A review of common methods used in the analysis of human microbiome sequencing data

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The advent of high-throughput sequencing (HTS) has allowed scientists sequence the genomic content of entire microbial communities rapidly and affordably. Methods for data management, visualization, and analysis of this microbiome sequencing data are now widespread in open and closed source software tools. In this review, we identify common analysis methods used in microbiome sequencing studies, including methods for normalisation of abundance table data, visualization of high-dimensional microbiome data and the analysis of longitudinal microbiome sequencing data. In particular we find that a very small proportion of metagenomic studies made use of compositional data analysis methods and none that made use of compositional methods for longitudinal microbiome data." } { "@context": "http://schema.org", "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": "1", "item": { "@id": "https://f1000research.com/", "name": "Home" } }, { "@type": "ListItem", "position": "2", "item": { "@id": "https://f1000research.com/browse/articles", "name": "Browse" } }, { "@type": "ListItem", "position": "3", "item": { "@id": "https://f1000research.com/articles/13-369", "name": "A review of common methods used in the analysis of human microbiome..." } } ] } Home Browse A review of common methods used in the analysis of human microbiome... ALL Metrics - Views Downloads Get PDF Get XML Cite How to cite this article Hannan P, Nicol M and Lesosky M. A review of common methods used in the analysis of human microbiome sequencing data [version 1; peer review: 1 approved, 1 not approved] . F1000Research 2024, 13 :369 ( https://doi.org/10.12688/f1000research.110605.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Review A review of common methods used in the analysis of human microbiome sequencing data [version 1; peer review: 1 approved, 1 not approved] Patrick Hannan https://orcid.org/0000-0002-8217-843X 1 , Mark Nicol 2 , Maia Lesosky 1 Patrick Hannan https://orcid.org/0000-0002-8217-843X 1 , Mark Nicol 2 , Maia Lesosky 1 PUBLISHED 23 Apr 2024 Author details Author details 1 Division of Epidemiology and Biostatistics, University of Cape Town, Cape Town, Western Cape, 7701, South Africa 2 Division of Infection and Immunity, University of Western Australia, Perth, Western Australia, 6907, Australia Patrick Hannan Roles: Conceptualization, Investigation, Writing – Original Draft Preparation, Writing – Review & Editing Mark Nicol Roles: Supervision, Writing – Review & Editing Maia Lesosky Roles: Conceptualization, Supervision, Writing – Review & Editing OPEN PEER REVIEW DETAILS REVIEWER STATUS Abstract The past two decades have seen a rapid rise in the development and use of sequencing technologies. The advent of high-throughput sequencing (HTS) has allowed scientists sequence the genomic content of entire microbial communities rapidly and affordably. Methods for data management, visualization, and analysis of this microbiome sequencing data are now widespread in open and closed source software tools. In this review, we identify common analysis methods used in microbiome sequencing studies, including methods for normalisation of abundance table data, visualization of high-dimensional microbiome data and the analysis of longitudinal microbiome sequencing data. In particular we find that a very small proportion of metagenomic studies made use of compositional data analysis methods and none that made use of compositional methods for longitudinal microbiome data. READ ALL READ LESS Keywords human microbiome, review, compositional data Corresponding Author(s) Patrick Hannan ( [email protected] ) Close Corresponding author: Patrick Hannan Competing interests: No competing interests were disclosed. Grant information: PH receives funding from the Drakenstein Child Health Study. The Drakenstein Child Health Study was funded by the Bill \& Melinda Gates Foundation (OPP1017641), Medical Research Council South Africa, National Research Foundation South Africa, National Institute of Health, and H3Africa (1U01AI110466–01A1). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Copyright: © 2024 Hannan P et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Hannan P, Nicol M and Lesosky M. A review of common methods used in the analysis of human microbiome sequencing data [version 1; peer review: 1 approved, 1 not approved] . F1000Research 2024, 13 :369 ( https://doi.org/10.12688/f1000research.110605.1 ) First published: 23 Apr 2024, 13 :369 ( https://doi.org/10.12688/f1000research.110605.1 ) Latest published: 23 Apr 2024, 13 :369 ( https://doi.org/10.12688/f1000research.110605.1 ) Author summary The rapid decrease in the cost of sequencing large amounts of genomic material has led to an equally rapid increase in the number of studies sequencing entire microbial communities. There is increasing awareness in the literature that microbiome datasets generated by next generation or high-throughput sequencing (HTS) are compositional due to the arbitrary read count total. In this study, we investigated the methods used to analyse microbiome data generated by HTS machines and the adoption of methods that take into account the compositional nature of microbiome data. A specific focus of this study was to investigate compositional methods in the analysis of longitudinal microbiome sequencing data. Although recent studies were more likely to make use of compositional methods or employ software tools that account for compositionality such as ANCOM or ALDeX2, we find that a very small proportion of metagenomic studies made use of compositional data analysis methods and none that made use of compositional methods for longitudinal microbiome data. Introduction It has long been known that plant and animal species are also host to a wide variety of microbial life ( Luckey, 1972 ; Savage, 1977 ). These microbes, generally known as the “host-associated microbiota” play a variety of roles in the development and biology of the associated host from necessary activity, such as immune function modulation and homeostasis ( Chervonsky, 2010 ; Muniz et al., 2012 ), to coincidental activity. There is at least one estimate that the number of these “host-associated microbes” that reside on or within the human body roughly equal the number of human somatic and germ cells that make-up an average human body ( Sender et al., 2016a , 2016b ). Together, the genomes of these microbial organisms (collectively defined as the human microbiome, the human metagenome, or the human microbiota) provide services to humans, and in some cases, may play a role in disease progression