Create interactive, reproducible views of omics datasets with the iSEE Bioconductor framework. Participants configure linked panels, inspect bulk and single-cell RNA data and preserve exploratory workflows. Guided cookbook exercises progress to independent exploration, collections of datasets and participants’ own projects, with troubleshooting and companion-package demonstrations. The framework also supports spatial and proteomic data represented by SummarizedExperiment objects. Sessions: 25–26 November, 13:00–17:00 daily, Europe/Berlin (CET, UTC+01:00). Live online training; fee €280. For
In Bio-Informatics and Genomics
Analyze gene expression within tissue using imaging-based and sequencing-based spatial transcriptomics. Guided R/Bioconductor labs cover cell segmentation, quality control, interactive exploration and integration with single-cell datasets. Downstream analyses characterize cellular niches, neighbourhoods and cell–cell communication. The course concludes with project design and a team hackathon using participant or public data, connecting computational outputs to biological interpretation. 2026-12-14–2026-12-18, 14:00–18:00 daily, Europe/Berlin (CET, UTC+01:00). Online course; fee €530. Designe
Work through mass-spectrometry proteomics analysis in R and Bioconductor, from raw observations to statistical interpretation. Practical tutorials cover data formats, extraction and visualization, integration of identification results, quantitative data structures and preprocessing. The final session uses statistical analysis to identify proteins with different abundance between experimental groups. 2027-01-27–2027-01-29, 14:00–20:00 daily, Europe/Berlin (CET, UTC+01:00). Online course; fee €380. Working R knowledge is required. Mass-spectrometry or proteomics familiarity is useful but not es