The projection score - an evaluation criterion for variable subset selection in PCA visualization

BMC Bioinformatics

Research Article

Charlotte Soneson¹  Magnus Fontes¹ 

[1] Centre for Mathematical Sciences, Lund University, Box 118, SE-221 00, Lund, Sweden;

关键词: Principal Component Analysis;    Variable Subset;    Sample Configuration;    Sparse Principal Component Analysis;    Projection Score;   

DOI  :  10.1186/1471-2105-12-307

 received in 2011-04-29, accepted in 2011-07-28,  发布年份 2011

来源: Springer

【 摘 要 】

BackgroundIn many scientific domains, it is becoming increasingly common to collect high-dimensional data sets, often with an exploratory aim, to generate new and relevant hypotheses. The exploratory perspective often makes statistically guided visualization methods, such as Principal Component Analysis (PCA), the methods of choice. However, the clarity of the obtained visualizations, and thereby the potential to use them to formulate relevant hypotheses, may be confounded by the presence of the many non-informative variables. For microarray data, more easily interpretable visualizations are often obtained by filtering the variable set, for example by removing the variables with the smallest variances or by only including the variables most highly related to a specific response. The resulting visualization may depend heavily on the inclusion criterion, that is, effectively the number of retained variables. To our knowledge, there exists no objective method for determining the optimal inclusion criterion in the context of visualization.ResultsWe present the projection score, which is a straightforward, intuitively appealing measure of the informativeness of a variable subset with respect to PCA visualization. This measure can be universally applied to find suitable inclusion criteria for any type of variable filtering. We apply the presented measure to find optimal variable subsets for different filtering methods in both microarray data sets and synthetic data sets. We note also that the projection score can be applied in general contexts, to compare the informativeness of any variable subsets with respect to visualization by PCA.ConclusionsWe conclude that the projection score provides an easily interpretable and universally applicable measure of the informativeness of a variable subset with respect to visualization by PCA, that can be used to systematically find the most interpretable PCA visualization in practical exploratory analysis.

【 授权许可】

CC BY   
© Fontes and Soneson; licensee BioMed Central Ltd. 2011