or severity ( Garrett, 2015 ; Paun et al., 2017 ). The first widespread technology for sequencing of genomic material, such as that used by the Human Genome Project ( Lander et al., 2001 ), was performed using a technique known as Sanger sequencing. Sanger sequencing sequences multiple copies of the same fragment of DNA at a time and is considered a low-throughput sequencing method ( Sanger et al., 1977 ). More modern sequencing technologies are now well established and are capable of sequencing millions of strands of DNA in parallel. Collectively the group of modern sequencers capable of such high-volume parallel genomic sequencing are known as high-throughput sequencing (HTS) or “next generation” sequencing technologies. The technology to sequence millions of strands of DNA in parallel allows large comprehensive genomic datasets to be generated, which can be analysed for insights into the genomic make-up of whole microbial communities. Host microbial composition is typically investigated using two main sequencing methodologies, 16S ribosomal RNA (rRNA) amplicon sequencing or whole metagenome shotgun (WMGS) sequencing. 16S rRNA sequencing amplifies specific ribosomal subunit genes found only in bacteria, while WMGS sequencing amplifies and sequences all DNA in a given sample including Archaeal and Viral genetic material. While the focus of this review is not on the differences in microbial sequencing pipelines, it is important to note that decisions made during sequencing and quality control can significantly impact the results obtained after analysis ( Caporaso et al., 2010 ; Dahlberg et al., 2019 ; McMurdie and Holmes, 2014 ; Schloss et al., 2009 ; Straub et al., 2019 ; Thorsen et al., 2016 ). Furthermore, the location from which samples are obtained as well as the manner in which they are stored can alter the reported microbial community composition and lead to the inclusion of contaminant DNA ( Dahlberg et al., 2019 ; Drengenes et al., 2019 ; Salter et al., 2014 ). The final output of the sequencing and quality control pipelines is a table of read counts assigned to each sample that is grouped by DNA sequence similarity of a specific taxonomic marker gene or reference genomic sequence, the resultant group is known as an operational taxonomic unit (OTU), and the resultant table is therefore known as an OTU table or abundance table. Abundance tables that relate OTU or sequence abundance produced at the end of both sequencing pipelines to sample data are typically analysed using similar statistical approaches ( Calle, 2019 ). Compositionality of metagenomic data The final sequence abundance output of HTS experiments are then analysed under the assumption that the output, such as abundance or OTU tables, provides independent counts of organisms or reads within each given sample ( Robinson et al., 2016 ), similar to how a birder would count species of birds on a beach, however there are issues with this perspective for microbiome data. Researchers are increasingly drawing attention to the fact that HTS machines have a maximum number of total reads that can be sequenced in a given run, also known as their throughput or maximum throughput ( Morey et al., 2013 ; Quince et al., 2017 ; Reuter et al., 2015 ). The consequence of a fixed maximum throughput in HTS machines means that the read counts generated by such experiments are constrained by an arbitrary constant sum, implying statistical non-independence in the individual OTU counts. The read count returned from a HTS run can therefore be thought of as a fixed-size random sample of the relative abundance of the genetic material in the underlying ecosystem ( Gloor, Macklaim et al., 2017 ). A further issue is the amplification process for 16S sequencing which preserves ratios but changes absolute abundances. For example, a library sequenced on an Illumina MiSeq could return approximately 25 million reads, whereas the same library sequenced on an Illumina HiSeq could return approximately 300 million reads. Constrained data of this nature are known as compositional data (CoDa). Compositional data are vectors of non-negative elements constrained to a constant sum. This constrained sum limitation inherent in HTS technology extends to all fixed capacity sequencing machines regardless of the number of taxa identified or analysed ( Gloor, Macklaim et al., 2017 ). A further problem relevant to metagenomic data and inherent in compositional data is that spurious correlation occurs when a dataset is subset into composition with fewer parts than the original composition, known as a subcomposition. This is particularly problematic because metagenomic sequencing experiments are always subcompositions of the full metagenomic environment ( Calle, 2019 ; Gloor, Wu et al., 2016 ). Decisions made in the sequencing quality control pipeline ( Bolyen et al., 2019 ; Schloss et al., 2009 ), as well as the maximum throughput of the HTS machine itself mean that not all OTUs present in the environment are present in the output results. Given the compositional nature of microbiome sequencing data, metagenome experiments alone cannot quantify the absolute number of organisms present within a given sample, but only provide data about the relative differences in abundance. HTS data is implicitly treated as compositional data when researchers choose to use microbiome data as relative abundance values, normalised counts, or rarefied count values ( Weiss et al., 2017 ). This review will investigate what statistical methods and approaches are being used to analyse and make inference from such metagenomic data. Methods Search strategy The following electronic databases were searched using the relevant keywords and search syntax appropriate to each database. The bibliographies of the articles were hand searched to identify additional studies. 