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Motivation Co-inertia analysis (CIA) is a multivariate statistical analysis method that can assess relationships and trends in two sets of data. Recently CIA has been used for an integrative analysis of multiple high-dimensional omics data. However, for classical CIA, all elements in the loading vectors are nonzero, presenting a challenge for the interpretation when analyzing omics data. For other multivariate statistical methods such as canonical correlation analysis (CCA), penalized least squares (PLS), various approaches have been proposed to produce sparse loading vectors via l 1-penalization/constraint. We propose a novel CIA method that uses l 1-penalization to induce sparsity in estimators of loading vectors. Our method simultaneously conducts model fitting and variable selection. Also, we propose another CIA method that incorporates structure/network information such as those from functional genomics, besides using sparsity penalty so that one can get biologically meaningful and interpretable results. Results Extensive simulations demonstrate that our proposed penalized CIA methods achieve the best or close to the best performance compared to the existing CIA method in terms of feature selection and recovery of true loading vectors. Also, we apply our methods to the integrative analysis of gene expression data and protein abundance data from the NCI-60 cancer cell lines. Our analysis of the NCI-60 cancer cell line data reveals meaningful variables for cancer diseases and biologically meaningful results that are consistent with previous studies. Availability and implementation Our algorithms are implemented as an R package which is freely available at: https://www.med.upenn.edu/long-lab/. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Full genome data sets are currently being explored on a regular basis to infer phylogenetic trees, but there are often discordances among the trees produced by different genes. An important goal in phylogenomics is to identify which individual gene and species produce the same phylogenetic tree and are thus likely to share the same evolutionary history. On the other hand, it is also essential to identify which genes and species produce discordant topologies and therefore evolve in a different way or represent noise in the data. The latter are outlier genes or species and they can provide a wealth of information on potentially interesting biological processes, such as incomplete lineage sorting, hybridization, and horizontal gene transfers. Here, we propose a new method to explore the genomic tree space and detect outlier genes and species based on multiple co-inertia analysis (MCOA), which efficiently captures and compares the similarities in the phylogenetic topologies produced by individual genes. Our method allows the rapid identification of outlier genes and species by extracting the similarities and discrepancies, in terms of the pairwise distances, between all the species in all the trees, simultaneously. This is achieved by using MCOA, which finds successive decomposition axes from individual ordinations (i.e., derived from distance matrices) that maximize a covariance function. The method is freely available as a set of R functions. The source code and tutorial can be found online at http://phylomcoa.cgenomics.org. [ABSTRACT FROM PUBLISHER]

Recent advances in NGS sequencing, microarrays and mass spectrometry for omics data production have enabled the generation and collection of different modalities of high-dimensional molecular data. The integration of multiple omics datasets is a statistical challenge, due to the limited number of individuals, the high number of variables and the heterogeneity of the datasets to integrate. Recently, a lot of tools have been developed to solve the problem of integrating omics data including canonical correlation analysis, matrix factorization and SM. These commonly used techniques aim to analyze simultaneously two or more types of omics. In this article, we compare a panel of 13 unsupervised methods based on these different approaches to integrate various types of multi-omics datasets: iClusterPlus, regularized generalized canonical correlation analysis, sparse generalized canonical correlation analysis, multiple co-inertia analysis (MCIA), integrative-NMF (intNMF), SNF, MoCluster, mixKernel, CIMLR, LRAcluster, ConsensusClustering, PINSPlus and multi-omics factor analysis (MOFA). We evaluate the ability of the methods to recover the subgroups and the variables that drive the clustering on eight benchmarks of simulation. MOFA does not provide any results on these benchmarks. For clustering, SNF, MoCluster, CIMLR, LRAcluster, ConsensusClustering and intNMF provide the best results. For variable selection, MoCluster outperforms the others. However, the performance of the methods seems to depend on the heterogeneity of the datasets (especially for MCIA, intNMF and iClusterPlus). Finally, we apply the methods on three real studies with heterogeneous data and various phenotypes. We conclude that MoCluster is the best method to analyze these omics data. Availability: An R package named CrIMMix is available on GitHub at https://github.com/CNRGH/crimmix to reproduce all the results of this article. [ABSTRACT FROM AUTHOR]

With rapidly evolving high-throughput technologies and consistently decreasing costs, collecting multimodal omics data in large-scale studies has become feasible. Although studying multiomics provides a new comprehensive approach in understanding the complex biological mechanisms of human diseases, the high dimensionality of omics data and the complexity of the interactions among various omics levels in contributing to disease phenotypes present tremendous analytical challenges. There is a great need of novel analytical methods to address these challenges and to facilitate multiomics analyses. In this paper, we propose a multimodal functional deep learning (MFDL) method for the analysis of high-dimensional multiomics data. The MFDL method models the complex relationships between multiomics variants and disease phenotypes through the hierarchical structure of deep neural networks and handles high-dimensional omics data using the functional data analysis technique. Furthermore, MFDL leverages the structure of the multimodal model to capture interactions between different types of omics data. Through simulation studies and real-data applications, we demonstrate the advantages of MFDL in terms of prediction accuracy and its robustness to the high dimensionality and noise within the data. [ABSTRACT FROM AUTHOR]

With the recent developments in the field of multi-omics integration, the interest in factors such as data preprocessing, choice of the integration method and the number of different omics considered had increased. In this work, the impact of these factors is explored when solving the problem of sample classification, by comparing the performances of five unsupervised algorithms: Multiple Canonical Correlation Analysis, Multiple Co-Inertia Analysis, Multiple Factor Analysis, Joint and Individual Variation Explained and Similarity Network Fusion. These methods were applied to three real data sets taken from literature and several ad hoc simulated scenarios to discuss classification performance in different conditions of noise and signal strength across the data types. The impact of experimental design, feature selection and parameter training has been also evaluated to unravel important conditions that can affect the accuracy of the result. [ABSTRACT FROM AUTHOR]

