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Title: Variable Selection for Sparse Data with Applications to Vaginal Microbiome and Gene Expression Data
Sparse data with a high portion of zeros arise in various disciplines. Modeling sparse high-dimensional data is a challenging and growing research area. In this paper, we provide statistical methods and tools for analyzing sparse data in a fairly general and complex context. We utilize two real scientific applications as illustrations, including a longitudinal vaginal microbiome data and a high dimensional gene expression data. We recommend zero-inflated model selections and significance tests to identify the time intervals when the pregnant and non-pregnant groups of women are significantly different in terms of Lactobacillus species. We apply the same techniques to select the best 50 genes out of 2426 sparse gene expression data. The classification based on our selected genes achieves 100% prediction accuracy. Furthermore, the first four principal components based on the selected genes can explain as high as 83% of the model variability.  more » « less
Award ID(s):
1924859
PAR ID:
10478990
Author(s) / Creator(s):
; ;
Publisher / Repository:
MDPI
Date Published:
Journal Name:
Genes
Volume:
14
Issue:
2
ISSN:
2073-4425
Page Range / eLocation ID:
403
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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