南方科技大学知识苑(SUSTech KC): GENEFISHING TO UNCOVER HIDDEN PATTERNS IN OMICS DATA

题名	GENEFISHING TO UNCOVER HIDDEN PATTERNS IN OMICS DATA
其他题名	基于 GeneFishing 方法挖掘生物组学数据中的隐藏模式
姓名	郭迪清
姓名拼音	GUO Diqing
学号	12232874
学位类型	硕士
学位专业	0701 数学
学科门类/专业学位类别	07 理学
导师	张卓松
导师单位	统计与数据科学系
论文答辩日期	2024-05-19
论文提交日期	2024-07-06
学位授予单位	南方科技大学
学位授予地点	深圳
摘要	The rapid development of omics technologies has led to large-scale omics data. These data often exhibit characteristics such as high dimensionality, heterogeneity, and low signal-to-noise ratio, making it challenging to extract useful information. Traditional statistical methods may not adequately address these issues. GeneFishing, a semi-supervised learning approach based on resampling and clustering, was introduced in 2019. It effectively captures signals from biological sequencing data, initially applied to RNA sequencing for gene prioritization. It utilizes a set of known genes as“baits”and combines them with unknown candidate genes for resampling and clustering, identifying genes with similar functions. While GeneFishing’s effectiveness has been demonstrated in RNA data, its application to other omics data, such as proteomics, remains unexplored. Additionally, defining baits and their transferability across different omics data are key issues. This study aims to address these challenges. We apply GeneFishing to explore multi-omics data sets: single-cell RNA-seq data of mouse embryo, protein abundance data from colon cancer, protein and RNA-seq data from glioblastoma patients. We conduct exploratory data analysis, determine fishing targets, select appropriate baits using multivariate statistics and bioinformatics methods, and explore bait transferability between omics data. We adjust and improve GeneFishing parameters, and discuss the final findings. In the study of embryo data set, we find that beside the traditional gene level, GeneFishing can also be applied at the cell level to effectively identify cells of the same type as the baits. Moreover, adjusting the clustering parameter k enables the identification of more refined cell sub-types. In the analysis of protein data, we address the missing values characteristic of protein data and apply GeneFishing, rediscovering important genes and proteins related to cancer regulation and predicting valuable genes for further research. In the protein and RNA data from glioblastoma patients, we focus on transferring baits from proteomics to RNA data, identifying significant results in the RNA data that were not prominent in the protein data, indicating the potential for comprehensive biological discoveries by integrating both omics data sets.
关键词	Gene Prioritization Data Mining Semi-supervised Learning Multi-omics Data
语种	英语
培养类别	独立培养
入学年份	2022
学位授予年份	2024-06
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所在学位评定分委会	数学
国内图书分类号	O212.6
来源库	人工提交
成果类型	学位论文
条目标识符	http://sustech.caswiz.com/handle/2SGJ60CL/779037
专题	理学院_统计与数据科学系
推荐引用方式 GB/T 7714	Guo DQ. GENEFISHING TO UNCOVER HIDDEN PATTERNS IN OMICS DATA[D]. 深圳. 南方科技大学,2024.

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