1. PubMed (January 2000 to January 2022) 2. Scopus (January 2000 to January 2022) 3. Embase (January 2000 to January 2022) Only primary articles in the English language were considered. The search used a combination of the following search terms: (microbiota OR metagenom* OR microbiom* OR “microbiota”[MeSH]) AND (16S ribosomal RNA OR 16S rRNA OR WMGS OR NGS OR shotgun sequencing OR “High-Throughput Nucleotide Sequencing”[MeSH]) AND (analysis OR analyse*) NOT (protocol OR comparative study [pt] OR systematic review [ti] OR review [ti] OR review [pt]). A total of 6100 studies were extracted from electronic databases. Of the 6100 abstracts included, 381 were randomly selected to be assessed by the author to determine the study population, type of metagenomic sequencing used and analysis methods. To further assess the types of methods used in metagenomic studies an R script was written to extract keywords from all abstracts returned by the search strategy in order to determine sequencing methods and possible analysis methods for the studies that were not selected to be reviewed. All text extraction and analyses were performed in R (version 4.1.2) ( R Core Team, 2020 ). The full text of an additional five randomly selected abstracts was reviewed for each section of the review (normalisation methods, diversity metrics, and, inferential statistics for microbiome data). For full text review, 18 papers that discussed methods for compositional data in their abstracts were included. In total the full text of 38 randomly selected articles was reviewed. Results Of the 15 articles randomly selected from the full list of abstracts included in this review seven used rarefying in order to normalize abundance table count ( Azad et al., 2016 ; Craig et al., 2018 ; Ding et al., 2019 ; Hantsoo et al., 2019 ; Piyathilake et al., 2016 ; Sugita et al., 2016 ; Zhu et al., 2019 ). One article did not normalise the data prior to analysis, however the Bayesian model the authors developed included the use of a modelling offset to adjust a parametric generalized linear model (GLM) for fluctuating library size ( Pendegraft et al., 2019 ). Five articles did not normalise the microbiome data prior to analysis ( Chaudhari et al., 2019 ; Eren et al., 2015 ; Shi et al., 2020 ; Sjödin et al., 2019 ; Zhang et al., 2015 ). One did not perform any form of normalisation as only the quality of the reads was studied ( Ram et al., 2011 ). One article did not investigate the microbiome, instead performing RNA-seq on strains from a single bacteria species ( Hansen et al., 2011 ). Of the 381 abstracts evaluated for inclusion, 144 (37 %) included calculation of either an alpha or beta diversity metric in the abstract. Five papers that included an alpha or beta diversity metric were randomly selected for full text review in order to demonstrate the variety of approaches and use of diversity metrics in the metagenomic literature. The Shannon diversity index is one of the most common methods of presenting information about intra-sample diversity in the literature. Shannon diversity is typically calculated for the groups or subgroups of interest and then compared using standard statistical tests. For example, comparison of Shannon diversity across two groups was done using a Mann-Whitney U test ( Depner et al., 2017 ), and a t-test ( Dickson et al., 2015 ). In order to investigate the differences in Shannon diversity in more than two categories some researchers make use of boxplots and test for differences using ANOVA ( Dickson et al., 2015 ). In four of the articles randomly selected to evaluate diversity metrics a beta diversity metric was also calculated and used to perform clustering or comparison of the diversity between samples. One paper did not report any alpha diversity metric, however unweighted UniFrac distances were calculated as beta diversity metrics and group differences in beta diversity compared using a Mann-Whitney U test ( Iwauchi et al., 2019 ). Similarly, Depner et al. (2017) calculated UniFrac distances and compared differences in beta diversity between individuals with asthma and controls using Wilcoxon test. One randomly selected paper presented the visualized UniFrac distances after applying Principal Coordinates Analysis (PCoA) as an ordination method, and then performed clustering via partitioning around medoids for a number of distance metrics ( Koren et al., 2013 ). Finally unweighted and weighted UniFrac distances were compared between groups using Permutational Multivariate Analysis of Variance (PERMANOVA), and a novel method, known as MiRKAT, developed by the study authors ( Zhao et al., 2015 ). A wide variety of methods are used to analyse microbiome data, however testing for differences in diversity and testing for differential abundance are the most common goals of microbiome sequencing analyses. Of the five papers randomly selected to investigate analysis methods used in microbiome sequencing studies, one paper made use of the “standard QIIME pipeline” and ANOVA to test for differentially abundant bacterial genera from 16S rRNA sequencing ( Chaudhari et al., 2019 ). One paper used linear discriminant analysis (LDA) effect size (LEfSe) to identify Blautia oligotypes that show statistically significant differential abundances among groups of human and sewage samples versus animals ( Eren et al., 2015 ). LEfSe determines the features (organisms, clades, operational taxonomic units, genes, or functions) most likely to explain differences between classes by coupling standard tests for statistical significance with additional tests encoding biological consistency and effect relevance ( Segata et al., 2011 ). Another common software tool for differential abundance analysis is DESeq2 . DESeq2 was originally developed to analyse data from RNA-seq experiments, however it has been advocated for use in other experiments that generate sequence count data ( Calgaro et al., 2020 ). One randomly selected paper used DESeq2 to investigate relative changes in gut microbiota between allergic and nonallergic children over time ( Sjödin et al., 2019 ). One of the randomly selected papers created a novel Bayesian hierarchical negative binomial model to investigate differentially abundant OTUs found in metagenomic sequencing ( Pendegraft et al., 2019 ). One paper made use of generalized linear models to assess differential mean abundance between samples from women who had adverse childhood experiences and those that had not; multiple tests were corrected with the Benjamini-Hochberg method ( Hantsoo et al., 2019 ). Of the 6100 abstracts searched, only 19 (0.54%) abstracts included compositional data analysis methods, one of these papers provided an example and walkthrough of a metagenomic analysis using available compositional data software tools in the R programming language ( Macklaim and Gloor, 2018 ), six of these papers focussed on development of methods for cross-sectional compositional data ( Fang et al., 2015 ; Friedman and Alm, 2012 ; Kurtz et al., 2015 ; Quinn et al., 2018 ; Weiss et al., 2017 ; Woloszynek et al., 2019 ) and one paper investigated novel methodology for longitudinal compositional data ( Äijö et al., 2018 ). These 7 papers combined explored the development of a range of problems in compositional data analysis including the creation of networks from compositional data, probabilistic approaches to model overdispersion and sampling zeros using Gaussian Processes, and modelling types of zeroes present in microbiome data, but were all focussed on the development of novel methods. The remaining 12 (63%) papers that used