Author:
Kim Philip M.,Tidor Bruce
Abstract
The availability of parallel, high-throughput biological experiments that
simultaneously monitor thousands of cellular observables provides an
opportunity for investigating cellular behavior in a highly quantitative
manner at multiple levels of resolution. One challenge to more fully exploit
new experimental advances is the need to develop algorithms to provide an
analysis at each of the relevant levels of detail. Here, the data analysis
method non-negative matrix factorization (NMF) has been applied to the
analysis of gene array experiments. Whereas current algorithms identify
relationships on the basis of large-scale similarity between expression
patterns, NMF is a recently developed machine learning technique capable of
recognizing similarity between subportions of the data corresponding to
localized features in expression space. A large data set consisting of 300
genome-wide expression measurements of yeast was used as sample data to
illustrate the performance of the new approach. Local features detected are
shown to map well to functional cellular subsystems. Functional relationships
predicted by the new analysis are compared with those predicted using standard
approaches; validation using bioinformatic databases suggests predictions
using the new approach may be up to twice as accurate as some conventional
approaches.
Publisher
Cold Spring Harbor Laboratory
Subject
Genetics (clinical),Genetics
Cited by
164 articles.
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