Characterization of chromatin accessibility patterns in different mouse cell types using machine learning methods at single-cell resolution

被引:1
|
作者
Xu, Yaochen [1 ]
Huang, FeiMing [2 ]
Guo, Wei [3 ,4 ]
Feng, KaiYan [5 ]
Zhu, Lin [6 ]
Zeng, Zhenbing [1 ]
Huang, Tao [7 ,8 ]
Cai, Yu-Dong [2 ]
机构
[1] Shanghai Univ, Sch Sci, Dept Math, Shanghai, Peoples R China
[2] Shanghai Univ, Sch Life Sci, Shanghai, Peoples R China
[3] Shanghai Jiao Tong Univ Sch Med SJTUSM, Key Lab Stem Cell Biol, Shanghai, Peoples R China
[4] Chinese Acad Sci, Shanghai Inst Biol Sci SIBS, Shanghai, Peoples R China
[5] Guangdong AIB Polytech Coll, Dept Comp Sci, Guangzhou, Peoples R China
[6] Univ Shanghai Sci & Technol, Sch Hlth Sci & Engn, Shanghai, Peoples R China
[7] Univ Chinese Acad Sci, Chinese Acad Sci, Shanghai Inst Nutr & Hlth, Biomed Big Data Ctr,CAS Key Lab Computat Biol, Shanghai, Peoples R China
[8] Univ Chinese Acad Sci, Shanghai Inst Nutr & Hlth, Chinese Acad Sci, CAS Key Lab Tissue Microenvironm & Tumor, Shanghai, Peoples R China
基金
国家重点研发计划;
关键词
chromatin accessibility; chromatin heterogeneity; single-cell resolution; mouse cell type; machine learning; biomarker genes; HLA-DMB; FEATURE-SELECTION; GENE; DNA; PROTEIN; EXPRESSION; RECEPTOR; ALLELES; GENOME; SITES;
D O I
10.3389/fgene.2023.1145647
中图分类号
Q3 [遗传学];
学科分类号
071007 ; 090102 ;
摘要
Chromatin accessibility is a generic property of the eukaryotic genome, which refers to the degree of physical compaction of chromatin. Recent studies have shown that chromatin accessibility is cell type dependent, indicating chromatin heterogeneity across cell lines and tissues. The identification of markers used to distinguish cell types at the chromosome level is important to understand cell function and classify cell types. In the present study, we investigated transcriptionally active chromosome segments identified by sci-ATAC-seq at single-cell resolution, including 69,015 cells belonging to 77 different cell types. Each cell was represented by existence status on 20,783 genes that were obtained from 436,206 active chromosome segments. The gene features were deeply analyzed by Boruta, resulting in 3897 genes, which were ranked in a list by Monte Carlo feature selection. Such list was further analyzed by incremental feature selection (IFS) method, yielding essential genes, classification rules and an efficient random forest (RF) classifier. To improve the performance of the optimal RF classifier, its features were further processed by autoencoder, light gradient boosting machine and IFS method. The final RF classifier with MCC of 0.838 was constructed. Some marker genes such as H2-Dmb2, which are specifically expressed in antigen-presenting cells (e.g., dendritic cells or macrophages), and Tenm2, which are specifically expressed in T cells, were identified in this study. Our analysis revealed numerous potential epigenetic modification patterns that are unique to particular cell types, thereby advancing knowledge of the critical functions of chromatin accessibility in cell processes.
引用
收藏
页数:12
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