Use of SVD-based probit transformation in clustering gene expression profiles

DOI10.1016/j.csda.2007.01.022zbMath1445.62275OpenAlexW2144018724MaRDI QIDQ1020741

Faming Liang

Publication date: 2 June 2009

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2007.01.022

zbMATH Keywords

singular value decompositionmodel-based clusteringgene expression profilesprobit transformation

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10)

Related Items (5)

Polarization of forecast densities: a new approach to time series classification ⋮ A hypothesis test using bias-adjusted AR estimators for classifying time series in small samples ⋮ An exposition of multivariate analysis with the singular value decomposition in R ⋮ Adaptive clustering for time series: application for identifying cell cycle expressed genes ⋮ Editorial: Statistical genetics \& statistical genomics: where biology, epistemology, statistics, and computation collide

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• Tight Clustering: A Resampling‐Based Approach for Identifying Stable and Tight Patterns in Data

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