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Network-Based Interpretation of Diverse High-Throughput Datasets through the Omics Integrator Software Package.

ABSTRACT: High-throughput, 'omic' methods provide sensitive measures of biological responses to perturbations. However, inherent biases in high-throughput assays make it difficult to interpret experiments in which more than one type of data is collected. In this work, we introduce Omics Integrator, a software package that takes a variety of 'omic' data as input and identifies putative underlying molecular pathways. The approach applies advanced network optimization algorithms to a network of thousands of molecular interactions to find high-confidence, interpretable subnetworks that best explain the data. These subnetworks connect changes observed in gene expression, protein abundance or other global assays to proteins that may not have been measured in the screens due to inherent bias or noise in measurement. This approach reveals unannotated molecular pathways that would not be detectable by searching pathway databases. Omics Integrator also provides an elegant framework to incorporate not only positive data, but also negative evidence. Incorporating negative evidence allows Omics Integrator to avoid unexpressed genes and avoid being biased toward highly-studied hub proteins, except when they are strongly implicated by the data. The software is comprised of two individual tools, Garnet and Forest, that can be run together or independently to allow a user to perform advanced integration of multiple types of high-throughput data as well as create condition-specific subnetworks of protein interactions that best connect the observed changes in various datasets. It is available at http://fraenkel.mit.edu/omicsintegrator and on GitHub at https://github.com/fraenkel-lab/OmicsIntegrator.

SUBMITTER: Tuncbag N

PROVIDER: S-EPMC4838263 | biostudies-literature | 2016 Apr

REPOSITORIES: biostudies-literature

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Network-Based Interpretation of Diverse High-Throughput Datasets through the Omics Integrator Software Package.

Tuncbag Nurcan N Gosline Sara J C SJ Kedaigle Amanda A Soltis Anthony R AR Gitter Anthony A Fraenkel Ernest E

PLoS computational biology 20160420 4

High-throughput, 'omic' methods provide sensitive measures of biological responses to perturbations. However, inherent biases in high-throughput assays make it difficult to interpret experiments in which more than one type of data is collected. In this work, we introduce Omics Integrator, a software package that takes a variety of 'omic' data as input and identifies putative underlying molecular pathways. The approach applies advanced network optimization algorithms to a network of thousands of ...[more]

PMID: 27096930

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Project description:BackgroundThe statistical evaluation of pathway enrichment, i.e. of gene profiles' confluence to the pathway level, allows exploring molecular landscapes using functionally annotated gene sets. However, pathway scores can also be used as predictive features in machine learning. That requires, firstly, increasing statistical power and biological relevance via a network enrichment analysis (NEA) and, secondly, a fast and convenient procedure for rendering the original data into a space of pathway scores. However, previous implementations of NEA involved multiple runs of network randomization and were therefore slow.ResultsHere, we present a new R package NEArender which can transform raw 'omics' features of experimental or clinical samples into matrices describing the same samples with many fewer NEA-based pathway scores. This is done via a parametric estimation of the null binomial distribution and is thus much faster and less biased than randomization procedures. Further, we compare estimates from these two alternative procedures and demonstrate that the summarization of individual genes to pathways increases the statistical power compared to both the default differential expression analysis on individual genes and the state-of-the-art gene set enrichment analysis. The package also contains functions for preparing input, modeling null distributions, and evaluating alternative versions of the global network.ConclusionsBeyond the state-of-the-art exploration of molecular data through pathway enrichment, score matrices produced by NEArender can be used in larger bioinformatics pipelines as input for phenotype modeling, predicting disease outcomes etc. This approach is often more sensitive and robust than using the original data. The package NEArender is complementary to the online NEA tool EviNet ( https://www.evinet.org ) and, unlike of the latter, enables high performance of computations off-line. The R package NEArender version 1.4 is available at CRAN repository https://cran.r-project.org/web/packages/NEArender/.

Project description:A variety of high throughput genome-wide assays enable the exploration of genetic risk factors underlying complex traits. Although these studies have remarkable impact on identifying susceptible biomarkers, they suffer from issues such as limited sample size and low reproducibility. Combining individual studies of different genetic levels/platforms has the promise to improve the power and consistency of biomarker identification. In this paper, we propose a novel integrative method, namely sparse group multitask regression, for integrating diverse omics datasets, platforms, and populations to identify risk genes/factors of complex diseases. This method combines multitask learning with sparse group regularization, which will: (1) treat the biomarker identification in each single study as a task and then combine them by multitask learning; (2) group variables from all studies for identifying significant genes; (3) enforce sparse constraint on groups of variables to overcome the "small sample, but large variables" problem. We introduce two sparse group penalties: sparse group lasso and sparse group ridge in our multitask model, and provide an effective algorithm for each model. In addition, we propose a significance test for the identification of potential risk genes. Two simulation studies are performed to evaluate the performance of our integrative method by comparing it with conventional meta-analysis method. The results show that our sparse group multitask method outperforms meta-analysis method significantly. In an application to our osteoporosis studies, 7 genes are identified as significant genes by our method and are found to have significant effects in other three independent studies for validation. The most significant gene SOD2 has been identified in our previous osteoporosis study involving the same expression dataset. Several other genes such as TREML2, HTR1E, and GLO1 are shown to be novel susceptible genes for osteoporosis, as confirmed from other studies.

Dataset Information

Network-Based Interpretation of Diverse High-Throughput Datasets through the Omics Integrator Software Package.

Publications

Network-Based Interpretation of Diverse High-Throughput Datasets through the Omics Integrator Software Package.

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