AUTHOR=Hu Zongliang , Zhou Yan , Tong Tiejun 

TITLE=Meta-Analyzing Multiple Omics Data With Robust Variable Selection

JOURNAL=Frontiers in Genetics

VOLUME=Volume 12 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2021.656826

DOI=10.3389/fgene.2021.656826

ISSN=1664-8021

ABSTRACT=High-throughput omics data are becoming more and more popular in various areas of science. Given that many publicly available datasets address the same questions, researchers have applied meta-analysis to synthesize multiple datasets to achieve more reliable results  for model estimation and prediction. Due to the high-dimensionality of omics data,  it is also  desirable to incorporate variable selection into meta-analysis. Existing meta-analyzing  variable selection methods are often sensitive to the presence of outliers, and may lead to missed detections of relevant covariates, especially for lasso-type penalties. In this paper, we develop a robust variable selection algorithm for meta-analyzing high-dimensional datasets. We first search an outlier-free subset from each dataset by borrowing information across the datasets with repeatedly use of the least trimmed squared estimates for the logistic model and together with a hierarchical bi-level variable selection technique. We then refine a reweighting step to further improve the efficiency after obtaining a reliable non-outlier subset. Simulation studies and real data analysis show that our new method can provide more reliable results than the existing meta-analysis methods in the presence of outliers.