Phylogenomic analyses of long sequences, consisting of many genes and genomic segments, reconstruct organismal relationships with high statistical confidence. But, inferred relationships can be sensitive to excluding just a few sequences. Currently, there is no direct way to identify fragile relationships and the associated individual gene sequences in species. Here, we introduce novel metrics for gene-species sequence concordance and clade probability derived from evolutionary sparse learning models. We validated these metrics using fungi, plant, and animal phylogenomic datasets, highlighting the ability of the new metrics to pinpoint fragile clades and the sequences responsible. The new approach does not necessitate the investigation of alternative phylogenetic hypotheses, substitution models, or repeated data subset analyses. Our methodology offers a streamlined approach to evaluating major inferred clades and identifying sequences that may distort reconstructed phylogenies using large datasets. [ABSTRACT FROM AUTHOR]

Biomedical research now commonly integrates diverse data types or views from the same individuals to better understand the pathobiology of complex diseases, but the challenge lies in meaningfully integrating these diverse views. Existing methods often require the same type of data from all views (cross-sectional data only or longitudinal data only) or do not consider any class outcome in the integration method, which presents limitations. To overcome these limitations, we have developed a pipeline that harnesses the power of statistical and deep learning methods to integrate cross-sectional and longitudinal data from multiple sources. In addition, it identifies key variables that contribute to the association between views and the separation between classes, providing deeper biological insights. This pipeline includes variable selection/ranking using linear and nonlinear methods, feature extraction using functional principal component analysis and Euler characteristics, and joint integration and classification using dense feed-forward networks for cross-sectional data and recurrent neural networks for longitudinal data. We applied this pipeline to cross-sectional and longitudinal multiomics data (metagenomics, transcriptomics and metabolomics) from an inflammatory bowel disease (IBD) study and identified microbial pathways, metabolites and genes that discriminate by IBD status, providing information on the etiology of IBD. We conducted simulations to compare the two feature extraction methods. [ABSTRACT FROM AUTHOR]

Recent advances in sequencing, mass spectrometry, and cytometry technologies have enabled researchers to collect multiple 'omics data types from a single sample. These large datasets have led to a growing consensus that a holistic approach is needed to identify new candidate biomarkers and unveil mechanisms underlying disease etiology, a key to precision medicine. While many reviews and benchmarks have been conducted on unsupervised approaches, their supervised counterparts have received less attention in the literature and no gold standard has emerged yet. In this work, we present a thorough comparison of a selection of six methods, representative of the main families of intermediate integrative approaches (matrix factorization, multiple kernel methods, ensemble learning, and graph-based methods). As non-integrative control, random forest was performed on concatenated and separated data types. Methods were evaluated for classification performance on both simulated and real-world datasets, the latter being carefully selected to cover different medical applications (infectious diseases, oncology, and vaccines) and data modalities. A total of 15 simulation scenarios were designed from the real-world datasets to explore a large and realistic parameter space (e.g. sample size, dimensionality, class imbalance, effect size). On real data, the method comparison showed that integrative approaches performed better or equally well than their non-integrative counterpart. By contrast, DIABLO and the four random forest alternatives outperform the others across the majority of simulation scenarios. The strengths and limitations of these methods are discussed in detail as well as guidelines for future applications. [ABSTRACT FROM AUTHOR]

Motivation Although the human microbiome plays a key role in health and disease, the biological mechanisms underlying the interaction between the microbiome and its host are incompletely understood. Integration with other molecular profiling data offers an opportunity to characterize the role of the microbiome and elucidate therapeutic targets. However, this remains challenging to the high dimensionality, compositionality, and rare features found in microbiome profiling data. These challenges necessitate the use of methods that can achieve structured sparsity in learning cross-platform association patterns. Results We propose Tree-Aggregated factor RegressiOn (TARO) for the integration of microbiome and metabolomic data. We leverage information on the taxonomic tree structure to flexibly aggregate rare features. We demonstrate through simulation studies that TARO accurately recovers a low-rank coefficient matrix and identifies relevant features. We applied TARO to microbiome and metabolomic profiles gathered from subjects being screened for colorectal cancer to understand how gut microrganisms shape intestinal metabolite abundances. Availability and implementation The R package TARO implementing the proposed methods is available online at https://github.com/amishra-stats/taro-package. [ABSTRACT FROM AUTHOR]

Long-term temporal variation in the community structure of fish species richness over the period 1953–1994 along the Norwegian Skagerrak coast is analysed in relation to abiotic environmental factors (oxygen saturation, salinity and temperature respectively at the sea surface, 10 and 50 m depth). Data on the observed number of fish species derive from a long-term and ongoing beach-seine monitoring programme being conducted each autumn. Data on observed fish species richness are analysed using multivariate techniques especially designed for the coupling of environmental and biological tables (Co-inertia analysis). The correlation structure of the abiotic variables is investigated by applying the STATIS (“S tructuration des T ableaux A T rois I ndices de la S tatistique”) multivariate technique. We demonstrate that environmental variables in the deeper waters are linked to the community structure of the coastal zone of the Southern Skagerrak Coast. It is suggested that this effect probably operates through the Norwegian Coastal Current (NCC) and is a proxy measure for advective processes. Copyright 2002 International Council for the Exploration of the Sea. Published by Elsevier Science Ltd. All rights reserved. [Copyright &y& Elsevier]

Multi-omic analyses are necessary to understand the complex biological processes taking place at the tissue and cell level, but also to make reliable predictions about, for example, disease outcome. Several linear methods exist that create a joint embedding using paired information per sample, but recently there has been a rise in the popularity of neural architectures that embed paired -omics into the same non-linear manifold. This work describes a head-to-head comparison of linear and non-linear joint embedding methods using both bulk and single-cell multi-modal datasets. We found that non-linear methods have a clear advantage with respect to linear ones for missing modality imputation. Performance comparisons in the downstream tasks of survival analysis for bulk tumor data and cell type classification for single-cell data lead to the following insights: First, concatenating the principal components of each modality is a competitive baseline and hard to beat if all modalities are available at test time. However, if we only have one modality available at test time, training a predictive model on the joint space of that modality can lead to performance improvements with respect to just using the unimodal principal components. Second, -omic profiles imputed by neural joint embedding methods are realistic enough to be used by a classifier trained on real data with limited performance drops. Taken together, our comparisons give hints to which joint embedding to use for which downstream task. Overall, product-of-experts performed well in most tasks and was reasonably fast, while early integration (concatenation) of modalities did quite poorly. [ABSTRACT FROM AUTHOR]