compositional data analysis methods focussed on application of CoDa methods for metagenomic data. Discussion Normalisation of metagenomic data Comparison between samples within microbiome sequencing experiments is challenging because it is often very difficult to collect the same number of reads for each sample even if the same sample is sequenced multiple times. Differences in sample read count can arise from differences in the sequencing machine, difficulties in loading the same molar amounts of the sequencing libraries, or because of random variation ( Gloor, Macklaim et al., 2017 ). The total read depth is a strong confounder for any distance or diversity metric used and is thus also a strong confounder for any downstream analyses that make use of these distance or diversity metrics ( McMurdie and Holmes, 2014 ). Initially “rarefying” was a common method of normalising read counts between samples, however McMurdie and Holmes (2014) question the validity of rarefying because “although rarefying does equalise variances, it does so only by inflating the variances in all samples to the largest (worst) value among them at the cost of discriminating power (increased uncertainty)”. The prevalence of rarefying in microbiome sequencing studies is likely to due to software pipelines for analysing microbiome data that implement rarefying as the default method for dealing with large differences in read counts between samples. A variety of alternative normalisation strategies for metagenomic data have been proposed and are found in the literature. Commonly used alternatives to rarefying include the trimmed mean of M-values (TMM) ( McCarthy et al., 2012 ; Robinson and Oshlack, 2010 ), and the normalisation scaling factor calculated by DESeq2 ( Love et al., 2014 ). The utility of count normalisation strategies in general has also been questioned, as the number of counts returned by sequencing instruments do not contain information on the absolute number of molecules in the original environment due to the already described constraint to a fixed total number of reads. Researchers using a compositional data analysis approach normalise the data prior to analysis when sequence count data are converted to log-ratios ( Gloor, Macklaim et al., 2017 ). The choice of normalisation method can dramatically alter the ability of methods to distinguish signal from noise depending on the methods used ( Lin et al., 2016 ; Weiss et al., 2017 ). For example, the choice of normalisation methods can alter conclusions drawn from community comparisons using beta diversity metrics ( McKnight et al., 2019 ). Some normalisation methods, such as TMM normalisation, can give different results depending on how low abundance reads get removed from the dataset. Microbiome diversity metrics A key scientific objective of many metagenomic sequencing experiments is to investigate if a particular microbial composition is associated with a given outcome. For example, investigating if the diversity in a sample from an individual with a particular disease differs from that of a comparable healthy control. Microbiome sequencing studies have been used to investigate questions in a wide range of research areas including epidemiology ( Willner et al., 2009 ), clinical diagnostics, and as potential treatments for certain diseases ( Rossen et al., 2015 ). One common way this is done in the literature is to calculate a summary measure of the diversity present in a given sample, typically an alpha diversity measure, and then compare the mean species diversity between the two groups representing those samples. These diversity metrics are then often used in further downstream analyses, such as ordination or regression. Beta diversity, a measure of the dissimilarity of microbial communities between samples, is also commonly calculated in microbiome studies. Beta diversity dissimilarity matrices are used for ordination, such as principal coordinates analysis (PCoA) or principal component analysis (PCA), or classification depending on the research question. These methods allow for the visualization of complex high-dimensional dissimilarity matrices in lower dimensional spaces. Ordination plots can then be supplemented by the inclusion of sample metadata to visualize any possible sample clustering based on the metadata, for example PCA plots can be generated and coloured by sample disease status. Although the total number of reads in a sample is a strong confounder for dissimilarity-based methods ( McMurdie and Holmes, 2014 ), dissimilarity matrices generated after the data has been normalised may still provide useful information about sample dissimilarity. Three such dissimilarity matrices are commonly used in the literature; Bray-Curtis dissimilarity, Jensen-Shannon distance, and UniFrac distance ( Lozupone et al., 2011 ). The use of these diversity metrics in metagenomic studies has been criticised as inappropriate for compositional data, as the dissimilarity metrics do not take into account the compositional nature of microbiome data. Beta diversity metrics are sensitive to the total sequencing read depth of a sample, and many distance or dissimilarity methods discriminate between samples largely based on the most relatively abundant features in the samples, not on the features that are necessarily the most variable between samples ( Gorvitovskaia et al., 2016 ; Wong et al., 2016 ). This can lead to a substanial shift in the location of included samples in an ordination depending on included and excluded dataset features ( Gloor, Macklaim et al., 2017 ; Wong et al., 2016 ). Alternatives for compositional data such as Aitchison distance and adjustments to UniFrac distances have been proposed ( Wong et al., 2016 ) that do account for the compositional nature of the data. Inferential statistics for microbiome data Alpha and beta diversity metrics provide useful descriptive summaries of the microbial diversity present in a given sample. However, in many microbiome studies the aim is not merely to determine if samples are more or less diverse than one another but rather to determine which microbes are differentially abundant between the comparison groups of interest. In compositional data structures, when the proportion of one microorganism increases, the proportions of the other microorganisms in the sample must by definition decrease to allow for the standardised proportion of all OTUs to sum of 1 ( Aitchison, 1986 ; Gloor, Wu et al., 2016 ). Furthermore, microbiome data is sparse and high-dimensional, typically containing many more features than there are samples and many OTUs are either not detected in all samples or are removed during data quality control and pre-processing pipelines ( Paulson et