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

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 four unsupervised research tasks: clustering of bacterial genera, hub detection in microbial networks, differential microbial network analysis, and clustering of samples. 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 four 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

NetCoMi: network construction and comparison for microbiome data in R

MOTIVATION Estimating microbial association networks from high-throughput sequencing data is a common exploratory data analysis approach aiming at understanding the complex interplay of microbial communities in their natural habitat. Statistical network estimation workflows comprise several analysis steps, including methods for zero handling, data normalization and computing microbial associations. Since microbial interactions are likely to change between conditions, e.g. between healthy individuals and patients, identifying network differences between groups is often an integral secondary analysis step. Thus far, however, no unifying computational tool is available that facilitates the whole analysis workflow of constructing, analysing and comparing microbial association networks from high-throughput sequencing data. RESULTS Here, we introduce NetCoMi (Network Construction and comparison for Microbiome data), an R package that integrates existing methods for each analysis step in a single reproducible computational workflow. The package offers functionality for constructing and analysing single microbial association networks as well as quantifying network differences. This enables insights into whether single taxa, groups of taxa or the overall network structure change between groups. NetCoMi also contains functionality for constructing differential networks, thus allowing to assess whether single pairs of taxa are differentially associated between two groups. Furthermore, NetCoMi facilitates the construction and analysis of dissimilarity networks of microbiome samples, enabling a high-level graphical summary of the heterogeneity of an entire microbiome sample collection. We illustrate NetCoMi's wide applicability using data sets from the GABRIELA study to compare microbial associations in settled dust from children's rooms between samples from two study centers (Ulm and Munich). AVAILABILITY R scripts used for producing the examples shown in this manuscript are provided as supplementary data. The NetCoMi package, together with a tutorial, is available at https://github.com/stefpeschel/NetCoMi. CONTACT Tel:+49 89 3187 43258; [email protected]. SUPPLEMENTARY INFORMATION Supplementary data are available at Briefings in Bioinformatics online

Analysis of death in major trauma: value of prompt post mortem computed tomography (pmCT) in comparison to office hour autopsy

Background: To analyze diagnostic accuracy of prompt post mortem Computed Tomography (pmCT) in determining causes of death in patients who died during trauma room management and to compare the results to gold standard autopsy during office hours. Methods: Multiple injured patients who died during trauma room care were enrolled. PmCT was performed immediately followed by autopsy during office hours. PmCT and autopsy were analyzed primarily regarding pmCT ability to find causes of death and secondarily to define exact causes of death including accurate anatomic localizations. For the secondary analysis data was divided in group-I with equal results of pmCT and autopsy, group-II with autopsy providing superior results and group-III with pmCT providing superior information contributing to but not majorly causing death. Results: Seventeen multiple trauma patients were enrolled. Since multiple trauma patients were enrolled more injuries than patients are provided. Eight patients sustained deadly head injuries (47.1 %), 11 chest (64.7 %), 4 skeletal system (23.5 %) injuries and one patient drowned (5.8 %). Primary analysis revealed in 16/17 patients (94.1 %) causes of death in accordance with autopsy. Secondary analysis revealed in 9/17 cases (group-I) good agreement of autopsy and pmCT. In seven cases autopsy provided superior results (group-II) whereas in 1 case pmCT found more information (group-III). Discussion: The presented work studied the diagnostic value of pmCT in defining causes of death in comparison to standard autopsy. Primary analysis revealed that in 94.1% of cases pmCT was able to define causes of death even if only indirect signs were present. Secondary analysis showed that pmCT and autopsy showed equal results regarding causes of death in 52.9%. Conclusions: PmCT is useful in traumatic death allowing for an immediate identification of causes of death and providing detailed information on bony lesions, brain injuries and gas formations. It is advisable to conduct pmCT especially in cases without consent to autopsy to gain information about possible causes of death and to rule out possible clinical errors

The Bcl10–Malt1 complex segregates FcɛRI-mediated nuclear factor κB activation and cytokine production from mast cell degranulation

Mast cells are pivotal effector cells in IgE-mediated allergic inflammatory diseases. Central for mast cell activation are signals from the IgE receptor FcɛRI, which induce cell degranulation with the release of preformed mediators and de novo synthesis of proinflammatory leukotrienes and cytokines. How these individual mast cell responses are differentially controlled is still unresolved. We identify B cell lymphoma 10 (Bcl10) and mucosa-associated lymphoid tissue 1 (Malt1) as novel key regulators of mast cell signaling. Mice deficient for either protein display severely impaired IgE-dependent late phase anaphylactic reactions. Mast cells from these animals neither activate nuclear factor κB (NF-κB) nor produce tumor necrosis factor α or interleukin 6 upon FcɛRI ligation even though proximal signaling, degranulation, and leukotriene secretion are normal. Thus, Bcl10 and Malt1 are essential positive mediators of FcɛRI-dependent mast cell activation that selectively uncouple NF-κB–induced proinflammatory cytokine production from degranulation and leukotriene synthesis

Nonlinear polarization holography of nanoscale iridium films

The phasing problem of heterodyne-detected nonlinear spectroscopy states that the relative time delay between the exciting pulses and a local oscillator must be known with subcycle precision to separate absorptive and dispersive contributions. Here, a solution to this problem is presented which is the time-domain analogue of holographic interferometry, in which the comparison of two holograms reveals changes of an objects size and position with interferometric precision (i.e. to fractions of a wavelength of light). The introduced method, called nonlinear polarization holography, provides equivalent information as attosecond nonlinear polarization spectroscopy but has the advantage of being all-optical instead of using an attosecond streak camera. Nonlinear polarization holography is used here to retrieve the time-domain nonlinear response of a nanoscale iridium film to an ultrashort femtosecond pulse. Using density matrix calculations it is shown that the knowledge of the nonlinear response with subcycle precision allows to distinguish excitation and relaxation mechanisms of low-energetic electrons that depend on the nanoscale structure of the iridium film

Maturation of the gut microbiome during the first year of life contributes to the protective farm effect on childhood asthma

Peer reviewedPostprin