INVESTIGADORES
FERNANDEZ elmer Andres
congresos y reuniones científicas
Título:
Aiming at the visualization of interactions between treatment ands other experimental factors on HeatMaps.
Autor/es:
FERNÁNDEZ, ELMER ANDRÉS; SALVATIERRA, EDGARDO; BIZAMA, CAROLINA; GIDEKEL, MANUEL; PODHAJCER, OSVALDO LUIS; BALZARINI, MÓNICA
Lugar:
Quilmes, Argentina
Reunión:
Congreso; ongreso de Bioinformatica y Biologia Computacional, Quilmes Mayo 12-14,2010; 2010
Institución organizadora:
Sociedad Argentina de Bioinformatica y Biologia Computacional
Resumen:
p { margin-bottom: 0.08in; }
Background
One of the most used visualization
methods for microarray gene expression analysis is the HeatMap, that
simultaneously represents hierarchical clusters of genes and
arrays/samples in a bi-dimensional image where expression values are
represented by a color scale. The usual way for representing the
expression levels of many expressed genes, to remove undesired
technical effects, is to use the within and between arrays/samples
normalized expression values as input of the HeatMap algorithm.
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. In order to improve the
visualization of interactions between treatment and other
experimental factor effects, we propose the use of residuals from an
additive reduced model over those genes where the treatment effect is
present, that is differentially expressed genes. The methodology is
shown in two cancer studies where we explore the interaction effects
between treatment (tumor and adjacent tissue) and sex of patient from
who tissue samples were drawn. For each gene the following model is
proposed where is the overall gene expression mean, T, S and TS
stands for treatment, sex and their interaction effect, is random
term for the patient effect, and the the last term is a random
error. Here we are interested in those genes were . Classically, the
heatmap is done by using Y as input values. We propose to use the
residuals from the additive model, that is, where and their residuals
are , as input for the HeatMap. In our case we expect that
upregulated (downregulated) genes in tumor (adjacent) samples from
females (males) are downregulated (upregulated) in males (females).
This effect is easily seen with the proposed approach (right panel in
the figure ) but not using the Y data (left panel in the figure).