Development of statistical tools for integrating time course ‘omics’ data

ABSTRACTDynamic changes in biological systems can be captured by measuring molecular expression from different levels (e.g., genes and proteins) across time. Integration of such data aims to identify molecules that show similar expression changes over time; such molecules may be co-regulated and thus involved in similar biological processes. Combining data sources presents a systematic approach to study molecular behaviour. It can compensate for missing data in one source, and can reduce false positives when multiple sources highlight the same pathways. However, integrative approaches must accommodate the challenges inherent in ‘omics’ data, including high-dimensionality, noise, and timing differences in expression. As current methods for identification of co-expression cannot cope with this level of complexity, we developed a novel algorithm called DynOmics. DynOmics is based on the fast Fourier transform, from which the difference in expression initiation between trajectories can be estimated. This delay can then be used to realign the trajectories and identify those which show a high degree of correlation. Through extensive simulations, we demonstrate that DynOmics is efficient and accurate compared to existing approaches. We consider two case studies highlighting its application, identifying regulatory relationships across ‘omics’ data within an organism and for comparative gene expression analysis across organisms.

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Ridge estimation of the VAR(1) model and its time series chain graph from multivariate time-course omics data

Biometrical Journal ◽

10.1002/bimj.201500269 ◽

2016 ◽

Vol 59 (1) ◽

pp. 172-191 ◽

Cited By ~ 4

Author(s):

Viktorian Miok ◽

Saskia M. Wilting ◽

Wessel N. van Wieringen

Keyword(s):

Time Series ◽

Time Course ◽

Omics Data ◽

Chain Graph ◽

Ridge Estimation

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Enter the matrix: factorization uncovers knowledge from omics Names/Affiliations

10.1101/196915 ◽

2017 ◽

Cited By ~ 2

Author(s):

Genevieve L. Stein-O’Brien ◽

Raman Arora ◽

Aedin C. Culhane ◽

Alexander V. Favorov ◽

Lana X. Garmire ◽

...

Keyword(s):

High Throughput ◽

Matrix Factorization ◽

Time Course ◽

High Dimensional Data ◽

Dimensional Structure ◽

High Dimensional ◽

Biological Knowledge ◽

Omics Data ◽

Cellular Interactions ◽

Low Dimensional

AbstractOmics data contains signal from the molecular, physical, and kinetic inter- and intra-cellular interactions that control biological systems. Matrix factorization techniques can reveal low-dimensional structure from high-dimensional data that reflect these interactions. These techniques can uncover new biological knowledge from diverse high-throughput omics data in topics ranging from pathway discovery to time course analysis. We review exemplary applications of matrix factorization for systems-level analyses. We discuss appropriate application of these methods, their limitations, and focus on analysis of results to facilitate optimal biological interpretation. The inference of biologically relevant features with matrix factorization enables discovery from high-throughput data beyond the limits of current biological knowledge—answering questions from high-dimensional data that we have not yet thought to ask.

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Integrated time course omics analysis distinguishes immediate therapeutic response from acquired resistance

10.1101/136564 ◽

2017 ◽

Cited By ~ 2

Author(s):

Genevieve Stein-O’Brien ◽

Luciane T Kagohara ◽

Sijia Li ◽

Manjusha Thakar ◽

Ruchira Ranaweera ◽

...

Keyword(s):

Targeted Therapies ◽

Time Course ◽

Therapeutic Response ◽

Acquired Resistance ◽

New Drugs ◽

Integrated Analysis ◽

Omics Data ◽

Epigenetic Alterations ◽

Molecular Changes ◽

Omics Analysis

AbstractBACKGROUNDTargeted therapies specifically act by blocking the activity of proteins that are encoded by genes critical for tumorigenesis. However, most cancers acquire resistance and long-term disease remission is rarely observed. Understanding the time course of molecular changes responsible for the development of acquired resistance could enable optimization of patients’ treatment options. Clinically, acquired therapeutic resistance can only be studied at a single time point in resistant tumors. To determine the dynamics of these molecular changes, we obtained high throughput omics data weekly during the development of cetuximab resistance in a head and neck cancer in vitro model.RESULTSAn unsupervised algorithm, CoGAPS, was used to quantify the evolving transcriptional and epigenetic changes. Applying a PatternMarker statistic to the results from CoGAPS enabled novel heatmap-based visualization of the dynamics in these time course omics data. We demonstrate that transcriptional changes result from immediate therapeutic response or resistance, whereas epigenetic alterations only occur with resistance. Integrated analysis demonstrates delayed onset of changes in DNA methylation relative to transcription, suggesting that resistance is stabilized epigenetically.CONCLUSIONSGenes with epigenetic alterations associated with resistance that have concordant expression changes are hypothesized to stabilize resistance. These genes include FGFR1, which was associated with EGFR inhibitor resistance previously. Thus, integrated omics analysis distinguishes the timing of molecular drivers of resistance. Our findings provide a relevant towards better understanding of the time course progression of changes resulting in acquired resistance to targeted therapies. This is an important contribution to the development of alternative treatment strategies that would introduce new drugs before the resistant phenotype develops.

