Olfactory receptors likely constitute the largest gene superfamily in the vertebrate genome. Here we present the nearly complete human olfactory subgenome elucidated by mining the genome draft with gene discovery algorithms. Over 900 olfactory receptor genes and pseudogenes (ORs) were identified, two-thirds of which were not annotated previously. The number of extrapolated ORs is in good agreement with previous theoretical predictions. The sequence of at least 63% of the ORs is disrupted by what appears to be a random process of pseudogene formation. ORs constitute 17 gene families, 4 of which contain more than 100 members each. "Fish-like" Class I ORs, previously considered a relic in higher tetrapods, constitute as much as 10% of the human repertoire, all in one large cluster on chromosome 11. Their lower pseudogene fraction suggests a functional significance. ORs are disposed on all human chromosomes except 20 and Y, and nearly 80% are found in clusters of 6-138 genes. A novel comparative cluster analysis was used to trace the evolutionary path that may have led to OR proliferation and diversification throughout the genome. The results of this analysis suggest the following genome expansion history: first, the generation of a "tetrapod-specific" Class II OR cluster on chromosome 11 by local duplication, then a single-step duplication of this cluster to chromosome 1, and finally an avalanche of duplication events out of chromosome 1 to most other chromosomes. The results of the data mining and characterization of ORs can be accessed at the Human Olfactory Receptor Data Exploratorium Web site (http://bioinfo.weizmann.ac.il/HORDE).

/pdf/the-complete-human-olfactory-subgenome-kkm62770zz.pdf

The Complete Human Olfactory Subgenome

Introduction.- Vector and Matrix Algebra.- The Multivariate Normal Distribution, Multivariate Normality, and Covariance Structure.- One- and Two-Sample Tests.- Multivariate Analysis of Variance.- Discriminant Analysis.- Canonical Correlation.- Principal Component Analysis.- Factor Analysis.- Structural Equations.

Applied Multivariate Analysis

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The Design of Experiments: Statistical Principles for Practical Applications.

Conexión y conectores : estudio de su relación en el registro informal de la lengua

Principal component analysis is often considered as the bas ic method of factor analysis, which aims to find linear combinations of the p variables called components used to visualize the observations in a simple way. Because i t transforms a large number of correlated variables into a few uncorrelated prin cipal components, PCA is a dimension reduction method. However, PCA can also be use d a a multivariate outlier detection method, especially by studying the last p rincipal components. This property is useful in multidimensional quality control.

https://hal.archives-ouvertes.fr/hal-01125713/document

Principal Component Analysis: Application to Statistical Process Control

Contributions to this work begin from a homogeneous-analytic point of view, then go further to analyze continuous variables, extending the geometrical approach of Gifi, and applying functional analytic techniques to problems involving replicated time series data (which are not subject to classical correspondence analysis and principal component analysis). Closing chapters address probability coding (which is related to fuzzy coding), and two approaches to component analysis: optimal scaling (which uses montone splines) embedded in a more classical statistical framework; and the connection between explorative multivariate data analysis and confirmation analysis based on statistical modelling.

Jan de Leeus

Papers

Component and correspondence analysis: dimension reduction by functional approximation