Michael B Sohn1, Ruofei Du2, Lingling An3. 1. Interdisciplinary Program in Statistics and. 2. Department of Agricultural and Biosystems Engineering, University of Arizona, Tucson, AZ 85721, USA. 3. Interdisciplinary Program in Statistics and Department of Agricultural and Biosystems Engineering, University of Arizona, Tucson, AZ 85721, USA.
Abstract
MOTIVATION: The analysis of differential abundance for features (e.g. species or genes) can provide us with a better understanding of microbial communities, thus increasing our comprehension and understanding of the behaviors of microbial communities. However, it could also mislead us about the characteristics of microbial communities if the abundances or counts of features on different scales are not properly normalized within and between communities, prior to the analysis of differential abundance. Normalization methods used in the differential analysis typically try to adjust counts on different scales to a common scale using the total sum, mean or median of representative features across all samples. These methods often yield undesirable results when the difference in total counts of differentially abundant features (DAFs) across different conditions is large. RESULTS: We develop a novel method, Ratio Approach for Identifying Differential Abundance (RAIDA), which utilizes the ratio between features in a modified zero-inflated lognormal model. RAIDA removes possible problems associated with counts on different scales within and between conditions. As a result, its performance is not affected by the amount of difference in total abundances of DAFs across different conditions. Through comprehensive simulation studies, the performance of our method is consistently powerful, and under some situations, RAIDA greatly surpasses other existing methods. We also apply RAIDA on real datasets of type II diabetes and find interesting results consistent with previous reports. AVAILABILITY AND IMPLEMENTATION: An R package for RAIDA can be accessed from http://cals.arizona.edu/%7Eanling/sbg/software.htm.
MOTIVATION: The analysis of differential abundance for features (e.g. species or genes) can provide us with a better understanding of microbial communities, thus increasing our comprehension and understanding of the behaviors of microbial communities. However, it could also mislead us about the characteristics of microbial communities if the abundances or counts of features on different scales are not properly normalized within and between communities, prior to the analysis of differential abundance. Normalization methods used in the differential analysis typically try to adjust counts on different scales to a common scale using the total sum, mean or median of representative features across all samples. These methods often yield undesirable results when the difference in total counts of differentially abundant features (DAFs) across different conditions is large. RESULTS: We develop a novel method, Ratio Approach for Identifying Differential Abundance (RAIDA), which utilizes the ratio between features in a modified zero-inflated lognormal model. RAIDA removes possible problems associated with counts on different scales within and between conditions. As a result, its performance is not affected by the amount of difference in total abundances of DAFs across different conditions. Through comprehensive simulation studies, the performance of our method is consistently powerful, and under some situations, RAIDA greatly surpasses other existing methods. We also apply RAIDA on real datasets of type II diabetes and find interesting results consistent with previous reports. AVAILABILITY AND IMPLEMENTATION: An R package for RAIDA can be accessed from http://cals.arizona.edu/%7Eanling/sbg/software.htm.
Authors: Brian E Lacy; Michael D Crowell; Ann Schettler-Duncan; Carole Mathis; Pankaj J Pasricha Journal: Diabetes Care Date: 2004-10 Impact factor: 19.112
Authors: Valerie De Anda; Icoquih Zapata-Peñasco; Augusto Cesar Poot-Hernandez; Luis E Eguiarte; Bruno Contreras-Moreira; Valeria Souza Journal: Gigascience Date: 2017-11-01 Impact factor: 6.524