scDiffCoAM:利用 scRNA-Seq 数据分析确定食管鳞状细胞癌潜在生物标记物的完整框架

IF 2.1 4区 生物学 Q2 BIOLOGY Journal of Biosciences Pub Date : 2024-08-06 DOI:10.1007/s12038-024-00447-6
Manaswita Saikia, Dhruba K Bhattacharyya, Jugal K Kalita
{"title":"scDiffCoAM:利用 scRNA-Seq 数据分析确定食管鳞状细胞癌潜在生物标记物的完整框架","authors":"Manaswita Saikia, Dhruba K Bhattacharyya, Jugal K Kalita","doi":"10.1007/s12038-024-00447-6","DOIUrl":null,"url":null,"abstract":"<p>Single-cell RNA sequencing (scRNA-Seq) technology provides the scope to gain insight into the interplay between intrinsic cellular processes as well as transcriptional and behavioral changes in gene–gene interactions across varying conditions. The high level of scarcity of scRNA-seq data, however, poses a significant challenge for analysis. We propose a complete differential co-expression (DCE) analysis framework for scRNA-Seq data to extract network modules and identify hub-genes. The performance of our method has been shown to be satisfactory after validation using an scRNA-Seq esophageal squamous cell carcinoma (ESCC) dataset. From comparison with four other existing hub-gene finding methods, it has been observed that our method performs better in the majority of cases and has the ability to identify unique potential biomarkers that were not detected by the other methods. The potential biomarker genes identified by our framework, differential co-expression analysis method for single-cell RNA sequencing data (scDiffCoAM), have been validated both statistically and biologically.</p>","PeriodicalId":15171,"journal":{"name":"Journal of Biosciences","volume":null,"pages":null},"PeriodicalIF":2.1000,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"scDiffCoAM: A complete framework to identify potential biomarkers for esophageal squamous cell carcinoma using scRNA-Seq data analysis\",\"authors\":\"Manaswita Saikia, Dhruba K Bhattacharyya, Jugal K Kalita\",\"doi\":\"10.1007/s12038-024-00447-6\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Single-cell RNA sequencing (scRNA-Seq) technology provides the scope to gain insight into the interplay between intrinsic cellular processes as well as transcriptional and behavioral changes in gene–gene interactions across varying conditions. The high level of scarcity of scRNA-seq data, however, poses a significant challenge for analysis. We propose a complete differential co-expression (DCE) analysis framework for scRNA-Seq data to extract network modules and identify hub-genes. The performance of our method has been shown to be satisfactory after validation using an scRNA-Seq esophageal squamous cell carcinoma (ESCC) dataset. From comparison with four other existing hub-gene finding methods, it has been observed that our method performs better in the majority of cases and has the ability to identify unique potential biomarkers that were not detected by the other methods. The potential biomarker genes identified by our framework, differential co-expression analysis method for single-cell RNA sequencing data (scDiffCoAM), have been validated both statistically and biologically.</p>\",\"PeriodicalId\":15171,\"journal\":{\"name\":\"Journal of Biosciences\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.1000,\"publicationDate\":\"2024-08-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Biosciences\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1007/s12038-024-00447-6\",\"RegionNum\":4,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Biosciences","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1007/s12038-024-00447-6","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOLOGY","Score":null,"Total":0}
引用次数: 0

摘要

单细胞 RNA 测序(scRNA-Seq)技术为深入了解细胞内在过程之间的相互作用以及不同条件下基因-基因相互作用的转录和行为变化提供了机会。然而,scRNA-seq 数据的高度稀缺性给分析带来了巨大挑战。我们为 scRNA-seq 数据提出了一个完整的差异共表达(DCE)分析框架,以提取网络模块并识别枢纽基因。在使用 scRNA-Seq 食管鳞癌(ESCC)数据集进行验证后,我们的方法性能令人满意。通过与其他四种现有的中枢基因发现方法进行比较,我们发现我们的方法在大多数情况下表现更好,而且有能力发现其他方法没有检测到的独特的潜在生物标记基因。我们的框架--单细胞 RNA 测序数据差异共表达分析方法(scDiffCoAM)--所发现的潜在生物标记基因已通过统计学和生物学验证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
scDiffCoAM: A complete framework to identify potential biomarkers for esophageal squamous cell carcinoma using scRNA-Seq data analysis

Single-cell RNA sequencing (scRNA-Seq) technology provides the scope to gain insight into the interplay between intrinsic cellular processes as well as transcriptional and behavioral changes in gene–gene interactions across varying conditions. The high level of scarcity of scRNA-seq data, however, poses a significant challenge for analysis. We propose a complete differential co-expression (DCE) analysis framework for scRNA-Seq data to extract network modules and identify hub-genes. The performance of our method has been shown to be satisfactory after validation using an scRNA-Seq esophageal squamous cell carcinoma (ESCC) dataset. From comparison with four other existing hub-gene finding methods, it has been observed that our method performs better in the majority of cases and has the ability to identify unique potential biomarkers that were not detected by the other methods. The potential biomarker genes identified by our framework, differential co-expression analysis method for single-cell RNA sequencing data (scDiffCoAM), have been validated both statistically and biologically.

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
Journal of Biosciences
Journal of Biosciences 生物-生物学
CiteScore
5.80
自引率
0.00%
发文量
83
审稿时长
3 months
期刊介绍: The Journal of Biosciences is a quarterly journal published by the Indian Academy of Sciences, Bangalore. It covers all areas of Biology and is the premier journal in the country within its scope. It is indexed in Current Contents and other standard Biological and Medical databases. The Journal of Biosciences began in 1934 as the Proceedings of the Indian Academy of Sciences (Section B). This continued until 1978 when it was split into three parts : Proceedings-Animal Sciences, Proceedings-Plant Sciences and Proceedings-Experimental Biology. Proceedings-Experimental Biology was renamed Journal of Biosciences in 1979; and in 1991, Proceedings-Animal Sciences and Proceedings-Plant Sciences merged with it.
期刊最新文献
Comparative analysis of Quercus suber L. acorns in natural and semi-natural stands: Morphology characterization, insect attacks, and chemical composition Phosphorylation mapping of laminin γ1-chain: Kinases, functional interaction sequences, and phosphorylation-interfering cancer mutations IRF9 and STAT1 as biomarkers involved in T-cell immunity in atherosclerosis Wisdom of (molecular) crowds: How a snake’s temperature-sensing superpower separates information from misinformation CDCA: Community detection in RNA-seq data using centrality-based approach
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1