利用 scRNA-seq 数据中的原始计数矩阵进行单细胞 RNA 注释的机器学习、统计方法和人工智能研究进展

arXiv - STAT - Other Statistics Pub Date : 2024-06-07 DOI:arxiv-2406.05258

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

{"title":"利用 scRNA-seq 数据中的原始计数矩阵进行单细胞 RNA 注释的机器学习、统计方法和人工智能研究进展","authors":"Megha Patel, Nimish Magre, Himanshi Motwani, Nik Bear Brown","doi":"arxiv-2406.05258","DOIUrl":null,"url":null,"abstract":"Single-cell RNA sequencing (scRNA-seq) has revolutionized our ability to\nanalyze gene expression at the resolution of individual cells, providing\nunprecedented insights into cellular heterogeneity and complex biological\nsystems. This paper reviews various advanced computational and machine learning\ntechniques tailored for the analysis of scRNA-seq data, emphasizing their roles\nin different stages of the data processing pipeline.","PeriodicalId":501323,"journal":{"name":"arXiv - STAT - Other Statistics","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Advances in Machine Learning, Statistical Methods, and AI for Single-Cell RNA Annotation Using Raw Count Matrices in scRNA-seq Data\",\"authors\":\"Megha Patel, Nimish Magre, Himanshi Motwani, Nik Bear Brown\",\"doi\":\"arxiv-2406.05258\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Single-cell RNA sequencing (scRNA-seq) has revolutionized our ability to\\nanalyze gene expression at the resolution of individual cells, providing\\nunprecedented insights into cellular heterogeneity and complex biological\\nsystems. This paper reviews various advanced computational and machine learning\\ntechniques tailored for the analysis of scRNA-seq data, emphasizing their roles\\nin different stages of the data processing pipeline.\",\"PeriodicalId\":501323,\"journal\":{\"name\":\"arXiv - STAT - Other Statistics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-06-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - STAT - Other Statistics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2406.05258\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - STAT - Other Statistics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2406.05258","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 0

摘要

单细胞 RNA 测序（scRNA-seq）彻底改变了我们以单个细胞为单位分析基因表达的能力，为我们深入了解细胞异质性和复杂的生物系统提供了前所未有的视角。本文回顾了为分析 scRNA-seq 数据而量身定制的各种先进计算和机器学习技术，强调了它们在数据处理管道不同阶段的作用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

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

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.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

arXiv - STAT - Other Statistics

自引率

0.00%

发文量