Motivation: Microarrays are widely used to measure gene expression differences between sets of biological samples. Many of these differences will be due to differences in the activities of transcription factors. In principle, these differences can be detected by associating motifs in promoters with differences in gene expression levels between the groups. In practice, this is hard to do.Results: We combine correspondence analysis, between group analysis and co-inertia analysis to determine which motifs, from a database of promoter motifs, are strongly associated with differences in gene expression levels. Given a database of motifs and gene expression levels from a set of arrays, the method produces a ranked list of motifs associated with any specified split in the arrays. We give an example using the Gene Atlas compendium of gene expression levels for human tissues where we search for motifs that are associated with expression in central nervous system (CNS) or muscle tissues. Most of the motifs that we find are known from previous work to be strongly associated with expression in CNS or muscle. We give a second example using a published prostate cancer dataset where we can simply and clearly find which transcriptional pathways are associated with differences between benign and metastatic samples.Availability: The source code is freely available upon request from the authors.Contact:Ian.Jeffery@ucd.ie [ABSTRACT FROM AUTHOR]

In phylogenomics, incongruences between gene trees, resulting from both artifactual and biological reasons, can decrease the signal-to-noise ratio and complicate species tree inference. The amount of data handled today in classical phylogenomic analyses precludes manual error detection and removal. However, a simple and efficient way to automate the identification of outliers from a collection of gene trees is still missing. Here, we present PhylteR, a method that allows rapid and accurate detection of outlier sequences in phylogenomic datasets, i.e. species from individual gene trees that do not follow the general trend. PhylteR relies on DISTATIS, an extension of multidimensional scaling to 3 dimensions to compare multiple distance matrices at once. In PhylteR, these distance matrices extracted from individual gene phylogenies represent evolutionary distances between species according to each gene. On simulated datasets, we show that PhylteR identifies outliers with more sensitivity and precision than a comparable existing method. We also show that PhylteR is not sensitive to ILS-induced incongruences, which is a desirable feature. On a biological dataset of 14,463 genes for 53 species previously assembled for Carnivora phylogenomics, we show (i) that PhylteR identifies as outliers sequences that can be considered as such by other means, and (ii) that the removal of these sequences improves the concordance between the gene trees and the species tree. Thanks to the generation of numerous graphical outputs, PhylteR also allows for the rapid and easy visual characterization of the dataset at hand, thus aiding in the precise identification of errors. PhylteR is distributed as an R package on CRAN and as containerized versions (docker and singularity). [ABSTRACT FROM AUTHOR]

We develop a manifold inexact augmented Lagrangian framework to solve a family of nonsmooth optimization problem on Riemannian submanifold embedding in Euclidean space, whose objective function is the sum of a smooth function (but possibly nonconvex) and a nonsmooth convex function in Euclidean space. By utilizing the Moreau envelope, we get a smoothing Riemannian minimization subproblem at each iteration of the proposed method. Consequentially, each iteration subproblem is solved by a Riemannian Barzilai–Borwein gradient method. Theoretically, the convergence to critical point of the proposed method is established under some mild assumptions. Numerical experiments on compressed modes problems in physic and sparse principal component analysis demonstrate that the proposed method is a competitive method compared with some state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Placing new sequences onto reference phylogenies is increasingly used for analyzing environmental samples, especially microbiomes. Existing placement methods assume that query sequences have evolved under specific models directly on the reference phylogeny. For example, they assume single-gene data (e.g. 16S rRNA amplicons) have evolved under the GTR model on a gene tree. Placement, however, often has a more ambitious goal: extending a (genome-wide) species tree given data from individual genes without knowing the evolutionary model. Addressing this challenging problem requires new directions. Here, we introduce Deep-learning Enabled Phylogenetic Placement (DEPP), an algorithm that learns to extend species trees using single genes without prespecified models. In simulations and on real data, we show that DEPP can match the accuracy of model-based methods without any prior knowledge of the model. We also show that DEPP can update the multilocus microbial tree-of-life with single genes with high accuracy. We further demonstrate that DEPP can combine 16S and metagenomic data onto a single tree, enabling community structure analyses that take advantage of both sources of data. [Deep learning; gene tree discordance; metagenomics; microbiome analyses; neural networks; phylogenetic placement.] [ABSTRACT FROM AUTHOR]

Phylogenetic reconstruction using concatenated loci ("phylogenomics" or "supermatrix phylogeny") is a powerful tool for solving evolutionary splits that are poorly resolved in single gene/protein trees. However, recent phylogenomic attempts to resolve the eukaryote root have yielded conflicting results, along with claims of various artifacts hidden in the data. We have investigated these conflicts using two new methods for assessing phylogenetic conflict. ConJak uses whole marker (gene or protein) jackknifing to assess deviation from a central mean for each individual sequence, whereas ConWin uses a sliding window to screen for incongruent protein fragments (mosaics). Both methods allow selective masking of individual sequences or sequence fragments in order to minimize missing data, an important consideration for resolving deep splits with limited data. Analyses focused on a set of 76 eukaryotic proteins of bacterial ancestry previously used in various combinations to assess the branching order among the three major divisions of eukaryotes: Amorphea (mainly animals, fungi, and Amoebozoa), Diaphoretickes (most other well-known eukaryotes and nearly all algae) and Excavata, represented here by Discoba (Jakobida, Heterolobosea, and Euglenozoa). ConJak analyses found strong outliers to be concentrated in undersampled lineages, whereas ConWin analyses of Discoba, the most undersampled of the major lineages, detected potentially incongruent fragments scattered throughout. Phylogenetic analyses of the full data using an LG-gamma model support a Discoba sister scenario (neozoan-excavate root), which rises to 99–100% bootstrap support with data masked according to either protocol. However, analyses with two site-specific (CAT) mixture models yielded widely inconsistent results and a striking sensitivity to missing data. The neozoan-excavate root places Amorphea and Diaphoretickes as more closely related to each other than either is to Discoba, a fundamental relationship that should remain unaffected by additional taxa. [CAT-GTR; Discoba; eukaryote tree of life; HGT; jackknife; mixture models; mosaic genes; phylogenomics; sliding window; supermatrix.] [ABSTRACT FROM AUTHOR]

