TY - JOUR
T1 - Differential network analysis of multiple human tissue interactomes highlights tissue-selective processes and genetic disorder genes
AU - Basha, Omer
AU - Argov, Chanan M.
AU - Artzy, Raviv
AU - Zoabi, Yazeed
AU - Hekselman, Idan
AU - Alfandari, Liad
AU - Chalifa-Caspi, Vered
AU - Yeger-Lotem, Esti
AU - Yeger-Lotem, Esti
N1 - Funding Information:
This work has been supported by the Broad Institute - Israel Science Foundation partnership for cell circuits research [2435/16 to E.Y.-L].
Funding Information:
This work has been supported by the Broad Institute-Israel Science Foundation partnership for cell circuits research [2435/16 to E.Y.-L].
Publisher Copyright:
© 2020 The Author(s). Published by Oxford University Press. All rights reserved.
PY - 2020/5/1
Y1 - 2020/5/1
N2 - Motivation: Differential network analysis, designed to highlight network changes between conditions, is an important paradigm in network biology. However, differential network analysis methods have been typically designed to compare between two conditions and were rarely applied to multiple protein interaction networks (interactomes). Importantly, large-scale benchmarks for their evaluation have been lacking. Results: Here, we present a framework for assessing the ability of differential network analysis of multiple human tissue interactomes to highlight tissue-selective processes and disorders. For this, we created a benchmark of 6499 curated tissue-specific Gene Ontology biological processes. We applied five methods, including four differential network analysis methods, to construct weighted interactomes for 34 tissues. Rigorous assessment of this benchmark revealed that differential analysis methods perform well in revealing tissue-selective processes (AUCs of 0.82-0.9). Next, we applied differential network analysis to illuminate the genes underlying tissue-selective hereditary disorders. For this, we curated a dataset of 1305 tissue-specific hereditary disorders and their manifesting tissues. Focusing on subnetworks containing the top 1% differential interactions in disease-relevant tissue interactomes revealed significant enrichment for disorder-causing genes in 18.6% of the cases, with a significantly high success rate for blood, nerve, muscle and heart diseases. Summary: Altogether, we offer a framework that includes expansive manually curated datasets of tissue-selective processes and disorders to be used as benchmarks or to illuminate tissue-selective processes and genes. Our results demonstrate that differential analysis of multiple human tissue interactomes is a powerful tool for highlighting processes and genes with tissue-selective functionality and clinical impact.
AB - Motivation: Differential network analysis, designed to highlight network changes between conditions, is an important paradigm in network biology. However, differential network analysis methods have been typically designed to compare between two conditions and were rarely applied to multiple protein interaction networks (interactomes). Importantly, large-scale benchmarks for their evaluation have been lacking. Results: Here, we present a framework for assessing the ability of differential network analysis of multiple human tissue interactomes to highlight tissue-selective processes and disorders. For this, we created a benchmark of 6499 curated tissue-specific Gene Ontology biological processes. We applied five methods, including four differential network analysis methods, to construct weighted interactomes for 34 tissues. Rigorous assessment of this benchmark revealed that differential analysis methods perform well in revealing tissue-selective processes (AUCs of 0.82-0.9). Next, we applied differential network analysis to illuminate the genes underlying tissue-selective hereditary disorders. For this, we curated a dataset of 1305 tissue-specific hereditary disorders and their manifesting tissues. Focusing on subnetworks containing the top 1% differential interactions in disease-relevant tissue interactomes revealed significant enrichment for disorder-causing genes in 18.6% of the cases, with a significantly high success rate for blood, nerve, muscle and heart diseases. Summary: Altogether, we offer a framework that includes expansive manually curated datasets of tissue-selective processes and disorders to be used as benchmarks or to illuminate tissue-selective processes and genes. Our results demonstrate that differential analysis of multiple human tissue interactomes is a powerful tool for highlighting processes and genes with tissue-selective functionality and clinical impact.
UR - http://www.scopus.com/inward/record.url?scp=85084379842&partnerID=8YFLogxK
U2 - 10.1093/bioinformatics/btaa034
DO - 10.1093/bioinformatics/btaa034
M3 - Article
C2 - 31960892
AN - SCOPUS:85084379842
SN - 1367-4803
VL - 36
SP - 2821
EP - 2828
JO - Bioinformatics
JF - Bioinformatics
IS - 9
ER -