<i>manta</i>- a clustering algorithm for weighted ecological networks

Röttjers, Lisa; Faust, Karoline

doi:10.1101/807511

Cited by 3 publications

(4 citation statements)

References 33 publications

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“…For each research task, we applied multiple method combinations to the discovery data, involving normalization methods (clr [30], mclr [31], and VST [32]), association estimation (Pearson correlation, Spearman correlation, latentcor [33], SPRING [31], and proportionality [34]), sparsification ( t -test, threshold method, and neighborhood selection), and, for the first research task, clustering (hierarchical clustering, spectral clustering [35], fast greedy modularity optimization [36], Louvain method for community detection [37], and manta [38]). Detailed descriptions of the combinations are given in Section 4.3.…”

Section: Resultsmentioning

confidence: 99%

“…Algorithms from the first category are based on (dis)similarity matrices: hierarchical clustering and spectral clustering [35]. Algorithms from the second category are based on networks with weighted edges: fast greedy modularity optimization [36], the Louvain method for community detection [37], and the manta algorithm [38].…”

Section: Methodsmentioning

confidence: 99%

“…For fast greedy modularity optimization and the Louvain method for community detection, we used the R package igraph [56]. For clustering with manta, we accessed the Python implementation [41] with the reticulate interface for R [57]. Orbit counts for the calculation of the GCD were generated with the R package orca [58].…”

Section: Methodsmentioning

confidence: 99%

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Over-optimism in unsupervised microbiome analysis: Insights from network learning and clustering

Ullmann

Peschel

Finger

et al. 2022

Preprint

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In recent years, unsupervised analysis of microbiome data, such as microbial network analysis and clustering, has increased in popularity. Many new statistical and computational methods have been proposed for these tasks. This multiplicity of analysis strategies poses a challenge for researchers, who are often unsure which method(s) to use and might be tempted to try different methods on their dataset to look for the "best" ones. However, if only the best results are selectively reported, this may cause over-optimism: the "best" method is overly fitted to the specific dataset, and the results might be non-replicable on validation data. Such effects will ultimately hinder research progress. Yet so far, these topics have been given little attention in the context of unsupervised microbiome analysis. In our illustrative study, we aim to quantify over-optimism effects in this context. We model the approach of a hypothetical microbiome researcher who undertakes three unsupervised research tasks: clustering of bacterial genera, hub detection in microbial networks, and differential microbial network analysis. While these tasks are unsupervised, the researcher might still have certain expectations as to what constitutes interesting results. We translate these expectations into concrete evaluation criteria that the hypothetical researcher might want to optimize. We then randomly split an exemplary dataset from the American Gut Project into discovery and validation sets multiple times. For each research task, multiple method combinations (e.g., methods for data normalization, network generation, and/or clustering) are tried on the discovery data, and the combination that yields the best result according to the evaluation criterion is chosen. While the hypothetical researcher might only report this result, we also apply the "best" method combination to the validation dataset. The results are then compared between discovery and validation data. In all three research tasks, there are notable over-optimism effects; the results on the validation data set are worse compared to the discovery data, averaged over multiple random splits into discovery/validation data. Our study thus highlights the importance of validation and replication in microbiome analysis to obtain reliable results and demonstrates that the issue of over-optimism goes beyond the context of statistical testing and fishing for significance.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Over-optimism in unsupervised microbiome analysis: Insights from network learning and clustering

Ullmann

Peschel

Finger

et al. 2022

Preprint

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“…Therefore, taxa in the CAN were compared to taxa reported as indicators of high microbial abundance or low microbial abundance (Moitinho-Silva, Steinert, et al, 2017). CAN network clusters were identified with manta (Röttjers & Faust, 2020b), as this algorithm has been designed to handle negative edges in the CAN. To run the clustering algorithm, default settings were used, except the number of iterations and permutations, which was set to 200.…”

Section: /25mentioning

confidence: 99%

A framework for comparing microbial networks reveals core associations

Röttjers

Vandeputte

Raes

et al. 2020

Preprint

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Microbial network construction and analysis is an important tool in microbial ecology. As microbial interactions are challenging to infer experimentally, such networks are often constructed from statistically inferred associations and may not represent ecological interactions. Hence, microbial association networks contain a large number of errors and their derived properties do not necessarily reflect true community structure. Such errors can be identified with the use of appropriate null models. We have developed anuran, a toolbox for investigation of noisy networks with null models, for identification of non-random patterns in groups of association networks. This toolbox compares multiple networks to identify conserved subsets (core association networks, CANs) and other network properties that are shared across all networks. Such groups of networks can be generated from a collection of time series data or from cross-sectional sample sets. We use data from the Global Sponge Project to demonstrate that different orders of sponges have a larger CAN than expected at random.

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manta: a Clustering Algorithm for Weighted Ecological Networks

Röttjers

Faust

2020

mSystems

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Microbial network inference and analysis have become successful approaches to extract biological hypotheses from microbial sequencing data. Network clustering is a crucial step in this analysis. Here, we present a novel heuristic network clustering algorithm, manta, which clusters nodes in weighted networks. In contrast to existing algorithms, manta exploits negative edges while differentiating between weak and strong cluster assignments. For this reason, manta can tackle gradients and is able to avoid clustering problematic nodes. In addition, manta assesses the robustness of cluster assignment, which makes it more robust to noisy data than most existing tools. On noise-free synthetic data, manta equals or outperforms existing algorithms, while it identifies biologically relevant subcompositions in real-world data sets. On a cheese rind data set, manta identifies groups of taxa that correspond to intermediate moisture content in the rinds, while on an ocean data set, the algorithm identifies a cluster of organisms that were reduced in abundance during a transition period but did not correlate strongly to biochemical parameters that changed during the transition period. These case studies demonstrate the power of manta as a tool that identifies biologically informative groups within microbial networks. IMPORTANCE manta comes with unique strengths, such as the abilities to identify nodes that represent an intermediate between clusters, to exploit negative edges, and to assess the robustness of cluster membership. manta does not require parameter tuning, is straightforward to install and run, and can be easily combined with existing microbial network inference tools.

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manta- a clustering algorithm for weighted ecological networks

Cited by 3 publications

References 33 publications

Over-optimism in unsupervised microbiome analysis: Insights from network learning and clustering

Over-optimism in unsupervised microbiome analysis: Insights from network learning and clustering

A framework for comparing microbial networks reveals core associations

manta: a Clustering Algorithm for Weighted Ecological Networks

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