Compositional data

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In statistics, compositional data are quantitative descriptions of the parts of some whole, conveying exclusively relative information.

This definition, given by John Aitchison (1986) has several consequences:

  • A compositional data point, or composition for short, can be represented by a positive real vector with as many parts as considered. Sometimes, if the total amount is fixed and known, one component of the vector can be omitted.
  • As compositions only carry relative information, the only information is given by the ratios between components. Consequently, a composition multiplied by any positive constant contains the same information as the former. Therefore, proportional positive vectors are equivalent when considered as compositions.
  • As usual in mathematics, equivalent classes are represented by some element of the class, called a representative. Thus, equivalent compositions can be represented by positive vectors whose components add to a given constant . The vector operation assigning the constant sum representative is called closure and is denoted by :

where D is the number of parts (components) and denotes a row vector.

  • Compositional data can be represented by constant sum real vectors with positive components, and this vectors span a simplex, defined as

This is the reason why is considered to be the sample space of compositional data. The positive constant is arbitrary. Frequent values for are 1 (per unit), 100 (percent, %), 1000, 106 (ppm), 109 (ppb), ...

  • In statistics, compositional data is frequently considered to be data in which each data point is a D-tuple of nonnegative numbers whose sum is 1. Typically each of the D components xi of each data point [x1, ..., xD] says what proportion (or "percentage") of a statistical unit falls into the ith category in a list of D categories. Very often ternary plots are used in analysis of compositional data to represent a three part composition.
  • An alternative nomenclature for compositional analysis is simplicial analysis, motivated by the concept of simplicial sets.

Remarks on the definition of the simplex:

  • In mathematical frameworks, the superscript of , accounting for the number of parts, is often changed to D − 1, describing the dimension.
  • The components of the vector are assumed to be positive. However, in some definitions of the simplex, non-negative components are admitted. Here null components are avoided, because ratios between components of which some are zero are meaningless.


  • Each data point may correspond to a rock composed of three different minerals; a rock of which 10% is the first mineral, 30% is the second, and the remaining 60% is the third would correspond to the triple [0.1, 0.3, 0.6]; a data set would contain one such triple for each rock in a sample of rocks.
  • Each data point may correspond to a town; a town in which 35% of the people are Christians, 55% are Muslims, 6% are Jews, and the remaining 4% are others would correspond to the quadruple [0.35, 0.55, 0.06, 0.04]; a data set would correspond to a list of towns.
  • In chemistry, compositions can be expressed as molar concentrations of each component. As the sum of all concentrations is not determined, the whole composition of D parts is needed and thus expressed as a vector of D molar concentrations. These compositions can be translated into weight per cent multiplying each component by the appropriated constant.
  • In a survey, the proportions of people positively answering some different items can be expressed as percentages. As the total amount is identified as 100, the compositional vector of D components can be defined using only D − 1 components, assuming that the remaining component is the percentage needed for the whole vector to add to 100.
  • In probability and statistics, a partition of the sampling space into disjoint events is described by the probabilities assigned to such events. The vector of D probabilities can be considered as a composition of D parts. As they add to one, one probability can be suppressed and the composition is completely determined.
  • In high throughput sequencing, data obtained are count compositions since the capacity of the machine determines the number of reads observed. These reduce to probabilities of observing a feature given the sequencing depth.


  • Aitchison J. (1986), The Statistical Analysis of Compositional Data, Chapman & Hall; reprinted in 2003, with additional material, by The Blackburn Press.
  • van den Boogaart K. G., Tolosana-Delgado R. (2013), Analyzing Compositional Data With R, Springer.
  • Pawlowsky-Glahn V., Egozcue J. J., Tolosana-Delgado R. (2015), Modeling and Analysis of Compositional Data, Wiley.

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