Abstract
Could there be unexpected similarities between different studies, diseases, or treatments, on a molecular level due to common biological mechanisms involved? To answer this question, we develop a method for computing similarities between empirical, statistical distributions of high-dimensional, low-sample datasets, and apply it on hundreds of -omics studies. The similarities lead to dataset-to-dataset networks visualizing the landscape of a large portion of biological data. Potentially interesting similarities connecting studies of different diseases are assembled in a disease-to-disease network. Exploring it, we discover numerous non-trivial connections between Alzheimer’s disease and schizophrenia, asthma and psoriasis, or liver cancer and obesity, to name a few. We then present a method that identifies the molecular quantities and pathways that contribute the most to the identified similarities and could point to novel drug targets or provide biological insights. The proposed method acts as a “statistical telescope” providing a global view of the constellation of biological data; readers can peek through it at: http://datascope.csd.uoc.gr:25000/.
Highlights
Public biological data repositories currently hold tens of thousands of-datasets
The question arises: how do the measurements from these studies compare against each other, what are their relations, and what is the collective, emerging picture and biological intuition they provide? Can we construct and look through a “statistical telescope” instead? Could it be that different diseases, treatments, other experimental or sampling conditions induce similar biological molecular patterns pointing to common pathophysiological pathways? Their identification could accelerate the deeper understanding of human pathology and the exploitation of clinical study results
Overall 103,088 Homo Sapiens and Mus Musculus samples were employed in the subsequent analyses and results, grouped in 978 datasets and spanning more than 500 different diseases and phenotypes, as revealed by automated text analysis.[2]
Summary
Public biological data repositories currently hold tens of thousands of (bio)-datasets. As of October 2019, the NCBI Gene Expression Omnibus (GEO)[1] contains 3,263,365 microarray and RNA-Seq profiles, grouped into 119,386 data series. Each dataset studies a specific biological question, regarding a disease, a treatment, or a phenotype. Examples include finding the gene expression differences between malignant and benign breast tissue or creating a diagnostic model between primary and metastatic lung cancer tumors. Data analysis methods typically focus on individually analyzing each dataset, like a “statistical microscope”. The question arises: how do the measurements from these studies compare against each other, what are their relations, and what is the collective, emerging picture and biological intuition they provide? Could it be that different diseases, treatments, other experimental or sampling conditions induce similar biological molecular patterns pointing to common pathophysiological pathways? The question arises: how do the measurements from these studies compare against each other, what are their relations, and what is the collective, emerging picture and biological intuition they provide? Can we construct and look through a “statistical telescope” instead? Could it be that different diseases, treatments, other experimental or sampling conditions induce similar biological molecular patterns pointing to common pathophysiological pathways? Their identification could accelerate the deeper understanding of human pathology and the exploitation of clinical study results
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