptomics data |
| title_short | Reference-free cell type deconvolution of multi-cellular pixel-resolution spatially resolved transcriptomics data |
| title_sort | reference free cell type deconvolution of multi cellular pixel resolution spatially resolved transcriptomics data |
| url | https://doi.org/10.1038/s41467-022-30033-z |
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