al., 2013 ). Count distributions arising from microbiomes sequencing data are also known to be overdispersed ( McMurdie and Holmes, et al., 2014 ; Richards, 2008 ). The combination of these features of microbiome sequencing data makes determining true differences in microbial composition challenging and calls for the use of specialized analytic methods. Identifying differentially abundant features in microbiome data is a common goal of microbiome sequencing studies. As such there are a number of methods that have been applied to detecting differentially abundant features. Although not appropriate for compositional data, classical statistical methods such as parametric statistical tests (for example, two sample t-tests, Wilcoxon rank sum tests, and ANOVA) and measures of correlation, including Spearman’s rank correlation, and Pearson correlation are frequently used to investigate differentially abundant organisms and microbial networks. More sophisticated methods have been developed to assess differentially abundant features between samples by taking into account the nature of microbiome data as well as the number of hypothesis tests performed during differential abundance analysis. For example, one of the most common methods for performing differential abundance analyses on an entire microbiome sequencing dataset at once is implemented in the R package DESeq2 ( Love et al., 2014 ). Given the challenges associated with metagenomic data and multiple testing, methods for detection of differentially abundant organisms in microbiome sequencing studies are an active area of research. The two most common software pipelines for bioinformatic processing of metagenomic sequencing data are QIIME2 ( Bolyen et al., 2019 ) and mothur ( Schloss et al., 2009 ), both of these software pipelines include tools to prepare, quality control, and analyse data from metagenomic experiments. Failing to account for compositionality of metagenomic data means that findings for microbiome analysis carried out with these methods are particularly sensitive to negative correlation bias. Furthermore, many current methods for differential abundance analysis in microbiome studies have been shown to have unacceptably high false discovery rates. In some cases, depending on the method and data pre-processing pipeline the false discovery rates exceeded 80% ( Hawinkel et al., 2019 ; Weiss et al., 2017 ). Compositional data analysis of microbiome An important step in microbiome sequencing projects is visualization of the high-dimensional abundance tables. Typically, this is done by reducing the dimensionality of the data via PCoA, and then plotting the resulting principal coordinates. In the compositional data analysis framework, the equivalent of the PCoA plot is the variance-based compositional principal component (PCA) biplot ( Aitchison and Greenacre, 2002 ). Compositional alternatives for investigating the relative abundance of OTUs between groups have also been developed to replace traditional differential abundance analysis methods. For example, the ALDEx2 R package provides compositional methods for estimation of differential expression by performing statistical tests on the centered log-ratio (CLR) values from a modelled probability distribution of the dataset and reports the expected values of parametric and non-parametric statistical tests along with estimate of effect-size ( Fernandes, Macklaim et al., 2013 ; Fernandes, Reid et al., 2014 ). The ANCOM R package offers an alternative to ALDEx2 for differential abundance testing while taking into account compositionality. ANCOM performs statistical tests on point estimates of data transformed by an additive log-ratio (ALR) transformation, where invariant taxa are chosen as the reference for the ALR transformation ( Mandal et al., 2015 ). Although not yet widely used in the literature, there exist statistical methods for the analysis of compositional microbial data from cross-sectional microbiome sequencing experiments ( Gloor, Macklaim et al., 2017 ). For example, sequencing read counts are explicitly normalized across samples when analysed as compositional, because compositional analyses make use of log-ratio transformations of the abundance tables ( Aitchison, 1986 ). Log-ratios have the important mathematical property that their sample space is the whole real number line, and thus many standard statistical analyses that have been developed for real random variables can be used in conjunction with log-ratios while appropriately taking into account the compositionality of the data. Similarly in some applications the Aitchison distance, defined as the Euclidean distance between two log-ratio points, can be used as the compositional alternative to common ecological distance measures, such as Bray-Curtis, UniFrac, and Shannon distances ( Gloor, Macklaim et al., 2017 ). There are also proposed compositional alternatives to determine correlation and other measures of association for compositional data, such as the proportionality (Φ) ( Lovell et al., 2015 ) and proportionality coefficient ( p ) ( Erb and Notredame, 2016 ) metrics. The 12 papers that analysed microbiome data using methods for compositional data can be broadly classified into two groups; studies that analysed data from longitudinal microbiome experiments (three papers) and studies that analysed data from cross-sectional microbiome sequencing experiments (nine papers). Of the papers that analysed cross-sectional microbiome data three papers did not fully utilise a compositional data analysis pipeline, instead only making use of ANCOM to evaluate differentially abundant OTUs ( Chung et al., 2019 ; Iszatt et al., 2019 ; Lee et al., 2014 ). All other papers that were included in this review transformed the data via the CLR transform prior to performing statistical tests on the OTU tables. Although the utility of ecological diversity measures for metagenomic data is debated, three papers that utilised compositional methods reported conventional ecological diversity metrics, such as Shannon and UniFrac distances ( Genco et al., 2019 ; Peters et al., 2017 ; San-Juan-Vergara et al., 2018 ), instead of the compositional alternatives such as Aitchison distance. Only three studies used compositional methods to investigate the change in human microbial composition over time, a specific focus of this review, however all three of the studies evaluated time points independently from one another. For example, Dahl et al. (2018) investigated the gut microbiome in preterm infants at ten days, four months, and one year. The investigators performed a standard analysis of metagenomic data without accounting for compositionality, calculated traditional diversity metrics