세미나/행사

> 정보센터 > 세미나/행사

제목 2021년 3월 11일 생물정보학 세미나
작성자 관리자 작성일 2021-03-04
서울대학교 생물정보학 협동과정 주최로 세미나를 아래와 같이 열고자 하오니 많은 참여 바랍니다.
세미나는 Zoom을 통한 온라인 강의로 예정되어 있으며 강의에 참여하고자 하시는 분께서는　mari911@snu.ac.kr로 메일 주시면 강의 링크 전달드리도록 하겠습니다.　

일시:　　2021년 3월 11일　목요일 오전 11시
연사:　　고현욱 연사님　(The State University of New York)

Title:　Meta-analysis methods for multiple related markers: applications to microbiome studies with the results on multiple α-diversity indices
Abstract
Meta-analysis is a practical and powerful analytic tool that enables a unified statistical inference across the results from multiple studies. Notably, researchers often report the results on multiple related markers in each study (e.g., various α-diversity indices in microbiome studies). However, univariate meta-analyses are limited to combining the results on a single common marker at a time, whereas existing multivariate meta-analyses are limited to the situations where marker-by-marker correlations are given in each study. Thus, here we introduce two meta-analysis methods, namely, multi-marker meta-analysis (mMeta) and adaptive multi-marker meta-analysis (aMeta), to combine multiple studies throughout multiple related markers with no priori results on marker-by-marker correlations. mMeta is a statistical estimator for a pooled estimate and its standard error across all the studies and markers, whereas aMeta is a statistical test based on the test statistic of the minimum p-value among marker-specific meta-analyses. mMeta conducts both effect estimation and hypothesis testing based on a weighted average of marker-specific pooled estimates while estimating marker-by-marker correlations non-parametrically via permutations, yet its power is only moderate. In contrast, aMeta closely approaches the highest power among marker-specific meta-analyses, yet it is limited to hypothesis testing. While their applications can be broader, we illustrate the use of mMeta and aMeta to combine microbiome studies throughout multiple α-diversity indices. We evaluate mMeta and aMeta in silico and apply them to real microbiome studies on the disparity in α-diversity by the status of HIV infection.