Integrative analysis of multiple data sets has the potential of fully leveraging the vast amount of high throughput biological data being generated. In particular such analysis will be powerful in making inference from publicly available collections of genetic, transcriptomic and epigenetic data sets which are designed to study shared biological processes, but which vary in their target measurements, biological variation, unwanted noise, and batch variation. Thus, methods that enable the joint analysis of multiple data sets are needed to gain insights into shared biological processes that would otherwise be hidden by unwanted intra-data set variation. Here, we propose a method called two-stage linked component analysis (2s-LCA) to jointly decompose multiple biologically related experimental data sets with biological and technological relationships that can be structured into the decomposition. The consistency of the proposed method is established and its empirical performance is evaluated via simulation studies. We apply 2s-LCA to jointly analyze four data sets focused on human brain development and identify meaningful patterns of gene expression in human neurogenesis that have shared structure across these data sets. [ABSTRACT FROM AUTHOR]

Classification methods that leverage the strengths of data from multiple sources (multiview data) simultaneously have enormous potential to yield more powerful findings than two‐step methods: association followed by classification. We propose two methods, sparse integrative discriminant analysis (SIDA), and SIDA with incorporation of network information (SIDANet), for joint association and classification studies. The methods consider the overall association between multiview data, and the separation within each view in choosing discriminant vectors that are associated and optimally separate subjects into different classes. SIDANet is among the first methods to incorporate prior structural information in joint association and classification studies. It uses the normalized Laplacian of a graph to smooth coefficients of predictor variables, thus encouraging selection of predictors that are connected. We demonstrate the effectiveness of our methods on a set of synthetic datasets and explore their use in identifying potential nontraditional risk factors that discriminate healthy patients at low versus high risk for developing atherosclerosis cardiovascular disease in 10 years. Our findings underscore the benefit of joint association and classification methods if the goal is to correlate multiview data and to perform classification. [ABSTRACT FROM AUTHOR]

Motivation The metabolome and microbiome disorders are highly associated with human health, and there are great demands for dual-omics interaction analysis. Here, we designed and developed an integrative platform, 3MCor, for metabolome and microbiome correlation analysis under the instruction of phenotype and with the consideration of confounders. Results Many traditional and novel correlation analysis methods were integrated for intra- and inter-correlation analysis. Three inter-correlation pipelines are provided for global, hierarchical and pairwise analysis. The incorporated network analysis function is conducive to rapid identification of network clusters and key nodes from a complicated correlation network. Complete numerical results (csv files) and rich figures (pdf files) will be generated in minutes. To our knowledge, 3MCor is the first platform developed specifically for the correlation analysis of metabolome and microbiome. Its functions were compared with corresponding modules of existing omics data analysis platforms. A real-world dataset was used to demonstrate its simple and flexible operation, comprehensive outputs and distinctive contribution to dual-omics studies. Availabilityand implementation 3MCor is available at http://3mcor.cn and the backend R script is available at https://github.com/chentianlu/3MCorServer. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Motivation: Cross-species meta-analyses of microarray data usually require prior affiliation of genes based on orthology information that often relies on sequence similarity. [ABSTRACT FROM PUBLISHER]

Phylogenomic subsampling is a procedure by which small sets of loci are selected from large genome-scale data sets and used for phylogenetic inference. This step is often motivated by either computational limitations associated with the use of complex inference methods or as a means of testing the robustness of phylogenetic results by discarding loci that are deemed potentially misleading. Although many alternative methods of phylogenomic subsampling have been proposed, little effort has gone into comparing their behavior across different data sets. Here, I calculate multiple gene properties for a range of phylogenomic data sets spanning animal, fungal, and plant clades, uncovering a remarkable predictability in their patterns of covariance. I also show how these patterns provide a means for ordering loci by both their rate of evolution and their relative phylogenetic usefulness. This method of retrieving phylogenetically useful loci is found to be among the top performing when compared with alternative subsampling protocols. Relatively common approaches such as minimizing potential sources of systematic bias or increasing the clock-likeness of the data are found to fare worse than selecting loci at random. Likewise, the general utility of rate-based subsampling is found to be limited: loci evolving at both low and high rates are among the least effective, and even those evolving at optimal rates can still widely differ in usefulness. This study shows that many common subsampling approaches introduce unintended effects in off-target gene properties and proposes an alternative multivariate method that simultaneously optimizes phylogenetic signal while controlling for known sources of bias. [ABSTRACT FROM AUTHOR]

Aims Atrial fibrillation (AF) is a progressive cardiac arrhythmia that increases the risk of hospitalization and adverse cardiovascular events. There is a clear demand for more inclusive and large-scale approaches to understand the molecular drivers responsible for AF, as well as the fundamental mechanisms governing the transition from paroxysmal to persistent and permanent forms. In this study, we aimed to create a molecular map of AF and find the distinct molecular programmes underlying cell type-specific atrial remodelling and AF progression. Methods and results We used a sheep model of long-standing, tachypacing-induced AF, sampled right and left atrial tissue, and isolated cardiomyocytes (CMs) from control, intermediate (transition), and late time points during AF progression, and performed transcriptomic and proteome profiling. We have merged all these layers of information into a meaningful three-component space in which we explored the genes and proteins detected and their common patterns of expression. Our data-driven analysis points at extracellular matrix remodelling, inflammation, ion channel, myofibril structure, mitochondrial complexes, chromatin remodelling, and genes related to neural function, as well as critical regulators of cell proliferation as hallmarks of AF progression. Most important, we prove that these changes occur at early transitional stages of the disease, but not at later stages, and that the left atrium undergoes significantly more profound changes than the right atrium in its expression programme. The pattern of dynamic changes in gene and protein expression replicate the electrical and structural remodelling demonstrated previously in the sheep and in humans, and uncover novel mechanisms potentially relevant for disease treatment. Conclusions Transcriptomic and proteomic analysis of AF progression in a large animal model shows that significant changes occur at early stages, and that among others involve previously undescribed increase in mitochondria, changes to the chromatin of atrial CMs, and genes related to neural function and cell proliferation. [ABSTRACT FROM AUTHOR]

