Improving visualization of interaction effects of expressed genes in microarray studies

FERNÁNDEZ, ELMER ANDRÉS; SALVATIERRA, EDGARDO; BIZAMA, CAROLINA; BENAVENTE, FELIPE; GIDEKEL, MANUEL; PODHAJCER, OSVALDO LUIS; BALZARINI, MÓNICA

Florianopolis, Brasil

Congreso; International Congress of the International Biometric Socienty, Florianopolis, Brasil, Dic. 2010; 2010

International Biometry Socienty

Visualization techniques such us Heatmaps (the most common) or Biplots are used to visualize microarray gene expression analysis data. In both cases a simultaneous representation of genes and arrays/samples in a bi-dimensional plot is achieved. The traditional values for gene expression level analyses are the array normalized log intensities. Nowadays, more complex experiments are carried out and many different sources of systematic variation, related to experimental factor such us gender or sex of sampled experimental units, could be present. These factors could mask important biological effects when expression levels are displayed through Heatmaps or Biplots. In order to improve the visualization of interactions between treatments and other experimental factor effects, we propose the use of residuals from an additive reduced model over those genes where the treatments effect is present, that is differentially expressed genes. The methodology is shown in a cancer study where we explore the interaction effects between treatment (tumour and adjacent tissue) and sex of patient from who tissue samples were drawn. To identify expressed genes the following gene by gene additive model is proposed    where µ is the overall gene expression mean, T, S and TS stands for treatment, Sex and interaction effects,   is random term for the patient effect, and the last term is a random error  . Here we are interested in those genes were TS≠0.  Classically, the HeatMap is done by using Y as input values of those genes satisfying TS≠0. Here we propose to use the residuals from a reduced model, that is,  where   and their residuals are   to feed the Heatmap or Biplot procedure. In our case we expect that upregulated (downregulated) genes in tumour (adjacent) samples from females (males) are downregulated (upregulated) in males (females). By means of this approach the interaction effects are much more clearly seen compared to the usual approach, being a suitable visualization strategy for biological experiments.