Exploiting scale-free information from expression data for cancer classification.
Most studies concerning expression data analyses usually exploit information on the variability of gene intensity across samples. This information is sensitive to initial data processing, which affects the final conclusions. However expression data contains scale-free information, which is directly comparable between different samples. We propose to use the pairwise ratio of gene expression values rather than their absolute intensities for a classification of expression data. This information is stable to data processing and thus more attractive for classification analyses. In proposed schema of data analyses only information on relative gene expression levels in each sample is exploited. Testing on publicly available datasets leads to superior classification results.
Antonov
Alexey V
AV
GSF National Research Center for Environment and Health, Institute for Bioinformatics, Ingolstädter Landstrasse 1, D-85764 Neuherberg, Germany. antonov@gsf.de
Tetko
Igor V
IV
Kosykh
Denis
D
Surmeli
Dmitrij
D
Mewes
Hans-Werner
HW
D001943:Breast Neoplasms / Q000145:classification* / Q000235:genetics*; D019992:Databases as Topic / Q000706:statistics & numerical data; D005260:Female; D020869:Gene Expression Profiling / Q000706:statistics & numerical data; D015972:Gene Expression Regulation, Neoplastic; D005910:Glioma / Q000145:classification* / Q000235:genetics*; D006801:Humans; D046228:Microarray Analysis; D009369:Neoplasms / Q000145:classification* / Q000235:genetics*