Environmental filtering and niche differentiation are often invoked to explain species coexistence at local scales. The ironstone campo rupestre of Brazil provides a biodiverse natural experiment in which edaphic gradients represent filters to test the hypothesis that plant community functional composition, despite converging on extreme stress tolerance, exhibits a co-structure with environmental parameters. At the Serra do Rola-Moça State Park, soil physico-chemical parameters were characterized alongside community-weighted mean plant functional traits and Grime's competitor, stress-tolerator and ruderal strategies for species at each sampling site. In general, species exhibited a high degree of stress tolerance (between 72.6% and 100%), while ruderalism was 0% for all species. Soil nutrients related to plant metabolism (e.g. P, Ca, Mg) were associated with the stress-tolerant strategy and with traits involved in the leaf economics and size spectra. Despite a major edaphic filter selecting stress tolerance, fine-scale microhabitat variability represented by soil parameters related to fertility (i.e. P, Ca, Mg) and water retention capacity (i.e. clay content) was associated with subtle variation in ecological strategies and functional traits of species in the ironstone campo rupestre. [ABSTRACT FROM AUTHOR]

Gene expression data have played an essential role in many biomedical studies. When the number of genes is large and sample size is limited, there is a 'lack of information' problem, leading to low-quality findings. To tackle this problem, both horizontal and vertical data integrations have been developed, where vertical integration methods collectively analyze data on gene expressions as well as their regulators (such as mutations, DNA methylation and miRNAs). In this article, we conduct a selective review of vertical data integration methods for gene expression data. The reviewed methods cover both marginal and joint analysis and supervised and unsupervised analysis. The main goal is to provide a sketch of the vertical data integration paradigm without digging into too many technical details. We also briefly discuss potential pitfalls, directions for future developments and application notes. [ABSTRACT FROM AUTHOR]

Gene expressions are subtly regulated by quantifiable measures of genetic molecules such as interaction with other genes, methylation, mutations, transcription factor and histone modifications. Integrative analysis of multi-omics data can help scientists understand the condition or patient-specific gene regulation mechanisms. However, analysis of multi-omics data is challenging since it requires not only the analysis of multiple omics data sets but also mining complex relations among different genetic molecules by using state-of-the-art machine learning methods. In addition, analysis of multi-omics data needs quite large computing infrastructure. Moreover, interpretation of the analysis results requires collaboration among many scientists, often requiring reperforming analysis from different perspectives. Many of the aforementioned technical issues can be nicely handled when machine learning tools are deployed on the cloud. In this survey article, we first survey machine learning methods that can be used for gene regulation study, and we categorize them according to five different goals: gene regulatory subnetwork discovery, disease subtype analysis, survival analysis, clinical prediction and visualization. We also summarize the methods in terms of multi-omics input types. Then, we explain why the cloud is potentially a good solution for the analysis of multi-omics data, followed by a survey of two state-of-the-art cloud systems, Galaxy and BioVLAB. Finally, we discuss important issues when the cloud is used for the analysis of multi-omics data for the gene regulation study. [ABSTRACT FROM AUTHOR]

The plant microbiota may differ depending on soil type, but these microbiota probably share the same functions necessary for holobiont fitness. Thus, we tested the hypothesis that phytostimulatory microbial functional groups are likely to co-occur in the rhizosphere, using groups corresponding to nitrogen fixation (nifH) and 1-aminocyclopropane-1-carboxylate deamination (acdS), i.e. two key modes of action in plant-beneficial rhizobacteria. The analysis of three maize fields in two consecutive years showed that quantitative PCR numbers of nifH and of acdS alleles differed according to field site, but a positive correlation was found overall when comparing nifH and acdS numbers. Metabarcoding analyses in the second year indicated that the diversity level of acdS but not nifH rhizobacteria in the rhizosphere differed across fields. Furthermore, between-class analysis showed that the three sites differed from one another based on nifH or acdS sequence data (or rrs data), and the bacterial genera contributing most to field differentiation were not the same for the three bacterial groups. However, co-inertia analysis indicated that the genetic structures of both functional groups and of the whole bacterial community were similar across the three fields. Therefore, results point to co-selection of rhizobacteria harboring nitrogen fixation and/or 1-aminocyclopropane-1-carboxylate deamination abilities. [ABSTRACT FROM AUTHOR]

This 6-month study characterized the contribution of deep chlorophyll maximum (DCM) to lake phytoplankton diversity and primary production, in relation to stratification during the ice-free season. Phytoplankton and zooplankton dynamics were examined with environmental drivers in a small stratified lake that presents vertical gradients of light and nutrients. The phytoplankton, first composed of diatoms and chrysophyceae, shifted to cyanobacteria in mid-July. With stratification increase, surface nutrient limitation appeared to favor motile species characteristic of oligotrophic environments above a deep layer of filamentous cyanobacteria, fueled by the vertical nutrient fluxes from sediment. The DCM contributed on average to 33% (but up to 60%) of total production during the strongest summer stratification period. In late summer, as stratification was eroding, the vertical gradient of nutrients was reduced, but light attenuation with depth increased. Distinct assemblages were identified between surface and deep layer with shade-adapted species. The contribution of DCM was reduced to 10%. Zooplankton community varied in conjunction with phytoplankton and stratification. Our study demonstrates no benefit of DCM for taxonomic and functional diversity and a limited contribution to total production. The depths over which phytoplankton use separate spatial niches may be lesser in a 6-m-deep lake compared with deeper stratified lakes. [ABSTRACT FROM AUTHOR]

Knowledge on the relationship between different biological modalities (RNA, chromatin, etc.) can help further our understanding of the processes through which biological components interact. The ready availability of multi-omics datasets has led to the development of numerous methods for identifying sources of common variation across biological modalities. However, evaluation of the performance of these methods, in terms of consistency, has been difficult because most methods are unsupervised. We present a comparison of sparse multiple canonical correlation analysis (Sparse mCCA), angle-based joint and individual variation explained (AJIVE) and multi-omics factor analysis (MOFA) using a cross-validation approach to assess overfitting and consistency. Both large and small-sample datasets were used to evaluate performance, and a permuted null dataset was used to identify overfitting through the application of our framework and approach. In the large-sample setting, we found that all methods demonstrated consistency and lack of overfitting; however, in the small-sample size setting, AJIVE provided the most stable results. We provide an R package so that our framework and approach can be applied to evaluate other methods and datasets. [ABSTRACT FROM AUTHOR]

