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» Gene set analysis using principal components
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BMCBI
2010
153views more  BMCBI 2010»
13 years 8 months ago
Challenges in microarray class discovery: a comprehensive examination of normalization, gene selection and clustering
Background: Cluster analysis, and in particular hierarchical clustering, is widely used to extract information from gene expression data. The aim is to discover new classes, or su...
Eva Freyhult, Mattias Landfors, Jenny Önskog,...
MIAR
2006
IEEE
14 years 2 months ago
Statistics of Pose and Shape in Multi-object Complexes Using Principal Geodesic Analysis
Abstract. A main focus of statistical shape analysis is the description of variability of a population of geometric objects. In this paper, we present work in progress towards mode...
Martin Styner, Kevin Gorczowski, P. Thomas Fletche...
IJCNN
2006
IEEE
14 years 2 months ago
Generalizing Independent Component Analysis for Two Related Data Sets
— We introduce in this paper methods for finding mutually corresponding dependent components from two different but related data sets in an unsupervised (blind) manner. The basi...
Juha Karhunen, Tomas Ukkonen
BMCBI
2008
145views more  BMCBI 2008»
13 years 8 months ago
Mapping gene expression quantitative trait loci by singular value decomposition and independent component analysis
Background: The combination of gene expression profiling with linkage analysis has become a powerful paradigm for mapping gene expression quantitative trait loci (eQTL). To date, ...
Shameek Biswas, John D. Storey, Joshua M. Akey
IDA
1998
Springer
13 years 7 months ago
Fast Dimensionality Reduction and Simple PCA
A fast and simple algorithm for approximately calculating the principal components (PCs) of a data set and so reducing its dimensionality is described. This Simple Principal Compo...
Matthew Partridge, Rafael A. Calvo