Biology subjects

Tsamardinos, I.

Publications and source records attributed to Tsamardinos, I..

7 recordsLinked to original sources

bioRxiv2020

STATegra: Multi-omics data integration - A conceptual scheme with a bioinformatics pipeline

Technologies for profiling samples using different omics platforms have been at the forefront since the human genome project. Large-scale multi-omics data hold the promise of deciphering different regulatory layers. Yet, while there is a myriad of bioinformatics tools, each multi-omics analysis appears to start from scratch with an arbitrary decision over which tools to use and how to combine them. It is therefore an unmet need to conceptualize how to integrate such data and to implement and validate pipelines in different cases. We have designed a conceptual framework (STATegra), aiming it to be as generic as possible for multi-omics analysis, combining machine learning component analysis, non-parametric data combination and a multi-omics exploratory analysis in a step-wise manner. While in several studies we have previously combined those integrative tools, here we provide a systematic description of the STATegra framework and its validation using two TCGA case studies. For both, the Glioblastoma and the Skin Cutaneous Melanoma cases, we demonstrate an enhanced capacity to identify features in comparison to single-omics analysis. Such an integrative multi-omics analysis framework for the identification of features and components facilitates the discovery of new biology. Finally, we provide several options for applying the STATegra framework when parametric assumptions are fulfilled, and for the case when not all the samples are profiled for all omics. The STATegra framework is built using several tools, which are being integrated step-by-step as OpenSource in the STATegRa Bioconductor package https://bioconductor.org/packages/release/bioc/html/STATegra.html.

bioinformatics↗

bioRxiv2020

Latent Feature Representations for Human Gene Expression Data Improve Phenotypic Predictions

High-throughput technologies such as microarrays and RNA-sequencing (RNA-seq) allow to precisely quantify transcriptomic profiles, generating datasets that are inevitably high-dimensional. In this work, we investigate whether the whole human transcriptome can be represented in a compressed, low dimensional latent space without loosing relevant information. We thus constructed low-dimensional latent feature spaces of the human genome, by utilizing three dimensionality reduction approaches and a diverse set of curated datasets. We applied standard Principal Component Analysis (PCA), kernel PCA and Autoencoder Neural Networks on 1360 datasets from four different measurement technologies. The latent feature spaces are tested for their ability to (a) reconstruct the original data and (b) improve predictive performance on validation datasets not used during the creation of the feature space. While linear techniques show better reconstruction performance, nonlinear approaches, particularly, neural-based models seem to be able to capture non-additive interaction effects, and thus enjoy stronger predictive capabilities. Our results show that low dimensional representations of the human transcriptome can be achieved by integrating hundreds of datasets, despite the limited sample size of each dataset and the biological / technological heterogeneity across studies. The created space is two to three orders of magnitude smaller compared to the raw data, offering the ability of capturing a large portion of the original data variability and eventually reducing computational time for downstream analyses.

bioinformatics↗

bioRxiv2020

Just Add Data: Automated Predictive Modeling and BioSignature Discovery

Fully automated machine learning, statistical modelling, and artificial intelligence for predictive modeling is becoming a reality, giving rise to the field of Automated Machine Learning (AutoML). AutoML systems promise to democratize data analysis to non-experts, drastically increase productivity, improve replicability of the statistical analysis, facilitate the interpretation of results, and shield against common methodological analysis pitfalls. We present the basic ideas and principles of Just Add Data Bio (JADBIO), an AutoML technology applicable to the low-sample, high-dimensional omics data that arise in translational medicine and bioinformatics applications. In addition to predictive and diagnostic models ready for clinical use, JADBIO also returns the corresponding biosignatures, i.e., minimal-size subsets of biomarkers that are jointly predictive of the outcome of interest. A use-case on thymic epithelial tumors is presented, along with an extensive evaluation on 374 public biological datasets. Results show that long-standing challenges with overfitting and overestimation of complex non-linear machine learning pipelines on high-dimensional, low small sample data can be overcome.

bioinformatics↗

bioRxiv2020

ERCC1-XPF Interacts with Topoisomerase IIβ to Facilitate the Repair of Activity-induced DNA Breaks