Objective To investigate compositional differences in the gut microbiota associated with bone homeostasis and fractures in a cohort of older adults. Methods Faecal microbiota profiles were determined from 181 individuals with osteopenia (n = 61) or osteoporosis (n = 60), and an age- and gender-matched group with normal BMD (n = 60). Analysis of the 16S (V3-V4 region) amplicon dataset classified to the genus level was used to identify significantly differentially abundant taxa. Adjustments were made for potential confounding variables identified from the literature using several statistical models. Results We identified six genera that were significantly altered in abundance in the osteoporosis or osteopenic groups compared with age- and gender-matched controls. A detailed study of microbiota associations with meta-data variables that included BMI, health status, diet and medication revealed that these meta-data explained 15–17% of the variance within the microbiota dataset. BMD measurements were significantly associated with alterations in the microbiota. After controlling for known biological confounders, five of the six taxa remained significant. Overall microbiota alpha diversity did not correlate to BMD in this study. Conclusion Reduced BMD in osteopenia and osteoporosis is associated with an altered microbiota. These alterations may be useful as biomarkers or therapeutic targets in individuals at high risk of reductions in BMD. These observations will lead to a better understanding of the relationship between the microbiota and bone homeostasis. [ABSTRACT FROM AUTHOR]

A most interesting exception within the parasitic Apicomplexa is Nephromyces , an extracellular, probably mutualistic, endosymbiont found living inside molgulid ascidian tunicates (i.e. sea squirts). Even though Nephromyces is now known to be an apicomplexan, many other questions about its nature remain unanswered. To gain further insights into the biology and evolutionary history of this unusual apicomplexan, we aimed to 1) find the precise phylogenetic position of Nephromyces within the Apicomplexa, 2) search for the apicoplast genome of Nephromyces , and 3) infer the major metabolic pathways in the apicoplast of Nephromyces. To do this, we sequenced a metagenome and a metatranscriptome from the molgulid renal sac, the specialized habitat where Nephromyces thrives. Our phylogenetic analyses of conserved nucleus-encoded genes robustly suggest that Nephromyces is a novel lineage sister to the Hematozoa, which comprises both the Haemosporidia (e.g. Plasmodium) and the Piroplasmida (e.g. Babesia and Theileria). Furthermore, a survey of the renal sac metagenome revealed 13 small contigs that closely resemble the genomes of the nonphotosynthetic reduced plastids, or apicoplasts, of other apicomplexans. We show that these apicoplast genomes correspond to a diverse set of most closely related but genetically divergent Nephromyces lineages that co-inhabit a single tunicate host. In addition, the apicoplast of Nephromyces appears to have retained all biosynthetic pathways inferred to have been ancestral to parasitic apicomplexans. Our results shed light on the evolutionary history of the only probably mutualistic apicomplexan known, Nephromyces , and provide context for a better understanding of its life style and intricate symbiosis. [ABSTRACT FROM AUTHOR]

Motivation Visualization of high-dimensional data is an important step in exploratory data analysis and knowledge discovery. However, it is challenging, because the interpretation is highly subjective. If we see dimensionality reduction (DR) techniques as the main tool for data visualization, they are like multiple cameras that look into the data from different perspectives or angles. We can hardly prescribe one single perspective for all datasets and problems. One snapshot of data cannot reveal all the relevant aspects of the data in higher dimensions. The reason is that each of these methods has its own specific strategy, normally based on well-established mathematical theories to obtain a low-dimensional projection of the data, which sometimes is totally different from the others. Therefore, relying only on one single projection can be risky, because it can close our eyes to important parts of the full knowledge space. Results We propose the first framework for multi-insight data visualization of multi-omics data. This approach, contrary to single-insight approaches, is able to uncover the majority of data features through multiple insights. The main idea behind the methodology is to combine several DR methods via tensor factorization and group the solutions into an optimal number of clusters (or insights). The experimental evaluation with low-dimensional synthetic data, simulated multi-omics data related to ovarian cancer, as well as real multi-omics data related to breast cancer show the competitive advantage over state-of-the-art methods. Availability and implementation https://folk.uio.no/hadift/MIV/ [user/pass via hadift@medisin. uio.no] Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Recent advances in proteomics allow the accurate measurement of abundances for thousands of proteins and phosphoproteins from multiple samples in parallel. Therefore, for the first time, we have the opportunity to measure the proteomic profiles of thousands of patient samples or disease model cell lines in a systematic way, to identify the precise underlying molecular mechanism and discover personalized biomarkers, networks and treatments. Here, we review examples of successful use of proteomics and phosphoproteomics data sets in as well as their integration other omics data sets with the aim of precision medicine. We will discuss the bioinformatics challenges posed by the generation, analysis and integration of such large data sets and present potential reasons why proteomics profiling and biomarkers are not currently widely used in the clinical setting. We will finally discuss ways to contribute to the better use of proteomics data in precision medicine and the clinical setting. [ABSTRACT FROM AUTHOR]

Elevational gradients have been highly useful for understanding the underlying forces driving variation in plant traits and plant–insect herbivore interactions. A widely held view from these studies has been that greater herbivory under warmer and less variable climatic conditions found at low elevations has resulted in stronger herbivore selection on plant defences. However, this prediction has been called into question by conflicting empirical evidence, which could be explained by a number of causes such as an incomplete assessment of defensive strategies (ignoring other axes of defence such as defence inducibility) or unaccounted variation in abiotic factors along elevational clines. We conducted a greenhouse experiment testing for inter-specific variation in constitutive leaf chemical defences (phenolic compounds) and their inducibility in response to feeding by gypsy moth larvae (Lymantria dispar L. Lepidoptera) using saplings of 18 oak (Quercus, Fagaceae) species. These species vary in their elevational distribution and together span >2400 m in elevation, therefore allowing us to test for among-species elevational clines in defences based on the elevational range of each species. In addition, we further tested for elevational gradients in the correlated expression of constitutive defences and their inducibility and for associations between defences and climatic factors potentially underlying elevational gradients in defences. Our results showed that oak species with high elevational ranges exhibited a greater inducibility of phenolic compounds (hydrolysable tannins), but this gradient was not accounted for by climatic predictors. In contrast, constitutive defences and the correlated expression of constitutive phenolics and their inducibility did not exhibit elevational clines. Overall, this study builds towards a more robust and integrative understanding of how multivariate plant defensive phenotypes vary along ecological gradients and their underlying abiotic drivers. [ABSTRACT FROM AUTHOR]

