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Figure . . [his figure also appears in the color insert.] Let: unsorted data matrix (log ratio gene
expression) map with two proximity matrix (Pearson correlation for arrays and Euclidean distance for
genes) maps for Dataset . Right: application of elliptical seriations to the three matrix maps on the let
panel
Color Spectrum
he selection of an appropriate color spectrum can be critical and is user-dependent
invisualization ofandinformation extraction fromdata andproximitymatrices. he
selection ofa suitable color spectrumshould focuson the capacity toexpressnumer-
ical nature individually and globally in the matrices. he choices for gene expression
profiles that we mentioned above may well give way to others in different circum-
stances. hus,illustrated inFig. . isacorrelation matrix map of fity psychosis dis-
order variables (Chen, ) coded with four different bidirectional color spectra.
While displays (a) and (b) appear more agreeable to our human perception, displays
(c)and(d)actually providebetterresolution fordistinguishing differentlevelsofcor-
relation intensities. herelative triplet color codes (red,green, blue) in the RGBcube
for these four color spectra are shown in Fig. . .
Display Conditions
he display conditions are analogous to data transformations for colors. Usually, the
whole color spectrum is used to represent the complete range of values in the data
matrix. he matrix conditions can be switched to row or column conditions to em-
phasize individual variable distributions or subject profiles. For a bidirectional color
spectrum(green-black-redfordifferentialgeneexpressions,blue-white-redforcor-
relation coe cients), the center matrix condition symmetrizes the color spectrum
around the baseline numeric value ( : for log ratio gene expression, zero for the
correlation coe cient). On occasion, we might want to downweight the effects of
extreme values in the dataset, and it is possible to use ranks as a replacement for
numerical values. his is termed the rank matrix condition.
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