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Activity Number:
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406
- Advances of Statistical Methodologies in Proteogenomics Research
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Type:
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Topic Contributed
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Date/Time:
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Wednesday, August 5, 2020 : 1:00 PM to 2:50 PM
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Sponsor:
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Section on Statistics in Genomics and Genetics
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Abstract #313290
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Title:
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Sparse Multiple Co-Inertia Analysis with Application to Integrative Analysis of Multi -Omics Data
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Author(s):
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Eun Jeong Min* and Qi Long
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Companies:
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Univerisity of Pennsylvania and University of Pennsylvania
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Keywords:
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integrative analysis;
multiple co-intertia analysis;
omic data;
high dimensional data;
gene network information;
l0 penalty
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Abstract:
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Multiple co-inertia analysis (mCIA) is a multivariate analysis method that can assess relationships and trends in multiple datasets. Recently it has been used for integrative analysis of multiple high-dimensional -omics datasets. However, its estimated loading vectors are non-sparse, which presents challenges for identifying important features and interpreting analysis results. We propose two new mCIA methods: 1) a sparse mCIA method that produces sparse loading estimates and 2) a structured sparse mCIA method that further enables incorporation of structural information among variables such as those from functional genomics. Our extensive simulation studies demonstrate the superior performance of the sparse mCIA and the structured sparse mCIA methods compared to the existing mCIA in terms of feature selection and estimation accuracy. Application to the integrative analysis of transcriptomics data and proteomics data from a cancer study identified biomarkers that are suggested in the literature related with cancer disease.
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Authors who are presenting talks have a * after their name.
