Advances in Machine Learning, Statistical Methods, and AI for Single-Cell RNA Annotation Using Raw Count Matrices in scRNA-seq Data

Megha Patel; Nimish Magre; Himanshi Motwani; Nik Bear Brown

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arXiv · 2406.05258

Advances in Machine Learning, Statistical Methods, and AI for Single-Cell RNA Annotation Using Raw Count Matrices in scRNA-seq Data

Megha Patel·Nimish Magre·Himanshi Motwani·Nik Bear Brown

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Abstract

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.

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Megha Patel, Nimish Magre, Himanshi Motwani, Nik Bear Brown. 2024-06-07. Advances in Machine Learning, Statistical Methods, and AI for Single-Cell RNA Annotation Using Raw Count Matrices in scRNA-seq Data. https://arxiv.org/abs/2406.05258

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