Molecular signatures that can be transferred across different omics platforms

Autor: Jörg Reinders, Michael Altenbuchinger, Neus Masqué-Soler, Peter J. Oefner, Wolfram Klapper, Wolfram Gronwald, Thorsten Rehberg, Christian W. Kohler, Rainer Spang, Monika Szczepanowski, Philipp Schwarzfischer, Julia Richter
Rok vydání: 2017
Předmět:
0301 basic medicine
Statistics and Probability
Matching (statistics)
Proteome
Computer science
Proteomics
computer.software_genre
Biochemistry
Transcriptome
03 medical and health sciences
0302 clinical medicine
Formaldehyde
Freezing
Humans
Molecular Biology
Models
Statistical
Paraffin Embedding
Computational Biology
Transmed
Omics
Burkitt Lymphoma
Ismb/Eccb 2017: The 25th Annual Conference Intelligent Systems for Molecular Biology Held Jointly with the 16th Annual European Conference on Computational Biology
Prague
Czech Republic
July 21–25
2017
Computer Science Applications
Human tumor
Computational Mathematics
030104 developmental biology
Computational Theory and Mathematics
Feature (computer vision)
030220 oncology & carcinogenesis
Lymphoma
Large B-Cell
Diffuse
Tissue Preservation
Data mining
Erratum
DNA microarray
computer
Algorithms
Software
Zdroj: Bioinformatics
ISSN: 1367-4811
Popis: Motivation Molecular signatures for treatment recommendations are well researched. Still it is challenging to apply them to data generated by different protocols or technical platforms. Results We analyzed paired data for the same tumors (Burkitt lymphoma, diffuse large B-cell lymphoma) and features that had been generated by different experimental protocols and analytical platforms including the nanoString nCounter and Affymetrix Gene Chip transcriptomics as well as the SWATH and SRM proteomics platforms. A statistical model that assumes independent sample and feature effects accounted for 69–94% of technical variability. We analyzed how variability is propagated through linear signatures possibly affecting predictions and treatment recommendations. Linear signatures with feature weights adding to zero were substantially more robust than unbalanced signatures. They yielded consistent predictions across data from different platforms, both for transcriptomics and proteomics data. Similarly stable were their predictions across data from fresh frozen and matching formalin-fixed paraffin-embedded human tumor tissue. Availability and Implementation The R-package ‘zeroSum’ can be downloaded at https://github.com/rehbergT/zeroSum. Complete data and R codes necessary to reproduce all our results can be received from the authors upon request.
Databáze: OpenAIRE
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