Reprint of “Abstraction for data integration: Fusing mammalian molecular, cellular and phenotype big datasets for better knowledge extraction”

Highlights

• A small fraction of biomedical Big Data is converted to useful knowledge or reused.

• Overview of a collection of structured mostly molecular mammalian biomedical Big Data resources.

• Biases within data from these resources are suspected.

• Data abstraction to attribute tables, networks and gene-sets enables reuse of biomedical datasets for integrative analyses.

• Once data is abstracted it can be integrated and analyzed using supervised, unsupervised and integrative methods.

Abstract

With advances in genomics, transcriptomics, metabolomics and proteomics, and more expansive electronic clinical record monitoring, as well as advances in computation, we have entered the Big Data era in biomedical research. Data gathering is growing rapidly while only a small fraction of this data is converted to useful knowledge or reused in future studies. To improve this, an important concept that is often overlooked is data abstraction. To fuse and reuse biomedical datasets from diverse resources, data abstraction is frequently required. Here we summarize some of the major Big Data biomedical research resources for genomics, proteomics and phenotype data, collected from mammalian cells, tissues and organisms. We then suggest simple data abstraction methods for fusing this diverse but related data. Finally, we demonstrate examples of the potential utility of such data integration efforts, while warning about the inherit biases that exist within such data.

Introduction

Big Data does not have to be defined by sheer size, i.e., giga-bytes, tera-bytes, or peta-bytes of data, but by the fact that almost all the variables of a complex system can be measured over time and under different conditions (Mayer-Schönberger and Cukier, 2013). Computational biology tools and databases rapidly emerge with an attempt to organize and integrate molecular and phenotype data for the ultimate goal of making predictions by performing virtual experiments. Data integration enables imputing missing values given the already existing data, identifying unexpected relationships between variables, mostly through correlation analyses such as unsupervised clustering, learn-to-rank methods such as enrichment analyses, network reconstruction methods, and supervised machine learning algorithms which are used to make predictions for unseen instances. Integrating x-omics data, a.k.a. the integrome is not as difficult as it may seem because most diverse datasets and resources represent their data in a relatively structured format with common fields such as cells, genes, proteins, drugs, diseases, and assays. Such diverse but structured data can be converted into attribute tables, bi-partite graphs, single-node-type networks, hierarchies and set libraries. Such data structures provide different views of the same data and are useful for different data integration purposes. Combining two or more datasets, if they share common entities such as: genes/proteins, cells, small-molecules/drugs, tissues/tumors/patients, or diseases/phenotypes/side-effects, can lead to new insights. Here we summarize some of the most relevant resources for x-omics data integration for better extracting knowledge from Big Data. We then define the data structures that can be used to combine such resources, and briefly review the primary methods that can be used to operate on the combined data for knowledge discovery, while providing a few examples applied to real data. While we recognize that typically system level data and the methods to integrate and analyze such data were initially developed for model organisms such as yeast, worm, fly and zebra fish, the focus of this review is on data collected from the mammalian system, as well as databases and computation tools applied to the data from mammalian cells, tissues and organisms. Finally, we discuss the concept and implications of the different biases that may exist across the diverse datasets we describe. In this next section we enlist major relevant emergent Big Data resources in computational systems biology.