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Title: Robust Differential Abundance Analysis of Microbiome Sequencing Data
It is well known that the microbiome data are ridden with outliers and have heavy distribution tails, but the impact of outliers and heavy-tailedness has yet to be examined systematically. This paper investigates the impact of outliers and heavy-tailedness on differential abundance analysis (DAA) using the linear models for the differential abundance analysis (LinDA) method and proposes effective strategies to mitigate their influence. The presence of outliers and heavy-tailedness can significantly decrease the power of LinDA. We investigate various techniques to address outliers and heavy-tailedness, including generalizing LinDA into a more flexible framework that allows for the use of robust regression and winsorizing the data before applying LinDA. Our extensive numerical experiments and real-data analyses demonstrate that robust Huber regression has overall the best performance in addressing outliers and heavy-tailedness.  more » « less
Award ID(s):
2113359 2113360
PAR ID:
10511577
Author(s) / Creator(s):
; ; ;
Publisher / Repository:
MDPI
Date Published:
Journal Name:
Genes
Volume:
14
Issue:
11
ISSN:
2073-4425
Page Range / eLocation ID:
2000
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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