ColorPhylo: A Color Code to Accurately Display Taxonomic Classifications

Author:

Lespinats Sylvain1,Fertil Bernard2

Affiliation:

1. UMR INSERM unité U722 and Université Denis Diderot, Paris 7, Faculté de médecine, site Xavier Bichat, 16 rue Henri Huchard, 75870 Paris cedex 18, France.

2. Laboratoire LSIS (UMR CNRS 6168), Equipe I&M (ESIL) case 925, 163, avenue de Luminy, 13288 Marseille cedex 9, France.

Abstract

Color may be very useful to visualise complex data. As far as taxonomy is concerned, color may help observing various species’ characteristics in correlation with classification. However, choosing the number of subclasses to display is often a complex task: on the one hand, assigning a limited number of colors to taxa of interest hides the structure imbedded in the subtrees of the taxonomy; on the other hand, differentiating a high number of taxa by giving them specific colors, without considering the underlying taxonomy, may lead to unreadable results since relationships between displayed taxa would not be supported by the color code. In the present paper, an automatic color coding scheme is proposed to visualise the levels of taxonomic relationships displayed as overlay on any kind of data plot. To achieve this goal, a dimensionality reduction method allows displaying taxonomic “distances” onto a Euclidean two-dimensional space. The resulting map is projected onto a 2D color space (the Hue, Saturation, Brightness colorimetric space with brightness set to 1). Proximity in the taxonomic classification corresponds to proximity on the map and is therefore materialised by color proximity. As a result, each species is related to a color code showing its position in the taxonomic tree. The so called ColorPhylo displays taxonomic relationships intuitively and can be combined with any biological result. A Matlab version of ColorPhylo is available at http://sy.lespi.free.fr/ColorPhylo-homepage.html . Meanwhile, an ad-hoc distance in case of taxonomy with unknown edge lengths is proposed.

Publisher

SAGE Publications

Subject

Computer Science Applications,Genetics,Ecology, Evolution, Behavior and Systematics

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