and evaluated differences in beta diversity using PERMANOVA. The authors then used ANCOM to test for differentially abundant OTUs at each time point separately. The other two papers that investigated the microbiome at two timepoints transformed all OTU counts via the centered log-ratio transformation. In the study by Dijkhuizen et al. (2019) zero values were dealt with by imputing the missing values using the robCompositions R package. Log ratio-transformed OTU relative abundances were then analysed using principal components analysis (PCA), and group difference in log-ratio transformed relative abundances were assessed with logistic regression. Notably, baseline, inactive disease, and persistent activity samples were analysed independently. Finally, Stanaway et al. (2016) measured exposure to seasonal pesticides during the spring-to-summer fruit-thinning season and the offseason. Prior to transformation into log-ratios alpha diversity measures were calculated, then OTU tables were then transformed using the CLR transform. The PCA on CLR transformed data was used as an exploratory examination of the beta diversity in spring/summer and winter samples separately. Group differences in PC1 scores from the PCA were compared using t-tests. The PCA data was then used to determine clusters in the spring/summer and winter season data respectively, in order to determine if a cluster with exposure to azinphos-methyl could be distinguished from non-exposure based on OTU table data. An area of open research in compositional methods for microbiome data is the application of compositional analysis methods to data from longitudinal microbiome sequencing studies where we must assume there exist correlation within individuals. Theoretical methods for the analysis of some forms of longitudinal dependence in compositional data have been developed ( Pawlowsky-Glahn et al., 2015 ), however these methods have not yet been applied or tested for use in microbiome sequencing studies. Conclusion This review had identified that many of the published analyses of microbiome sequencing data fail to account for the compositional nature of microbiome data, and the applicability of those methods to microbiome data. Given the rapid decrease in costs of sequencing entire microbial communities, it is likely that longitudinal studies of microbial communities will become more prevalent. Thus, longitudinal microbiome study designs and appropriate analysis methods will become critical for extracting useful information from microbiome data. Data availability Data and source code available from: https://github.com/luhann/lit-review-phd . This project contains the following underlying data: lit_review.r. R script and code used to process literature review results. pubmed_results.xml. Xml file containing full text of all open access articles in PubMed Central. lit_review_anon.csv. Annotated csv file containing annotations for the 381 abstracts screened by authors. Data are available under the terms of the Creative Commons Zero “No rights reserved” data waiver (CC0 1.0 Public domain dedication). Author contributions All authors contributed equally from conception, design. 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PubMed Abstract | Publisher Full Text Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 23 Apr 2024 ADD YOUR COMMENT Comment Author details Author details 1 Division of Epidemiology and Biostatistics, University of Cape Town, Cape Town, Western Cape, 7701, South Africa 2 Division of Infection and Immunity, University of Western Australia, Perth, Western Australia, 6907, Australia Patrick Hannan Roles: Conceptualization, Investigation, Writing – Original Draft Preparation, Writing – Review & Editing Mark Nicol Roles: Supervision, Writing – Review & Editing Maia Lesosky Roles: Conceptualization, Supervision, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information PH receives funding from the Drakenstein Child Health Study. The Drakenstein Child Health Study was funded by the Bill \& Melinda Gates Foundation (OPP1017641), Medical Research Council South Africa, National Research Foundation South Africa, National Institute of Health, and H3Africa (1U01AI110466–01A1). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Article Versions (1) version 1 Published: 23 Apr 2024, 13:369 https://doi.org/10.12688/f1000research.110605.1 Copyright © 2024 Hannan P et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article Hannan P, Nicol M and Lesosky M. A review of common methods used in the analysis of human microbiome sequencing data [version 1; peer review: 1 approved, 1 not approved] . F1000Research 2024, 13 :369 ( https://doi.org/10.12688/f1000research.110605.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 1 VERSION 1 PUBLISHED 23 Apr 2024 Views 0 Cite How to cite this report: Lowry CA. Reviewer Report For: A review of common methods used in the analysis of human microbiome sequencing data [version 1; peer review: 1 approved, 1 not approved] . F1000Research 2024, 13 :369 ( https://doi.org/10.5256/f1000research.122230.r294971 ) The direct URL for this report is: https://f1000research.com/articles/13-369/v1#referee-response-294971 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 22 Jul 2024 Christopher A. Lowry , University of Colorado Boulder, Boulder, CO, USA Approved VIEWS 0 https://doi.org/10.5256/f1000research.122230.r294971 This manuscript provides an assessment of analysis methods for 16S rRNA gene and metagenomic sequencing data based on a sample of studies published during a 22-year period between January 2000 and January 2022. The article is well-written and provides a ... Continue reading READ ALL This manuscript provides an assessment of analysis methods for 16S rRNA gene and metagenomic sequencing data based on a sample of studies published during a 22-year period between January 2000 and January 2022. The article is well-written and provides a useful account of how microbiome data, including measures of alpha diversity, beta diversity, and community composition, have been analyzed using statistical approaches. The article will be useful, particularly to researchers new to the microbiome field, in understanding many of the pitfalls of microbiome analysis, and may also be useful in courses and workshops designed to introduce trainees to microbiome analysis methods. The article highlights the need for continued development of statistical methods that are designed to analyze microbiome data, particularly longitudinal compositional data, statistical methods that have rarely been used during the period covered by the review. The microbiome field, including analysis tools, is rapidly evolving and, consequently, some new tools for analysis of compositional data, e.g., analysis of composition of microbiomes with bias correction (ANCOM-BC), 1 and its application 2 , are not mentioned. It is possibly worth highlighting efforts to establish reporting guidelines for microbiome research, such as the STORMS checklist. 