Testing of Chromosomal Clumping of Gene Properties
Artikel i vetenskaplig tidskrift, 2009

Clumping of gene properties like expression or mutant phenotypes along chromosomes is commonly detected using completely random null-models where their location is equally likely across the chromosomes. Interpretation of statistical tests based on these assumptions may be misleading if dependencies exist that are unequal between chromosomes or in different chromosomal parts. One such regional dependency is the telomeric effect, observed in several studies of Saccharomyces cerevisiae, under which e. g. essential genes are less likely to reside near the chromosomal ends. In this study we demonstrate that standard randomisation test procedures are of limited applicability in the presence of telomeric effects. Several extensions of such standard tests are here suggested for handling clumping simultaneously with regional differences in essentiality frequencies in sub-telomeric and central gene positions. Furthermore, a general non-homogeneous discrete Markov approach for combining parametrically modelled position dependent probabilities of a dichotomous property with a simple single parameter clumping is suggested. This Markov model is adapted to the observed telomeric effects and then simulations are used to demonstrate properties of the suggested modified randomisation tests. The model is also applied as a direct alternative tool for statistical analysis of the S. cerevisiae genome for clumping of phenotypes.

Markov chains

order

saccharomyces-cerevisiae

dynamics

clumping of essential genes

statistical tests

coexpression

genome biology

caenorhabditis-elegans

genome

evolution

S. cerevisiae

Författare

L. Fernandez-Ricaud

Daniel Dalevi

Chalmers, Data- och informationsteknik, Datavetenskap

A. Blomberg

Olle Nerman

Chalmers, Matematiska vetenskaper, matematisk statistik

Göteborgs universitet

Statistical Applications in Genetics and Molecular Biology

1544-6115 (ISSN)

Vol. 8 1 19 (artno)-

Ämneskategorier

Sannolikhetsteori och statistik

Genetik

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Skapat

2017-10-07