Joint Screening for Ultra-High Dimensional Multi-Omics Data
Investigators often face ultra-high dimensional multi-omics data, where identifying significant genes and omics within a gene is of interest. In such data, each gene forms a group consisting of its multiple omics. Moreover, some genes may also be highly correlated. This leads to a tri-level hierarch...
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MDPI AG
2024-11-01
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| Series: | Bioengineering |
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| Online Access: | https://www.mdpi.com/2306-5354/11/12/1193 |
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| author | Ulrich Kemmo Tsafack Chien-Wei Lin Kwang Woo Ahn |
| author_facet | Ulrich Kemmo Tsafack Chien-Wei Lin Kwang Woo Ahn |
| author_sort | Ulrich Kemmo Tsafack |
| collection | DOAJ |
| description | Investigators often face ultra-high dimensional multi-omics data, where identifying significant genes and omics within a gene is of interest. In such data, each gene forms a group consisting of its multiple omics. Moreover, some genes may also be highly correlated. This leads to a tri-level hierarchical structured data: the cluster level, which is the group of correlated genes, the subgroup level, which is the group of omics of the same gene, and the individual level, which consists of omics. Screening is widely used to remove unimportant variables so that the number of remaining variables becomes smaller than the sample size. Penalized regression with the remaining variables after performing screening is then used to identify important variables. To screen unimportant genes, we propose to cluster genes and conduct screening. We show that the proposed screening method possesses the sure screening property. Extensive simulations show that the proposed screening method outperforms competing methods. We apply the proposed variable selection method to the TCGA breast cancer dataset to identify genes and omics that are related to breast cancer. |
| format | Article |
| id | doaj-art-3866e13a2c4647bcaa5a34bd0902e718 |
| institution | DOAJ |
| issn | 2306-5354 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj-art-3866e13a2c4647bcaa5a34bd0902e7182025-08-20T02:57:07ZengMDPI AGBioengineering2306-53542024-11-011112119310.3390/bioengineering11121193Joint Screening for Ultra-High Dimensional Multi-Omics DataUlrich Kemmo Tsafack0Chien-Wei Lin 1Kwang Woo Ahn2Division of Biostatistics, Medical College of Wisconsin (MCW), Milwaukee, WI 53226, USADivision of Biostatistics, Medical College of Wisconsin (MCW), Milwaukee, WI 53226, USADivision of Biostatistics, Medical College of Wisconsin (MCW), Milwaukee, WI 53226, USAInvestigators often face ultra-high dimensional multi-omics data, where identifying significant genes and omics within a gene is of interest. In such data, each gene forms a group consisting of its multiple omics. Moreover, some genes may also be highly correlated. This leads to a tri-level hierarchical structured data: the cluster level, which is the group of correlated genes, the subgroup level, which is the group of omics of the same gene, and the individual level, which consists of omics. Screening is widely used to remove unimportant variables so that the number of remaining variables becomes smaller than the sample size. Penalized regression with the remaining variables after performing screening is then used to identify important variables. To screen unimportant genes, we propose to cluster genes and conduct screening. We show that the proposed screening method possesses the sure screening property. Extensive simulations show that the proposed screening method outperforms competing methods. We apply the proposed variable selection method to the TCGA breast cancer dataset to identify genes and omics that are related to breast cancer.https://www.mdpi.com/2306-5354/11/12/1193variable selectionscreeningmulti-omicsultra-high dimensional data |
| spellingShingle | Ulrich Kemmo Tsafack Chien-Wei Lin Kwang Woo Ahn Joint Screening for Ultra-High Dimensional Multi-Omics Data Bioengineering variable selection screening multi-omics ultra-high dimensional data |
| title | Joint Screening for Ultra-High Dimensional Multi-Omics Data |
| title_full | Joint Screening for Ultra-High Dimensional Multi-Omics Data |
| title_fullStr | Joint Screening for Ultra-High Dimensional Multi-Omics Data |
| title_full_unstemmed | Joint Screening for Ultra-High Dimensional Multi-Omics Data |
| title_short | Joint Screening for Ultra-High Dimensional Multi-Omics Data |
| title_sort | joint screening for ultra high dimensional multi omics data |
| topic | variable selection screening multi-omics ultra-high dimensional data |
| url | https://www.mdpi.com/2306-5354/11/12/1193 |
| work_keys_str_mv | AT ulrichkemmotsafack jointscreeningforultrahighdimensionalmultiomicsdata AT chienweilin jointscreeningforultrahighdimensionalmultiomicsdata AT kwangwooahn jointscreeningforultrahighdimensionalmultiomicsdata |