We introduce a novel class of factor analysis methodologies for the joint analysis of multiple studies. The goal is to separately identify and estimate (1) common factors shared across multiple studies, and (2) study‐specific factors. We develop an Expectation Conditional‐Maximization algorithm for parameter estimates and we provide a procedure for choosing the numbers of common and specific factors. We present simulations for evaluating the performance of the method and we illustrate it by applying it to gene expression data in ovarian cancer. In both, we clarify the benefits of a joint analysis compared to the standard factor analysis. We have provided a tool to accelerate the pace at which we can combine unsupervised analysis across multiple studies, and understand the cross‐study reproducibility of signal in multivariate data. An R package (MSFA), is implemented and is available on GitHub. [ABSTRACT FROM AUTHOR]

Integrative analysis aims to identify the driving factors of a biological process by the joint exploration of data from multiple cellular levels. The volume of omics data produced is constantly increasing, and so too does the collection of tools for its analysis. Comparative studies assessing performance and the biological value of results, however, are rare but in great demand. We present a comprehensive comparison of three integrative analysis approaches, sparse canonical correlation analysis (sCCA), non-negative matrix factorization (NMF) and logic data mining MicroArray Logic Analyzer (MALA), by applying them to simulated and experimental omics data. We find that sCCA and NMF are able to identify differential features in simulated data, while the Logic Data Mining method, MALA, falls short. Applied to experimental data, we show that MALA performs best in terms of sample classification accuracy, and in general, the classification power of prioritized feature sets is high (97.1–99.5% accuracy). The proportion of features identified by at least one of the other methods, however, is approximately 60% for sCCA and NMF and nearly 30% for MALA, and the proportion of features jointly identified by all methods is only around 16%. Similarly, the congruence on functional levels (Gene Ontology, Reactome) is low. Furthermore, the agreement of identified feature sets with curated gene signatures relevant to the investigated disease is modest. We discuss possible reasons for the moderate overlap of identified feature sets with each other and with curated cancer signatures. The R code to create simulated data, results and figures is provided at https://github.com/ThallingerLab/IamComparison. [ABSTRACT FROM AUTHOR]

Fisheries and marine ecosystem-based management requires a holistic understanding of the dynamics of fish communities and their responses to changes in environmental conditions. Environmental conditions can simultaneously shape the spatial distribution and the temporal dynamics of a population, which together can trigger changes in the functional structure of communities. Here, we developed a comprehensive framework based on complementary multivariate statistical methodologies to simultaneously investigate the effects of environmental conditions on the spatial, temporal and functional dynamics of species assemblages. The framework is tested using survey data collected during more than 4000 fisheries hauls over the Baltic Sea between 2001 and 2016. The approach revealed the Baltic fish community to be structured into three sub-assemblages along a strong and temporally stable salinity gradient decreasing from West to the East. Additionally, we highlight a mismatch between species and functional richness associated with a lower functional redundancy in the Baltic Proper compared with other sub-areas, suggesting an ecosystem more susceptible to external pressures. Based on a large dataset of community data analysed in an innovative and comprehensive way, we could disentangle the effects of environmental changes on the structure of biotic communities—key information for the management and conservation of ecosystems. [ABSTRACT FROM AUTHOR]

The growing number of modalities (e.g. multi-omics, imaging and clinical data) characterizing a given disease provides physicians and statisticians with complementary facets reflecting the disease process but emphasizes the need for novel statistical methods of data analysis able to unify these views. Such data sets are indeed intrinsically structured in blocks, where each block represents a set of variables observed on a group of individuals. Therefore, classical statistical tools cannot be applied without altering their organization, with the risk of information loss. Regularized generalized canonical correlation analysis (RGCCA) and its sparse generalized canonical correlation analysis (SGCCA) counterpart are component-based methods for exploratory analyses of data sets structured in blocks of variables. Rather than operating sequentially on parts of the measurements, the RGCCA/SGCCA-based integrative analysis method aims at summarizing the relevant information between and within the blocks. It processes a priori information defining which blocks are supposed to be linked to one another, thus reflecting hypotheses about the biology underlying the data blocks. It also requires the setting of extra parameters that need to be carefully adjusted. Here, we provide practical guidelines for the use of RGCCA/SGCCA. We also illustrate the flexibility and usefulness of RGCCA/SGCCA on a unique cohort of patients with four genetic subtypes of spinocerebellar ataxia, in which we obtained multiple data sets from brain volumetry and magnetic resonance spectroscopy, and metabolomic and lipidomic analyses. As a first step toward the extraction of multimodal biomarkers, and through the reduction to a few meaningful components and the visualization of relevant variables, we identified possible markers of disease progression. [ABSTRACT FROM AUTHOR]

In plants capable of clonal reproduction, demographic and ecological investigations are essential for understanding the factors contributing to population diversity. The perennial, clonal orchid Cypripedium calceolus has a variable status in Europe and Asia, being threatened with extinction in some countries and abundant in others. In Estonia, thriving populations can be observed in different biogeographical regions. The aim of this study was to characterize genetic diversity and differentiation in Estonian populations, by comparing the current variation with long-term observations of the clonal system and taking into account climatic variables. Diversity at 11 microsatellite loci for 11 populations representing the boreal and nemoral regions was investigated. Some signatures of bottleneck and recent founder events were detected, with a high genetic diversity in terms of heterozygosity and allele diversity and a pattern of differentiation not strictly correlated with geographical factors. Globally, information regarding population dynamics in C. calceolus provides the possibility to inform conservation management, prevent loss of ancestral variation and promote intra-population divergence through the combination of gene flow and ecological change related to the clonal system. [ABSTRACT FROM AUTHOR]

