by Dr Jan Koster, Team Leader Bioinformatics in Dept. Oncogenomics at AMC
11:00-11:50, room CCA 1.06
Introduction to R2 – integrating clinical and genomics data
R2 is a publicly accessible web-based program (http://r2.amc.nl) allowing biomedical researchers, without bioinformatics training, to integrate clinical and genomics data.
Link to Jan’s presentation: http://www.drylab.nl/wp-content/uploads/2016/04/Presentation_Jan-Koster-R2_160303.pdf
For more information see: http://hgserver1.amc.nl/cgi-bin/r2/main.cgi?option=about_intro_workshop
There will also be a workshop about R2 on
Monday, April 18th 2016:
Introduction to R2 –
Integrated Analysis of (Tumor) Genomics Data with R2
by Prof. Mark van de Wiel (VU/VUmc Department Biostatistics and Epidemiology)
11:00-11:50, room CCA 1.06
For many -omics studies, additional information on the features, like genomic annotation, external p-values, or correlation to another type of genomic variable, is available. In the context of binary (case-control), survival and continuous prediction (or classification), we introduce a method which makes structural use of such additional information, termed ‘co-data’. The co-data is used to a priori group the variables. Then, the method estimates group-specific penalties (or weights), which may lead to improved prediction performance. The method has several nice properties: i) it adapts to the informativeness of the co-data for the data at hand; ii) it can deal with multiple sources of co-data; iii) it is fast.
We show that the group-specific weights may facilitate post-hoc feature selection. The method, termed GRridge, is implemented in an easy-to-use R-package. It is demonstrated on two cancer genomics studies, which both concern the discrimination of precancerous cervical lesions from normal cervical tissues using methylation microarray data. For the first study, we use genomic annotation concerning the type of genomic region in which the probe is located (e.g. a CpG-island) to define the groups. For the second study, which concerns clinically relevant but impure samples, we use the p-values of the first study as a basis for the groups. For both examples, GRridge clearly improves the predictive performance of well-known alternatives. In addition, we show that for the second study the relatively good predictive performance is maintained when selecting only 42 probes.
by Dr Oscar Krijgsman (Nederlands Kanker Instituut)
10:30-11:30, room CCA 1.06
Current methods for detection of copy number variants (CNV) and aberrations (CNA) from targeted sequencing data are based on the depth of coverage of captured exons. Accurate CNA determination is complicated by uneven genomic distribution and non-uniform capture efficiency of targeted exons. Here we present CopywriteR, which eludes these problems by exploiting ‘off-target’ sequence reads. CopywriteR allows for extracting uniformly distributed copy number information, can be used without reference, and can be applied to sequencing data obtained from various techniques including chromatin immunoprecipitation and target enrichment on small gene panels. CopywriteR outperforms existing methods and constitutes a widely applicable alternative to available tools.
Dr Oscar Krijgsman did his PhD with Prof Bauke Ylstra and is now working at the NKI as Postdoc.