A Meta-learning based Graph-Hierarchical Clustering Method for Single Cell RNA-Seq Data Article Swipe
Zixiang Pan
,
Yuefan Lin
,
Haokun Zhang
,
Yuansong Zeng
,
Weijiang Yu
,
Yuedong Yang
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1101/2022.09.06.506784
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1101/2022.09.06.506784
Single cell sequencing techniques enable researchers view complex bio-tissues from a more precise perspective to identify cell types. However, more and more recent works have been done to find more detailed subtypes within already known cell types. Here, we present MeHi-SCC, a method which utilized meta-learning protocol and brought in multi scRNA-seq datasets’ information in order to assist graph-based hierarchical sub-clustering process. In result, MeHi-SCC outperformed current-prevailing scRNA clustering methods and successfully identified cell subtypes in two large scale cell atlas. Our codes and datasets are available online at https://github.com/biomed-AI/MeHi-SCC
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- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2022.09.06.506784
- https://www.biorxiv.org/content/biorxiv/early/2022/11/09/2022.09.06.506784.full.pdf
- OA Status
- green
- Cited By
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- References
- 48
- Related Works
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- OpenAlex ID
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Raw OpenAlex JSON
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- Title
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A Meta-learning based Graph-Hierarchical Clustering Method for Single Cell RNA-Seq DataWork title
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preprintOpenAlex work type
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enPrimary language
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2022Year of publication
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2022-09-08Full publication date if available
- Authors
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Zixiang Pan, Yuefan Lin, Haokun Zhang, Yuansong Zeng, Weijiang Yu, Yuedong YangList of authors in order
- Landing page
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https://doi.org/10.1101/2022.09.06.506784Publisher landing page
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https://www.biorxiv.org/content/biorxiv/early/2022/11/09/2022.09.06.506784.full.pdfDirect link to full text PDF
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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://www.biorxiv.org/content/biorxiv/early/2022/11/09/2022.09.06.506784.full.pdfDirect OA link when available
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Cluster analysis, Computer science, Hierarchical clustering, Graph, Perspective (graphical), Atlas (anatomy), Data mining, Computational biology, Artificial intelligence, Theoretical computer science, Biology, PaleontologyTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2024: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2007439698, https://openalex.org/W2056118660, https://openalex.org/W2102212449, https://openalex.org/W2793965282, https://openalex.org/W2747545374, https://openalex.org/W2158591705, https://openalex.org/W3043633702, https://openalex.org/W3162788683, https://openalex.org/W2951381561, https://openalex.org/W3002417351, https://openalex.org/W3045864178, https://openalex.org/W3164315209, https://openalex.org/W1918870341, https://openalex.org/W2889326414, https://openalex.org/W3140605903, https://openalex.org/W4226356561, https://openalex.org/W4205126801, https://openalex.org/W3183323060, https://openalex.org/W2937917790, https://openalex.org/W2173649752, https://openalex.org/W2741943936, https://openalex.org/W3127011314, https://openalex.org/W4213136259, https://openalex.org/W4205916264, https://openalex.org/W2949177718, https://openalex.org/W2778345128, https://openalex.org/W2952303649, https://openalex.org/W3095270930, https://openalex.org/W4287854777, https://openalex.org/W4211244138, https://openalex.org/W3203511368, https://openalex.org/W2970971581, https://openalex.org/W2792056048, https://openalex.org/W4285723986, https://openalex.org/W3080555959, https://openalex.org/W6779961489, https://openalex.org/W3137406645, https://openalex.org/W1522301498, https://openalex.org/W2101234009, https://openalex.org/W2128728535, https://openalex.org/W2800392236, https://openalex.org/W2895456557, https://openalex.org/W3048884493, https://openalex.org/W3153293222, https://openalex.org/W3049293761, https://openalex.org/W3189893840, https://openalex.org/W3108714922, https://openalex.org/W2857888574 |
| referenced_works_count | 48 |
| abstract_inverted_index.a | 10, 41 |
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| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 90 |
| corresponding_author_ids | https://openalex.org/A5055989750, https://openalex.org/A5023539493 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 6 |
| corresponding_institution_ids | https://openalex.org/I157773358 |
| citation_normalized_percentile.value | 0.44634883 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |