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

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

Other Quantitative Biology 2024-06-11 v1 Other Statistics

Authors: Megha Patel , Nimish Magre , Himanshi Motwani , Nik Bear Brown

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.

Keywords

rna structure and analysis genome sequencing analysis gene expression analysis

Cite

@article{arxiv.2406.05258,
  title  = {Advances in Machine Learning, Statistical Methods, and AI for Single-Cell RNA Annotation Using Raw Count Matrices in scRNA-seq Data},
  author = {Megha Patel and Nimish Magre and Himanshi Motwani and Nik Bear Brown},
  journal= {arXiv preprint arXiv:2406.05258},
  year   = {2024}
}

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A survey of best practices for using machine learning, statistical methods, and AI for Single-Cell RNA annotation using raw count matrices in scRNA-seq data

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

Abstract

Keywords

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