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ABSTRACT: Background
There is great interest to study how gene pathways change their structure across different tissues. The assessment of inter-study reliability of pathway changes across tissues can inform on the fraction of tissues with specific functional changes in network structure. However, there is a lack of agreement measures among studies that independently observe how a group of observations change across conditions. We, therefore, propose ?, a new inter-study reliability measure that determines the consistency to distinguish observations by condition.Results
We derived ?'s distributional characteristics, determine its reliability properties and compared it with Cohen's ?. We studied the co-expression structure of 287 gene pathways across four brain regions in two transcriptomic studies and applied ? to assess the inter-study reliability of the pathways' brain-regional changes. Brain-related pathways showed highest ?; the top value was for the nicotine addiction pathway whose structure was reliably distinguishable among regions with dopaminergic projections.Conclusion
Our results offer novel substantial evidence that changes in network structure across tissues can be inferred independently of samples, algorithms and experiments (RNA-sequencing or microarrays). Reliability measures, such as ?, can inform on the tissues where changes in a network's structure are likely functional. An R package is available at https://github.com/isglobal-brge/lambda .
SUBMITTER: Caceres A
PROVIDER: S-EPMC6327576 | biostudies-literature | 2019 Jan
REPOSITORIES: biostudies-literature
Cáceres Alejandro A Gonzalez Juan R JR
BMC genomics 20190109 1
<h4>Background</h4>There is great interest to study how gene pathways change their structure across different tissues. The assessment of inter-study reliability of pathway changes across tissues can inform on the fraction of tissues with specific functional changes in network structure. However, there is a lack of agreement measures among studies that independently observe how a group of observations change across conditions. We, therefore, propose λ, a new inter-study reliability measure that d ...[more]