Advances in Machine Learning, Statistical Methods, and AI for Single-Cell RNA Annotation Using Raw Count Matrices in scRNA-seq Data
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866914829907787776 |
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| author | Patel, Megha Magre, Nimish Motwani, Himanshi Brown, Nik Bear |
| author_facet | Patel, Megha Magre, Nimish Motwani, Himanshi Brown, Nik Bear |
| contents | Single-cell RNA sequencing (scRNA-seq) has revolutionized our ability to analyze gene expression at the resolution of individual cells, providing unprecedented insights into cellular heterogeneity and complex biological systems. This paper reviews various advanced computational and machine learning techniques tailored for the analysis of scRNA-seq data, emphasizing their roles in different stages of the data processing pipeline. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_05258 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Advances in Machine Learning, Statistical Methods, and AI for Single-Cell RNA Annotation Using Raw Count Matrices in scRNA-seq Data Patel, Megha Magre, Nimish Motwani, Himanshi Brown, Nik Bear Other Quantitative Biology Other Statistics 92C42, 62H30, 68T10 I.2.7; J.3; H.2.8 Single-cell RNA sequencing (scRNA-seq) has revolutionized our ability to analyze gene expression at the resolution of individual cells, providing unprecedented insights into cellular heterogeneity and complex biological systems. This paper reviews various advanced computational and machine learning techniques tailored for the analysis of scRNA-seq data, emphasizing their roles in different stages of the data processing pipeline. |
| title | Advances in Machine Learning, Statistical Methods, and AI for Single-Cell RNA Annotation Using Raw Count Matrices in scRNA-seq Data |
| topic | Other Quantitative Biology Other Statistics 92C42, 62H30, 68T10 I.2.7; J.3; H.2.8 |
| url | https://arxiv.org/abs/2406.05258 |