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The STATegra project: new statistical tools for analysis and integration of diverse omics data

EMBnet journal ◽

10.14806/ej.20.a.768 ◽

2014 ◽

Vol 20 (A) ◽

pp. 768

Author(s):

Ana Conesa

Keyword(s):

Omics Data ◽

Statistical Tools

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Visualizing metabolic network dynamics through time-series metabolomics data

10.1101/426106 ◽

2018 ◽

Author(s):

Lea F. Buchweitz ◽

James T. Yurkovich ◽

Christoph M. Blessing ◽

Veronika Kohler ◽

Fabian Schwarzkopf ◽

...

Keyword(s):

Time Series ◽

Metabolic Network ◽

Time Course ◽

Human Platelet ◽

New Method ◽

Omics Data ◽

Visualization Technique ◽

Data Types ◽

Metabolomics Data

ABSTRACTNew technologies have given rise to an abundance of -omics data, particularly metabolomics data. The scale of these data introduces new challenges for the interpretation and extraction of knowledge, requiring the development of new computational visualization methodologies. Here, we present a new method for the visualization of time-course metabolomics data within the context of metabolic network maps. We demonstrate the utility of this method by examining previously published data for two cellular systems—the human platelet and erythrocyte under cold storage for use in transfusion medicine.The results comprise two animated videos that allow for new insights into the metabolic state of both cell types. In the case study of the platelet metabolome during storage, the new visualization technique elucidates a nicotinamide accumulation which mirrors that of hypoxanthine and might, therefore, reflect similar pathway usage. This visual analysis provides a possible explanation for why the salvage reactions in purine metabolism exhibit lower activity during the first few days of the storage period. The second case study displays drastic changes in specific erythrocyte metabolite pools at different times during storage at different temperatures.In conclusion, this new visualization technique introduced in this article constitutes a well-suitable approach for large-scale network exploration and advances hypothesis generation. This method can be applied to any system with data and a metabolic map to promote visualization and understand physiology at the network level. More broadly, we hope that our approach will provide the blueprints for new visualizations of other longitudinal -omics data types.AUTHOR SUMMARYProfiling the dynamic state of a metabolic network through the use of time-course metabolomics technologies allows insights into cellular biochemistry. Interpreting these data together at the systems level provides challenges that can be addressed through the development of new visualization approaches. Here, we present a new method for the visualization of time-course metabolomics data that integrates data into an existing metabolic network map. In brief, the metabolomics data are visualized directly on a network map with dynamic elements (nodes that either change size, fill level, or color corresponding with the concentration) while the user controls the time series (i.e., which time point is being displayed) through a graphical interface. We provide short videos that illustrate the utility of this method through its application to existing data sets for the human platelet and erythrocyte. The results presented here give blueprints for the development of visualization methods for other time-course -omics data types that attempt to understand systems-level physiology.

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A Linear Mixed Model Spline Framework for Analysing Time Course ‘Omics’ Data

PLoS ONE ◽

10.1371/journal.pone.0134540 ◽

2015 ◽

Vol 10 (8) ◽

pp. e0134540 ◽

Cited By ~ 18

Author(s):

Jasmin Straube ◽

Alain-Dominique Gorse ◽

Bevan Emma Huang ◽

Kim-Anh Lê Cao ◽

Keyword(s):

Time Course ◽

Mixed Model ◽

Linear Mixed Model ◽

Omics Data

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Ultrastructural Changes of the Symbiotic Chlorella-Like Alga Isolated from Hydra Viridis

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100054212 ◽

1982 ◽

Vol 40 ◽

pp. 320-321

Author(s):

K.W. Lee ◽

R.H. Meints ◽

D. Kuczmarski ◽

J.L. Van Etten

Keyword(s):

Time Course ◽

Reciprocal Cross ◽

Ultrastructural Level ◽

Ultrastructural Changes ◽

Symbiotic Relationship ◽

Cell Recognition ◽

Green Hydra ◽

Different Strains ◽

Hydra Viridis

The physiological, biochemical, and ultrastructural aspects of the symbiotic relationship between the Chlorella-like algae and the hydra have been intensively investigated. Reciprocal cross-transfer of the Chlorellalike algae between different strains of green hydra provide a system for the study of cell recognition. However, our attempts to culture the algae free of the host hydra of the Florida strain, Hydra viridis, have been consistently unsuccessful. We were, therefore, prompted to examine the isolated algae at the ultrastructural level on a time course.

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