Limits...
Biomarker discovery and redundancy reduction towards classification using a multi-factorial MALDI-TOF MS T2DM mouse model dataset.

Bauer C, Kleinjung F, Smith CJ, Towers MW, Tiss A, Chadt A, Dreja T, Beule D, Al-Hasani H, Reinert K, Schuchhardt J, Cramer R - BMC Bioinformatics (2011)

Bottom Line: 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.

View Article: PubMed Central - HTML - PubMed

Affiliation: MicroDiscovery GmbH, Marienburger Str, 1, 10405 Berlin, Germany. chris.bauer@microdiscovery.de

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.

Show MeSH

Related in: MedlinePlus

Dendrogram Hemoglobin. Excerpt of the dendrogram in Figure 4 showing the three peaks identified as hemoglobin (colored red on the x-axis).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3116487&req=5

Figure 5: Dendrogram Hemoglobin. Excerpt of the dendrogram in Figure 4 showing the three peaks identified as hemoglobin (colored red on the x-axis).

Mentions: Protein composition of blood is typically dominated by albumin and other highly abundant proteins such as hemoglobin. Albumin and hemoglobin are large proteins represented by a multitude of peptides and thus should be presented by multiple peaks in our dataset. Assuming that many of their peptides are correlated they should be located in close proximity in the dendrogram. MS-based profile peak identification revealed one albumin and three hemoglobin peptides. Mapping the three hemoglobin peptide peaks in the dendrogram shows that they are indeed in close proximity (see Figure 5) verifying our assumption. The peak identified as albumin is located in the big cluster in the central part.


Biomarker discovery and redundancy reduction towards classification using a multi-factorial MALDI-TOF MS T2DM mouse model dataset.

Bauer C, Kleinjung F, Smith CJ, Towers MW, Tiss A, Chadt A, Dreja T, Beule D, Al-Hasani H, Reinert K, Schuchhardt J, Cramer R - BMC Bioinformatics (2011)

Dendrogram Hemoglobin. Excerpt of the dendrogram in Figure 4 showing the three peaks identified as hemoglobin (colored red on the x-axis).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3116487&req=5

Figure 5: Dendrogram Hemoglobin. Excerpt of the dendrogram in Figure 4 showing the three peaks identified as hemoglobin (colored red on the x-axis).
Mentions: Protein composition of blood is typically dominated by albumin and other highly abundant proteins such as hemoglobin. Albumin and hemoglobin are large proteins represented by a multitude of peptides and thus should be presented by multiple peaks in our dataset. Assuming that many of their peptides are correlated they should be located in close proximity in the dendrogram. MS-based profile peak identification revealed one albumin and three hemoglobin peptides. Mapping the three hemoglobin peptide peaks in the dendrogram shows that they are indeed in close proximity (see Figure 5) verifying our assumption. The peak identified as albumin is located in the big cluster in the central part.

Bottom Line: 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.

View Article: PubMed Central - HTML - PubMed

Affiliation: MicroDiscovery GmbH, Marienburger Str, 1, 10405 Berlin, Germany. chris.bauer@microdiscovery.de

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.

Show MeSH
Related in: MedlinePlus