A measure of agreement across numerous conditions: Assessing when changes in network structures are tissue-specific

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 λ, a new inter-study reliability measure that determines the consistency to distinguish observations by condition. Results: We derived λ's distributional characteristics, determine its reliability properties and compared it with Cohen's κ. We studied the co-expression structure of 287 gene pathways across four brain regions in two transcriptomic studies and applied λ to assess the inter-study reliability of the pathways' brain-regional changes. Brain-related pathways showed highest λ; 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 λ, 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.