Type II DNA Topoisomerases (TOP II) generate transient double-strand DNA breaks (DSBs) to resolve topological constraints during transcription. Using genome-wide mapping of DSBs and functional genomics approaches, we show that, in the absence of exogenous genotoxic stress, transcription leads to DSB accumulation and to the recruitment of the structure-specific ERCC1-XPF endonuclease on active gene promoters. Instead, we find that the complex is released from regulatory or gene body elements in UV-irradiated cells. Abrogation of ERCC1 or re-ligation blockage of TOP II-mediated DSBs aggravates the accumulation of transcription-associated {gamma}H2Ax and 53BP1 foci, which dissolve when TOP II-mediated DNA cleavage is inhibited. An in vivo biotinylation tagging strategy coupled to a high-throughput proteomics approach reveals that ERCC1-XPF interacts with TOP II{beta} and the CTCF/cohesin complex, which co-localize with the heterodimer on DSBs. Together; our findings provide a rational explanation for the remarkable clinical heterogeneity seen in human disorders with ERCC1-XPF defects.

genetics↗

bioRxiv2019

STATegra: a comprehensive multi-omics dataset of B-cell differentiation in mouse

Multi-omics approaches use a diversity of high-throughput technologies to profile the different molecular layers of living cells. Ideally, the integration of this information should result in comprehensive systems models of cellular physiology and regulation. However, most multi-omics projects still include a limited number of molecular assays and there have been very few multi-omic studies that evaluate dynamic processes such as cellular growth, development and adaptation. Hence, we lack formal analysis methods and comprehensive multi-omics datasets that can be leveraged to develop true multi-layered models for dynamic cellular systems. Here we present the STATegra multi-omics dataset that combines measurements from up to 10 different omics technologies applied to the same biological system, namely the well-studied mouse pre-B-cell differentiation. STATegra includes high-throughput measurements of chromatin structure, gene expression, proteomics and metabolomics, and it is complemented with single-cell data. To our knowledge, the STATegra collection is the most diverse multi-omics dataset describing a dynamic biological system.

bioinformatics↗

bioRxiv2018

Efficient feature selection on gene expression data: Which algorithm to use?

BackgroundFeature selection seeks to identify a minimal-size subset of features that is maximally predictive of the outcome of interest. It is particularly important for biomarker discovery from high-dimensional molecular data, where the features could correspond to gene expressions, Single Nucleotide Polymorphisms (SNPs), proteins concentrations, e.t.c. We evaluate, empirically, three state-of-the-art, feature selection algorithms, scalable to high-dimensional data: a novel generalized variant of OMP (gOMP), LASSO and FBED. All three greedily select the next feature to include; the first two employ the residuals re-sulting from the current selection, while the latter rebuilds a statistical model. The algorithms are compared in terms of predictive performance, number of selected features and computational efficiency, on gene expression data with either survival time (censored time-to-event) or disease status (case-control) as an outcome. This work attempts to answer a) whether gOMP is to be preferred over LASSO and b) whether residual-based algorithms, e.g. gOMP, are to be preferred over algorithms, such as FBED, that rely heavily on regression model fitting.\n\nResultsgOMP is on par, or outperforms LASSO in all metrics, predictive performance, number of features selected and computational efficiency. Contrasting gOMP to FBED, both exhibit similar performance in terms of predictive performance and number of selected features. Overall, gOMP combines the benefits of both LASSO and FBED; it is computationally efficient and produces parsimonious models of high predictive performance.\n\nConclusionsThe use of gOMP is suggested for variable selection with high-dimensional gene expression data, and the target variable need not be restricted to time-to-event or case control, as examined in this paper.

bioinformatics↗

bioRxiv2017

Predicting Causal Relationships from Biological Data: Applying Automated Casual Discovery on Mass Cytometry Data of Human Immune Cells

Learning the causal relationships that define a molecular system allows us to predict how the system will respond to different interventions. Distinguishing causality from mere association typically requires randomized experiments. Methods for automated causal discovery from limited experiments exist, but have so far rarely been tested in systems biology applications. In this work, we apply state-of-the art causal discovery methods on a large collection of public mass cytometry data sets, measuring intra-cellular signaling proteins of the human immune system and their response to several perturbations. We show how different experimental conditions can be used to facilitate causal discovery, and apply two fundamental methods that produce context-specific causal predictions. Causal predictions were reproducible across independent data sets from two different studies, but often disagree with the KEGG pathway databases. Within this context, we discuss the caveats we need to overcome for automated causal discovery to become a part of the routine data analysis in systems biology.

bioinformatics↗