Workshops

Workshop: Tools for CITE-seq preprocessing

Tools for CITE-seq preprocessing Helen Lindsay,Bernat Bramon Mora,Raphael Gottardo Biomedical Data Science Center at the Lausanne University Hospital (CHUV) Abstract CITE-seq and related technologies use antibody-bound oligo probes to get a quantitative readout of surface protein expression. These technologies have the potential to enable more fine-grained exploration of single cell phenotypes. How best to normalise antibody-derived tag (ADT) expression data and integrate it with other data modalities is an active research area.

Workshop: The Spectra package: seamless integration of mass spectrometry data from different sources

The Spectra package: seamless integration of mass spectrometry data from different sources Johannes Rainer,Michael Witting,Sebastian Gibb,Laurent Gatto Institute for Biomedicine, Eurac Research, Bolzano, Italy Abstract Mass spectrometry (MS) data is a key technology in modern proteomics and metabolomics experiments. Due to continuous improvements in MS instrumentation, the generated data can easily become very large. Also, different additional resources of MS data exist, such as spectra libraries and databases, all with their own specific file formats that sometimes do not support manipulations of the original data.

Workshop: Single-cell multi-modal data handling in R/Bioconductor

Single-cell multi-modal data handling in R/Bioconductor Dario Righelli,Davide Risso Department of Statistical Sciences, University of Padova Abstract Single-cell multi-modal experiments are becoming more common for investigating the roles of biological mechanisms involved in disease and in drug treatments. In the last few years, multiple multi-modal single-cell technologies have emerged, such as 10x-Genomics Multiome. These technologies allow to investigate gene expression (scRNAseq) together with chromatin accessibility (scATACseq), methylation, cell surface protein expression, and more.

Workshop: Single-cell based spatial analysis and visualization of highly multiplexed imaging data

Single-cell based spatial analysis and visualization of highly multiplexed imaging data Nils Eling,Jonas Windhager University of Zurich Abstract Highly multiplexed imaging similarly to FISH-based spatial transcriptomics allows the detection of tens of biomolecules in single cells across tissue sections. Upon image processing and segmentation, the protein/RNA expression as well as the location and morphological features of individual cells are extracted for downstream analysis. We developed the steinbock framework to support image pre-processing, segmentation, feature extraction, and data export in a reproducible fashion.

Workshop: simplifyEnrichment: A Bioconductor Package for Clustering and Visualizing Functional Enrichment Results

simplifyEnrichment: A Bioconductor Package for Clustering and Visualizing Functional Enrichment Results Zuguang Gu German Cancer Research Center Abstract Functional enrichment analysis or gene set enrichment analysis is a basic bioinformatics method that evaluates biological importance of a list of genes of interest. However, it may produce a long list of significant terms with highly redundant information that is difficult to summarize. Current tools to simplify enrichment results by clustering them into groups either still produce redundancy between clusters or do not retain consistent term similarities within clusters.

Workshop: Inference and Analysis of Synteny Networks with syntenet

Inference and Analysis of Synteny Networks with syntenet Fabrício Almeida-Silva,Kristian K Ullrich,Tao Zhao,Yves Van de Peer FA-S and YVdP: VIB-UGent Center for Plant Systems Biology, Ghent, Belgium; KKU: Max Planck Institute for Evolutionary Biology, Ploen, Germany; TZ: Northwest A&F University, Shaanxi, China Abstract The analysis of synteny (i.e., conserved gene content and order in a genomic segment across species) can help understand the trajectory of duplicated genes through evolution. In particular, synteny analyses are widely used to investigate the evolution of genes derived from whole-genome duplication (WGD) events, as they can reveal genomic rearrangements that happened following the duplication of all chromosomes.

Workshop: Differential abundance analysis for label-free mass spectrometry-based proteomics

Differential abundance analysis for label-free mass spectrometry-based proteomics Laurent Gatto,Stijn Vandenbulcke,Nina Demeulemeester,Lieven Clement de Duve Institute, UCLouvain, Belgium Abstract Mass spectrometry (MS) based proteomics experiments generate ever larger datasets and, as a consequence, complex data interpretation challenges. This hands-on package demo focuses on the key concepts for differential analysis of MS-based proteomics data acquired via label free data dependent technologies. Moreover, examples involving more advanced experimental designs and blocking will also be introduced.

Workshop: benchdamic: benchmarking of differential abundance methods for microbiome data

benchdamic: benchmarking of differential abundance methods for microbiome data Matteo Calgaro,Chiara Romualdi,Davide Risso,Nicola Vitulo University of Verona Abstract Recently, an increasing amount of methodological approaches have been proposed to tackle the complexity of metagenomics and microbiome data. In this scenario, reproducibility and replicability have become two critical issues, and the development of computational frameworks for the comparative evaluations of such methods is of utmost importance. Here, we present benchdamic, a Bioconductor package to benchmark methods for the identification of differentially abundant taxa.

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