Marcel Dettling

The Bioconductor project is an initiative for the collaborative creation of extensible software for computational biology and bioinformatics. The goals of the project include: fostering collaborative development and widespread use of innovative software, reducing barriers to entry into interdisciplinary scientific research, and promoting the achievement of remote reproducibility of research results. We describe details of our aims and methods, identify current challenges, compare Bioconductor to other open bioinformatics projects, and provide working examples.

The Bioconductor project is an initiative for the collaborative creation of the extensible software for computational biology and bioinformatics. The goals of the project include: fostering collaborative development and widespread use of innovative software, reducing barriers to entry into interdisciplinary scientific research, and promoting the achievement of remote reproducibility of research results. We describe details of our aims and methodes, identify current challenges, compare Bioconductor to other open bioinformatics projects, and provide working examples.

MOTIVATION: Microarray experiments are expected to contribute significantly to the progress in cancer treatment by enabling a precise and early diagnosis. They create a need for class prediction tools, which can deal with a large number of highly correlated input variables, perform feature selection and provide class probability estimates that serve as a quantification of the predictive uncertainty. A very promising solution is to combine the two ensemble schemes bagging and boosting to a novel algorithm called BagBoosting. RESULTS: When bagging is used as a module in boosting, the resulting classifier consistently improves the predictive performance and the probability estimates of both bagging and boosting on real and simulated gene expression data. This quasi-guaranteed improvement can be obtained by simply making a bigger computing effort. The advantageous predictive potential is also confirmed by comparing BagBoosting to several established class prediction tools for microarray data. AVAILABILITY: Software for the modified boosting algorithms, for benchmark studies and for the simulation of microarray data are available as an R package under GNU public license at http://stat.ethz.ch/~dettling/bagboost.html.

MOTIVATION: Microarray experiments generate large datasets with expression values for thousands of genes but not more than a few dozens of samples. Accurate supervised classification of tissue samples in such high-dimensional problems is difficult but often crucial for successful diagnosis and treatment. A promising way to meet this challenge is by using boosting in conjunction with decision trees. RESULTS: We demonstrate that the generic boosting algorithm needs some modification to become an accurate classifier in the context of gene expression data. In particular, we present a feature preselection method, a more robust boosting procedure and a new approach for multi-categorical problems. This allows for slight to drastic increase in performance and yields competitive results on several publicly available datasets. AVAILABILITY: Software for the modified boosting algorithms as well as for decision trees is available for free in R at http://stat.ethz.ch/~dettling/boosting.html.

Rhabdomyosarcoma is a pediatric tumor type, which is classified based on histological criteria into two major subgroups, namely embryonal rhabdomyosarcoma and alveolar rhabdomyosarcoma. The majority, but not all, alveolar rhabdomyosarcoma carry the specific PAX3(7)/FKHR-translocation, whereas there is no consistent genetic abnormality recognized in embryonal rhabdomyosarcoma. To gain additional insight into the genetic characteristics of these subtypes, we used oligonucleotide microarrays to measure the expression profiles of a group of 29 rhabdomyosarcoma biopsy samples (15 embryonal rhabdomyosarcoma, and 10 translocation-positive and 4 translocation-negative alveolar rhabdomyosarcoma). Hierarchical clustering revealed expression signatures clearly discriminating all three of the subgroups. Differentially expressed genes included several tyrosine kinases and G protein-coupled receptors, which might be amenable to pharmacological intervention. In addition, the alveolar rhabdomyosarcoma signature was used to classify an additional alveolar rhabdomyosarcoma case lacking any known PAX3 or PAX7 fusion as belonging to the translocation-positive group, leading to the identification of a novel translocation t(2;2)(q35;p23), which generates a fusion protein composed of PAX3 and the nuclear receptor coactivator NCOA1, having similar transactivation properties as PAX3/FKHR. These experiments demonstrate for the first time that gene expression profiling is capable of identifying novel chromosomal translocations.