Motivation: Recent high-throughput sequencing advances have expanded the breadth of available omics datasets and the integrated analysis of multiple datasets obtained on the same samples has allowed to gain important insights in a wide range of applications. However, the integration of various sources of information remains a challenge for systems biology since produced datasets are often of heterogeneous types, with the need of developing generic methods to take their different specificities into account. Results: We propose a multiple kernel framework that allows to integrate multiple datasets of various types into a single exploratory analysis. Several solutions are provided to learn either a consensus meta-kernel or a meta-kernel that preserves the original topology of the datasets. We applied our framework to analyse two public multi-omics datasets. First, the multiple metagenomic datasets, collected during the TARA Oceans expedition, was explored to demonstrate that our method is able to retrieve previous findings in a single kernel PCA as well as to provide a new image of the sample structures when a larger number of datasets are included in the analysis. To perform this analysis, a generic procedure is also proposed to improve the interpretability of the kernel PCA in regards with the original data. Second, the multi-omics breast cancer datasets, provided by The Cancer Genome Atlas, is analysed using a kernel Self-Organizing Maps with both single and multi-omics strategies. The comparison of these two approaches demonstrates the benefit of our integration method to improve the representation of the studied biological system. [ABSTRACT FROM AUTHOR]

The ongoing development of imaging systems continuously brings novel possibilities for the exploration of plant anatomy at different scales. However, increasing resolution often results in a smaller field of view, limiting the scope for wider conclusions. Staedler et al. (2018) got round this problem by making use of 3D images acquired at two different scales to estimate the number of pollen grains within flowers. It is a powerful approach, providing much more information than with a single scale. [ABSTRACT FROM AUTHOR]

Molecular dating studies typically need fossils to calibrate the analyses. Unfortunately, the fossil record is extremely poor or presently nonexistent for many species groups, rendering such dating analysis difficult. One such group is the Asian horned frogs (Megophryinae). Sampling all generic nomina, we combined a novel ~5 kb dataset composed of four nuclear and three mitochondrial gene fragments to produce a robust phylogeny, with an extensive external morphological study to produce a working taxonomy for the group. Expanding the molecular dataset to include out-groups of fossil-represented ancestral anuran families, we compared the priorless RelTime dating method with the widely used prior-based Bayesian timetree method, MCMCtree, utilizing a novel combination of fossil priors for anuran phylogenetic dating. The phylogeny was then subjected to ancestral phylogeographic analyses, and dating estimates were compared with likely biogeographic vicariant events. Phylogenetic analyses demonstrated that previously proposed systematic hypotheses were incorrect due to the paraphyly of genera. Molecular phylogenetic, morphological, and timetree results support the recognition of Megophryinae as a single genus, Megophrys, with a subgenus level classification. Timetree results using RelTime better corresponded with the known fossil record for the out-group anuran tree. For the priorless in-group, it also outperformed MCMCtree when node date estimates were compared with likely influential historical biogeographic events, providing novel insights into the evolutionary history of this pan-Asian anuran group. Given a relatively small molecular dataset, and limited prior knowledge, this study demonstrates that the computationally rapid RelTime dating tool may outperform more popular and complex prior reliant timetree methodologies. [ABSTRACT FROM AUTHOR]

In Alberta, wild bison, Bison bison (Linnaeus 1758) occurring outside protected areas are assumed to have originated from the free-ranging bison populations within Wood Buffalo National Park (WBNP). As bison in WBNP are infected with 2 diseases, it was assumed that neighboring bison outside WBNP were also diseased. However, the neighboring Harper Creek and Ronald Lake bison populations have not tested positive for either disease, suggesting limited contact with WBNP herds. To resolve this, we analyzed microsatellite genotypes from all free-ranging bison groups in Alberta and one from the Northwest Territories (NWT), Canada. In the WBNP area where bison have persisted, we recover a genetically panmictic population comprising all possible subpopulations within the park, plus the adjacent Wentzel Lake herd, all of which are diseased. In contrast, but consistent with their disease-free status, we find the Ronald Lake and Harper Creek bison herds to be genetically differentiated from this larger WBNP herd, despite their close proximity. This highlights the importance of the Ronald Lake and potentially the Harper Creek herds for bison recovery. We also document the genetic impacts of translocation on the species, as the Mackenzie, NWT, and Elk Island National Park (EINP), Alberta, bison populations founded in the 1960s from WBNP animals are now genetically differentiated, but the more recently founded Hay Zama bison population is not differentiated from its source population, the EINP wood bison. Interestingly, factorial correspondence analysis, FST, and its high private allelic richness suggest that the Harper Creek population is significantly differentiated from all other bison populations studied. Though they are most closely related to WBNP populations, more study of this population is warranted. [ABSTRACT FROM AUTHOR]

Stramenopiles or heterokonts constitute one of the most speciose and diverse clades of protists. It includes ecologically important algae (such as diatoms or large multicellular brown seaweeds), as well as heterotrophic (e.g., bicosoecids, MAST groups) and parasitic (e.g., Blastocystis, oomycetes) species. Despite their evolutionary and ecological relevance, deep phylogenetic relationships among stramenopile groups, inferred mostly from small-subunit rDNA phylogenies, remain unresolved, especially for the heterotrophic taxa. Taking advantage of recently released stramenopile transcriptome and genome sequences, as well as data from the genomic assembly of the MAST-3 species Incisomonas marina generated in our laboratory, we have carried out the first extensive phylogenomic analysis of stramenopiles, including representatives of most major lineages. Our analyses, based on a large data set of 339 widely distributed proteins, strongly support a root of stramenopiles lying between two clades, Bigyra and Gyrista (Pseudofungi plus Ochrophyta). Additionally, our analyses challenge the Phaeista-Khakista dichotomy of photosynthetic stramenopiles (ochrophytes) as two groups previously considered to be part of the Phaeista (Pelagophyceae and Dictyochophyceae), branch with strong support with the Khakista (Bolidophyceae and Diatomeae). We propose a new classification of ochrophytes within the two groups Chrysista and Diatomista to reflect the new phylogenomic results. Our stramenopile phylogeny provides a robust phylogenetic framework to investigate the evolution and diversification of this group of ecologically relevant protists. [ABSTRACT FROM AUTHOR]

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