title: Biomarker discovery and redundancy reduction towards classification using a multi-factorial MALDI-TOF MS T2DM mouse model dataset.
abstract: BACKGROUND
  Diabetes like many diseases and biological processes is not mono-causal. On the one hand multi-factorial studies with complex experimental design are required for its comprehensive analysis. On the other hand, the data from these studies often include a substantial amount of redundancy such as proteins that are typically represented by a multitude of peptides. Coping simultaneously with both complexities (experimental and technological) makes data analysis a challenge for Bioinformatics.
  
  
  RESULTS
  We present a comprehensive work-flow tailored for analyzing complex data including data from multi-factorial studies. The developed approach aims at revealing effects caused by a distinct combination of experimental factors, in our case genotype and diet. Applying the developed work-flow to the analysis of an established polygenic mouse model for diet-induced type 2 diabetes, we found peptides with significant fold changes exclusively for the combination of a particular strain and diet. Exploitation of redundancy enables the visualization of peptide correlation and provides a natural way of feature selection for classification and prediction. Classification based on the features selected using our approach performs similar to classifications based on more complex feature selection methods.
  
  
  CONCLUSIONS
  The combination of ANOVA and redundancy exploitation allows for identification of biomarker candidates in multi-dimensional MALDI-TOF MS profiling studies with complex experimental design. With respect to feature selection our method provides a fast and intuitive alternative to global optimization strategies with comparable performance. The method is implemented in R and the scripts are available by contacting the corresponding author.
authors.affiliation: MicroDiscovery GmbH, Marienburger Str, 1, 10405 Berlin, Germany. chris.bauer@microdiscovery.de
authors.forename: Chris
authors.initials: C
authors.lastname: Bauer
authors.forename: Frank
authors.initials: F
authors.lastname: Kleinjung
authors.forename: Celia J
authors.initials: CJ
authors.lastname: Smith
authors.forename: Mark W
authors.initials: MW
authors.lastname: Towers
authors.forename: Ali
authors.initials: A
authors.lastname: Tiss
authors.forename: Alexandra
authors.initials: A
authors.lastname: Chadt
authors.forename: Tanja
authors.initials: T
authors.lastname: Dreja
authors.forename: Dieter
authors.initials: D
authors.lastname: Beule
authors.forename: Hadi
authors.initials: H
authors.lastname: Al-Hasani
authors.forename: Knut
authors.initials: K
authors.lastname: Reinert
authors.forename: Johannes
authors.initials: J
authors.lastname: Schuchhardt
authors.forename: Rainer
authors.initials: R
authors.lastname: Cramer
mesh_terms: D000418:Albumins / Q000032:analysis; D000704:Analysis of Variance; D000818:Animals; D015415:Biomarkers / Q000032:analysis*; D003924:Diabetes Mellitus, Type 2 / Q000097:blood / Q000209:etiology / Q000235:genetics*; D004032:Diet; D006454:Hemoglobins / Q000032:analysis; D008297:Male; D051379:Mice; D008810:Mice, Inbred C57BL; D008820:Mice, Obese; D010455:Peptides / Q000032:analysis; D040901:Proteomics / Q000379:methods*; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization / Q000379:methods*
chemical_list: D000418:Albumins; D015415:Biomarkers; D006454:Hemoglobins; D010455:Peptides