Evidence map›Paper›PMID 33600415›Full record

ArticlePloS one2021

Comparison of beta diversity measures in clustering the high-dimensional microbial data.

Biyuan Chen, Xueyi He, Bangquan Pan, Xiaobing Zou, Na You

Abstract readComparative Study
In one paragraph

Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Biyuan ChenChild Development and Behavior Center, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
Xueyi HeSchool of Mathematics, Sun Yat-sen University, Guangzhou, China.
Bangquan PanSchool of Mathematics, Sun Yat-sen University, Guangzhou, China.
Xiaobing ZouChild Development and Behavior Center, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
Na YouSchool of Mathematics, Sun Yat-sen University, Guangzhou, China.ORCID 0000-0001-8014-7229

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The heterogeneity of disease is a major concern in medical research and is commonly characterized as subtypes with different pathogeneses exhibiting distinct prognoses and treatment effects. The classification of a population into homogeneous subgroups is challenging, especially for complex diseases. Recent studies show that gut microbiome compositions play a vital role in disease development, and it is of great interest to cluster patients according to their microbial profiles. There are a variety of beta diversity measures to quantify the dissimilarity between the compositions of different samples for clustering. However, using different beta diversity measures results in different clusters, and it is difficult to make a choice among them. Considering microbial compositions from 16S rRNA sequencing, which are presented as a high-dimensional vector with a large proportion of extremely small or even zero-valued elements, we set up three simulation experiments to mimic the microbial compositional data and evaluate the performance of different beta diversity measures in clustering. It is shown that the Kullback-Leibler divergence-based beta diversity, including the Jensen-Shannon divergence and its square root, and the hypersphere-based beta diversity, including the Bhattacharyya and Hellinger, can capture compositional changes in low-abundance elements more efficiently and can work stably. Their performance on two real datasets demonstrates the validity of the simulation experiments.

Indexed as

Gastrointestinal MicrobiomeAlgorithmsCluster AnalysisComputer SimulationGenetic VariationHumansMicrobiotaModels, GeneticPhylogenyRNA, Ribosomal, 16SRNA, Ribosomal, 16S

Identifiers

PMID33600415
PMCPMC7891732

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.