PMID- 36007229
OWN - NLM
STAT- MEDLINE
DCOM- 20220926
LR  - 20221007
IS  - 1477-4054 (Electronic)
IS  - 1467-5463 (Linking)
VI  - 23
IP  - 5
DP  - 2022 Sep 20
TI  - Principal microbial groups: compositional alternative to phylogenetic grouping of 
      microbiome data.
LID - bbac328 [pii]
LID - 10.1093/bib/bbac328 [doi]
AB  - Statistical and machine learning techniques based on relative abundances have 
      been used to predict health conditions and to identify microbial biomarkers. 
      However, high dimensionality, sparsity and the compositional nature of microbiome 
      data represent statistical challenges. On the other hand, the taxon grouping 
      allows summarizing microbiome abundance with a coarser resolution in a lower 
      dimension, but it presents new challenges when correlating taxa with a disease. 
      In this work, we present a novel approach that groups Operational Taxonomical 
      Units (OTUs) based only on relative abundances as an alternative to taxon 
      grouping. The proposed procedure acknowledges the compositional data making use 
      of principal balances. The identified groups are called Principal Microbial 
      Groups (PMGs). The procedure reduces the need for user-defined aggregation of 
      $\textrmOTU$s and offers the possibility of working with coarse group of 
      $\textrmOTU$s, which are not present in a phylogenetic tree. PMGs can be used 
      for two different goals: (1) as a dimensionality reduction method for 
      compositional data, (2) as an aggregation procedure that provides an alternative 
      to taxon grouping for construction of microbial balances afterward used for 
      disease prediction. We illustrate the procedure with a cirrhosis study data. PMGs 
      provide a coherent data analysis for the search of biomarkers in human 
      microbiota. The source code and demo data for PMGs are available at: 
      https://github.com/asliboyraz/PMGs.
CI  - (c) The Author(s) 2022. Published by Oxford University Press. All rights reserved. 
      For Permissions, please email: journals.permissions@oup.com.
FAU - Boyraz, Asli
AU  - Boyraz A
AD  - Department of Computer Programming, Recep Tayyip Erdogan University, Ardesen 
      Vocational School, Rize, 53400, Turkey.
FAU - Pawlowsky-Glahn, Vera
AU  - Pawlowsky-Glahn V
AD  - Department of Computer Sciences, Applied Mathematics and Statistics, University 
      of Girona, Campus Montilivi, 17003 Girona, Spain.
FAU - Egozcue, Juan Jose
AU  - Egozcue JJ
AD  - Department of Civil and Environmental Engineering, Universitat Politecnica de 
      Catalunya, Barcelona, 08034, Spain.
FAU - Acar, Aybar Can
AU  - Acar AC
AD  - Department of Medical Informatics, Middle East Technical University, Ankara 
      Turkey.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
PL  - England
TA  - Brief Bioinform
JT  - Briefings in bioinformatics
JID - 100912837
SB  - IM
MH  - Data Analysis
MH  - Humans
MH  - *Microbiota/genetics
MH  - Phylogeny
OTO - NOTNLM
OT  - balance
OT  - compositional data
OT  - microbial biomarkers
OT  - microbiome
EDAT- 2022/08/26 06:00
MHDA- 2022/09/28 06:00
CRDT- 2022/08/25 16:22
PHST- 2022/02/01 00:00 [received]
PHST- 2022/07/19 00:00 [revised]
PHST- 2022/07/20 00:00 [accepted]
PHST- 2022/08/26 06:00 [pubmed]
PHST- 2022/09/28 06:00 [medline]
PHST- 2022/08/25 16:22 [entrez]
AID - 6675749 [pii]
AID - 10.1093/bib/bbac328 [doi]
PST - ppublish
SO  - Brief Bioinform. 2022 Sep 20;23(5):bbac328. doi: 10.1093/bib/bbac328.