3 Increased use of such reporting guidelines has potential to enhance the reproducibility of microbiome research and to enhance efforts to document, in the future, how microbiome analysis methods will undoubtedly continue to evolve as new tools become available. References: (Lin H, et al., 2020 [Ref 1]), (Lin H, et al., 2024 [Ref 2]), (Mirzayi C, et al., 2021 [Ref 3]) Is the topic of the review discussed comprehensively in the context of the current literature? Yes Are all factual statements correct and adequately supported by citations? Yes Is the review written in accessible language? Yes Are the conclusions drawn appropriate in the context of the current research literature? Yes References 1. Lin H, Peddada SD: Analysis of compositions of microbiomes with bias correction. Nat Commun . 2020; 11 (1): 3514 PubMed Abstract | Publisher Full Text 2. Lin H, Peddada SD: Multigroup analysis of compositions of microbiomes with covariate adjustments and repeated measures. Nat Methods . 2024; 21 (1): 83-91 PubMed Abstract | Publisher Full Text 3. Mirzayi C, Renson A, Genomic Standards Consortium, Massive Analysis and Quality Control Society, et al.: Reporting guidelines for human microbiome research: the STORMS checklist. Nat Med . 2021; 27 (11): 1885-1892 PubMed Abstract | Publisher Full Text Competing Interests: C.A.L. is Cofounder, Board Member, and Chief Scientific Officer of Mycobacteria Therapeutics Corporation, and is a member of the faculty of Clinical Care Options, LLC (CCO), Reston, Virginia, the Integrative Psychiatry Institute, Boulder, Colorado, the Institute for Brain Potential, Los Banos, California, and Intelligent Health Ltd, Reading, UK. In the previous three years, C.A.L. served on the Scientific Advisory Board of Immodulon Therapeutics Ltd., London, UK. Reviewer Expertise: Dr. Lowry’s research program focuses on understanding stress-related physiology and behavior with an emphasis on the microbiome-gut-brain axis, a program designed to lead to novel, microbiome-based interventions for the prevention of anxiety disorders, mood disorders, and trauma- and stressor-related disorders, such as posttraumatic stress disorder (PTSD). I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Lowry CA. Reviewer Report For: A review of common methods used in the analysis of human microbiome sequencing data [version 1; peer review: 1 approved, 1 not approved] . F1000Research 2024, 13 :369 ( https://doi.org/10.5256/f1000research.122230.r294971 ) The direct URL for this report is: https://f1000research.com/articles/13-369/v1#referee-response-294971 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Hu YJ. Reviewer Report For: A review of common methods used in the analysis of human microbiome sequencing data [version 1; peer review: 1 approved, 1 not approved] . F1000Research 2024, 13 :369 ( https://doi.org/10.5256/f1000research.122230.r278219 ) The direct URL for this report is: https://f1000research.com/articles/13-369/v1#referee-response-278219 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 30 May 2024 Yi-Juan Hu , Emory University, Atlanta, Georgia, USA Not Approved VIEWS 0 https://doi.org/10.5256/f1000research.122230.r278219 This manuscript aims to identify common analysis methods used in microbiome sequencing studies, including methods for data normalization and visualization, and compositional methods for association testing, with a focus on compositional methods in the analysis of longitudinal microbiome data. It ... Continue reading READ ALL This manuscript aims to identify common analysis methods used in microbiome sequencing studies, including methods for data normalization and visualization, and compositional methods for association testing, with a focus on compositional methods in the analysis of longitudinal microbiome data. It was found that a very small proportion of microbiome studies made use of compositional data analysis methods. Major Comments: -. Timeliness of Literature Search: The manuscript limited the search of literature to studies published before January 2022, which was two and a half years ago. This approach misses a large number of more recent publications and fails to serve as a timely review of the literature. -. Clarity of Search Terms: The search terms should be explained in plain language rather than computer code to improve readability and accessibility. -. Summary Table: It would be more readable and informative to provide a table that summarizes the type of analysis, common methods for the analysis, and proportions of papers that used these methods. -. Scope of Review: The review was limited to a very small proportion of the articles (e.g., 381 abstracts, 38 full articles) that satisfied the search criteria. It would be more informative to expand the review to include more articles. Is the topic of the review discussed comprehensively in the context of the current literature? No Are all factual statements correct and adequately supported by citations? Partly Is the review written in accessible language? Yes Are the conclusions drawn appropriate in the context of the current research literature? Partly Competing Interests: No competing interests were disclosed. Reviewer Expertise: Biostatistics, bioinformatics, microbiome analysis I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Hu YJ. Reviewer Report For: A review of common methods used in the analysis of human microbiome sequencing data [version 1; peer review: 1 approved, 1 not approved] . F1000Research 2024, 13 :369 ( https://doi.org/10.5256/f1000research.122230.r278219 ) The direct URL for this report is: https://f1000research.com/articles/13-369/v1#referee-response-278219 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 23 Apr 2024 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 2 Version 1 23 Apr 24 read read Yi-Juan Hu , Emory University, Atlanta, USA Christopher A. Lowry , University of Colorado Boulder, Boulder, CO, USA Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2024 Lowry C. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 22 Jul 2024 | for Version 1 Christopher A. Lowry , University of Colorado Boulder, Boulder, CO, USA 0 Views copyright © 2024 Lowry C. