Cell Type Differentiation Using Network Clustering Algorithms
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| I publikationen: | bioRxiv (Dec 7, 2024) |
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| Huvudupphov: | |
| Övriga upphov: | , , , , , , , , , |
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Cold Spring Harbor Laboratory Press
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| Länkar: | Citation/Abstract Full Text - PDF Full text outside of ProQuest |
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| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3141971908 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2692-8205 | ||
| 024 | 7 | |a 10.1101/2024.12.04.626793 |2 doi | |
| 035 | |a 3141971908 | ||
| 045 | 0 | |b d20241207 | |
| 100 | 1 | |a Fatemeh Sadat Fatemi Nasrollahi | |
| 245 | 1 | |a Cell Type Differentiation Using Network Clustering Algorithms | |
| 260 | |b Cold Spring Harbor Laboratory Press |c Dec 7, 2024 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a Single cell RNA-seq (scRNA-seq) technologies provide unprecedented resolution representing transcriptomics at the level of single cell. One of the biggest challenges in scRNA-seq data analysis is the cell type annotation, which is usually inferred by cell separation approaches. In-silico algorithms that accurately identify individual cell types in ongoing single-cell sequencing studies are crucial for unlocking cellular heterogeneity and understanding the biological basis of diseases. In this study, we focus on robustly identifying cell types in single-cell RNA sequencing data; we conduct a comparative analysis using methods established in biology, like Seurat, Leiden, and WGCNA, as well as Infomap, statistical inference via Stochastic Block Models (SBM), and single-cell Graph Neural Networks (scGNN). We also analyze preprocessing pipelines to identify and optimize key components in the process. Leveraging two independent datasets, PBMC and ROSMAP, we employ clustering algorithms on cell-cell networks derived from gene expression data. Our findings reveal that while clusters detected by WGCNA exhibit limited correspondence with cell types, those identified by multiresolution Infomap and Leiden, and SBM show a closer alignment, with Infomap standing out as a particularly effective approach. Infomap notably offers valuable insights for the precise characterization of cellular landscapes related to neurodegenration and immunology in scRNA-seq.Competing Interest StatementThe authors have declared no competing interest. | |
| 653 | |a Data processing | ||
| 653 | |a Comparative analysis | ||
| 653 | |a Cell differentiation | ||
| 653 | |a Gene expression | ||
| 653 | |a Transcriptomics | ||
| 653 | |a Algorithms | ||
| 653 | |a Peripheral blood mononuclear cells | ||
| 653 | |a Cell culture | ||
| 653 | |a Statistical models | ||
| 653 | |a Neural networks | ||
| 700 | 1 | |a Filipi Nascimento Silva | |
| 700 | 1 | |a Liu, Shiwei | |
| 700 | 1 | |a Chaudhuri, Soumilee | |
| 700 | 1 | |a Yu, Meichen | |
| 700 | 1 | |a Wang, Juexin | |
| 700 | 1 | |a Nho, Kwangsik | |
| 700 | 1 | |a Saykin, Andrew J | |
| 700 | 1 | |a Bennett, David A | |
| 700 | 1 | |a Sporns, Olaf | |
| 700 | 1 | |a Fortunato, Santo | |
| 773 | 0 | |t bioRxiv |g (Dec 7, 2024) | |
| 786 | 0 | |d ProQuest |t Biological Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3141971908/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3141971908/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u https://www.biorxiv.org/content/10.1101/2024.12.04.626793v1 |