PT  - JOURNAL ARTICLE
AU  - Armin Deffur
AU  - Robert J Wilkinson
AU  - Bongani M Mayosi
AU  - Nicola Mulder
TI  - ANIMA: Association Network Integration for Multiscale Analysis
AID  - 10.1101/257642
DP  - 2018 Jan 01
TA  - bioRxiv
PG  - 257642
4099  - http://biorxiv.org/content/early/2018/02/10/257642.short
4100  - http://biorxiv.org/content/early/2018/02/10/257642.full
AB  - Contextual functional interpretation of -omics data derived from clinical samples is a classical and difficult problem in computational systems biology. The measurement of thousands of datapoints on single samples has become routine but relating ‘big data’ datasets to the complexities of human pathobiology is an area of ongoing research. Complicating this is the fact that many publically available datasets use bulk transcriptomics data from complex tissues like blood. The most prevalent analytic approaches derive molecular ‘signatures’ of disease states or apply modular analysis frameworks to the data. Here we show, using a network-based data integration method using clinical phenotype and microarray data as inputs, that we can reconstruct multiple features (or endophenotypes) of disease states at various scales of organization, from transcript abundance patterns of individual genes through co-expression patterns of groups of genes to patterns of cellular behavior in whole blood samples, both in single experiments as well as in a meta-analysis of multiple datasets.