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions This manuscript provides an assessment of analysis methods for 16S rRNA gene and metagenomic sequencing data based on a sample of studies published during a 22-year period between January 2000 and January 2022. The article is well-written and provides a useful account of how microbiome data, including measures of alpha diversity, beta diversity, and community composition, have been analyzed using statistical approaches. The article will be useful, particularly to researchers new to the microbiome field, in understanding many of the pitfalls of microbiome analysis, and may also be useful in courses and workshops designed to introduce trainees to microbiome analysis methods. The article highlights the need for continued development of statistical methods that are designed to analyze microbiome data, particularly longitudinal compositional data, statistical methods that have rarely been used during the period covered by the review. The microbiome field, including analysis tools, is rapidly evolving and, consequently, some new tools for analysis of compositional data, e.g., analysis of composition of microbiomes with bias correction (ANCOM-BC), 1 and its application 2 , are not mentioned. It is possibly worth highlighting efforts to establish reporting guidelines for microbiome research, such as the STORMS checklist. 3 Increased use of such reporting guidelines has potential to enhance the reproducibility of microbiome research and to enhance efforts to document, in the future, how microbiome analysis methods will undoubtedly continue to evolve as new tools become available. References: (Lin H, et al., 2020 [Ref 1]), (Lin H, et al., 2024 [Ref 2]), (Mirzayi C, et al., 2021 [Ref 3]) Is the topic of the review discussed comprehensively in the context of the current literature? Yes Are all factual statements correct and adequately supported by citations? Yes Is the review written in accessible language? Yes Are the conclusions drawn appropriate in the context of the current research literature? Yes References 1. Lin H, Peddada SD: Analysis of compositions of microbiomes with bias correction. Nat Commun . 2020; 11 (1): 3514 PubMed Abstract | Publisher Full Text 2. Lin H, Peddada SD: Multigroup analysis of compositions of microbiomes with covariate adjustments and repeated measures. Nat Methods . 2024; 21 (1): 83-91 PubMed Abstract | Publisher Full Text 3. Mirzayi C, Renson A, Genomic Standards Consortium, Massive Analysis and Quality Control Society, et al.: Reporting guidelines for human microbiome research: the STORMS checklist. Nat Med . 2021; 27 (11): 1885-1892 PubMed Abstract | Publisher Full Text Competing Interests C.A.L. is Cofounder, Board Member, and Chief Scientific Officer of Mycobacteria Therapeutics Corporation, and is a member of the faculty of Clinical Care Options, LLC (CCO), Reston, Virginia, the Integrative Psychiatry Institute, Boulder, Colorado, the Institute for Brain Potential, Los Banos, California, and Intelligent Health Ltd, Reading, UK. In the previous three years, C.A.L. served on the Scientific Advisory Board of Immodulon Therapeutics Ltd., London, UK. Reviewer Expertise Dr. Lowry’s research program focuses on understanding stress-related physiology and behavior with an emphasis on the microbiome-gut-brain axis, a program designed to lead to novel, microbiome-based interventions for the prevention of anxiety disorders, mood disorders, and trauma- and stressor-related disorders, such as posttraumatic stress disorder (PTSD). I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (0) Lowry CA. Peer Review Report For: A review of common methods used in the analysis of human microbiome sequencing data [version 1; peer review: 1 approved, 1 not approved] . F1000Research 2024, 13 :369 ( https://doi.org/10.5256/f1000research.122230.r294971) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-369/v1#referee-response-294971 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2024 Hu Y. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 30 May 2024 | for Version 1 Yi-Juan Hu , Emory University, Atlanta, Georgia, USA 0 Views copyright © 2024 Hu Y. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Not Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions This manuscript aims to identify common analysis methods used in microbiome sequencing studies, including methods for data normalization and visualization, and compositional methods for association testing, with a focus on compositional methods in the analysis of longitudinal microbiome data. It was found that a very small proportion of microbiome studies made use of compositional data analysis methods. Major Comments: -. Timeliness of Literature Search: The manuscript limited the search of literature to studies published before January 2022, which was two and a half years ago. This approach misses a large number of more recent publications and fails to serve as a timely review of the literature. -. Clarity of Search Terms: The search terms should be explained in plain language rather than computer code to improve readability and accessibility. -. Summary Table: It would be more readable and informative to provide a table that summarizes the type of analysis, common methods for the analysis, and proportions of papers that used these methods. -. Scope of Review: The review was limited to a very small proportion of the articles (e.g., 381 abstracts, 38 full articles) that satisfied the search criteria. It would be more informative to expand the review to include more articles. Is the topic of the review discussed comprehensively in the context of the current literature? No Are all factual statements correct and adequately supported by citations? Partly Is the review written in accessible language? Yes Are the conclusions drawn appropriate in the context of the current research literature? Partly Competing Interests No competing interests were disclosed. Reviewer Expertise Biostatistics, bioinformatics, microbiome analysis I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above. reply Respond to this report Responses (0) Hu YJ. Peer Review Report For: A review of common methods used in the analysis of human microbiome sequencing data [version 1; peer review: 1 approved, 1 not approved] . F1000Research 2024, 13 :369 ( https://doi.org/10.5256/f1000research.122230.r278219) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-369/v1#referee-response-278219 Alongside their report, reviewers assign a status to the article: Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions Adjust parameters to alter display View on desktop for interactive features Includes Interactive Elements View on desktop for interactive features Competing Interests Policy Provide sufficient details of any financial or non-financial competing interests to enable users to assess whether your comments might lead a reasonable person to question your impartiality. 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