Not Just a Sum? Identifying Different Types of Interplay between Constituents in Combined Interventions

• Published in: PloS one 2015-05, Vol.10 (5), p.e0125334-e0125334
• Main Authors: Van Deun, Katrijn, Thorrez, Lieven, van den Berg, Robert A, Smilde, Age K, Van Mechelen, Iven
• Format: Article
• Language: English
• Subjects: Animals; beta-Glucans - pharmacology; Bioinformatics; Biology; Cells, Cultured; Combined treatment; Computer Simulation; Constituents; Databases, Genetic; Dendritic cells; Dendritic Cells - drug effects; Dendritic Cells - metabolism; Diclofenac; DNA microarrays; Drug therapy; Drug Therapy, Combination; Empirical analysis; Gene expression; Gene Expression Profiling; Gene Expression Regulation - drug effects; Genetic engineering; Granulocyte-macrophage colony-stimulating factor; Granulocyte-Macrophage Colony-Stimulating Factor - pharmacology; Health aspects; Hypotheses; Hypothesis testing; Kinases; Melanoma; Mice; Mode of action; Models, Statistical; Oligonucleotide Array Sequence Analysis; Pattern Recognition, Automated - methods; Quantitative psychology; Sorafenib; Statistical methods; Statistics; Studies; Taxonomy; Test procedures; Transcription factors
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0125334

Experiments in which the effect of combined manipulations is compared with the effects of their pure constituents have received a great deal of attention. Examples include the study of combination therapies and the comparison of double and single knockout model organisms. Often the effect of the combined manipulation is not a mere addition of the effects of its constituents, with quite different forms of interplay between the constituents being possible. Yet, a well-formalized taxonomy of possible forms of interplay is lacking, let alone a statistical methodology to test for their presence in empirical data. Starting from a taxonomy of a broad range of forms of interplay between constituents of a combined manipulation, we propose a sound statistical hypothesis testing framework to test for the presence of each particular form of interplay. We illustrate the framework with analyses of public gene expression data on the combined treatment of dendritic cells with curdlan and GM-CSF and show that these lead to valuable insights into the mode of action of the constituent treatments and their combination. R code implementing the statistical testing procedure for microarray gene expression data is available as supplementary material. The data are available from the Gene Expression Omnibus with accession number GSE32986.