Systematic Interpretation of High-Throughput Biological Data

Abstract

MATLAB has evolved from the command-line-based ``MATrix LABoratory” into a fullyfeatured programming environment. But is it really practical for implementing a larger software package? Also if it is intended to run on servers and if Unix is preferred as a server operation system? What if there are more problem-related statistical methods available in R? Positive answers to these and more questions are shown in example discussing the ``MultiConditional Hybridization Processing System” (M-CHiPS). Here, as well, the name is not entirely descriptive because apart from the classical microarray hybridizations it takes data from e.g. antibody array incubations as well as methylation or quantitative tandem mass spectrometry data by now. The system was implemented predominantly in MATLAB. It currently contains more than 13,000 hybridizations, incubations, gels, runs etc. comprising all common microarray transcriptomics platforms but also genomic chip data, chip-based methylation data, 2D-DIGE gels, antibody arrays (both single and dual-channel), and TMT 6-plex MS/MS data. Apart from tumor biopsies, it contains also data about model organisms, e.g. Trypansosoma brucei, Candida albicans, and Aspergillus fumigates, to date 11 organisms in total. While data stemming from e. g. Microarray and Mass Spectrometry platforms need very different preprocessing steps prior to data interpretation, the result can generally be regarded as a table with its columns representing some biological conditions, e.g. various genotypes, growth conditions or tumor stages, just to give some examples. Also, in most cases, each row roughly represents a “gene”, more precisely standing for its DNA sequence, methylation status, RNA transcript abundance, or protein level. Thus, quantitative data stemming from different platforms and representing the status of either the transcriptome, methylome or the proteome can be collected in the very same format (database structure, MATLAB variables). Also, the same set of algorithms can be applied for analysis and visualization. However, the patterns comprised by these large genes × conditions data tables cannot be understood without additional information. The behaviours of some ten thousands of genes need to be explained by Gene Ontology terms or transcription factor binding sites. And often hundreds of samples need to be related to represented genotypes, growth conditions or disease states in order to interpret these data. In addition to the signal intensities, MCHiPS records information about the protocols involved (to track down systematic errors), sample biology and clinical data. Risk parameters such as alcohol consumption and