Package demo

The R for Mass Spectrometry initiative: from raw data to identifications and quantitative proteomics data analysis

The R for Mass Spectrometry initiative: from raw data to identifications and quantitative proteomics data analysis Author(s): Laurent Gatto,Sebastian Gibb,Johannes Rainer Affiliation(s): de Duve Institute, UCLouvain, Belgium Social media: https://fosstodon.org/@lgatto The aim of the RforMassSpectrometry initiative (https://www.rformassspectrometry.org/) is to provide efficient, thoroughly documented, tested and flexible R software for the analysis and interpretation of high throughput mass spectrometry assays. In this software demo, we will demonstrate three software packages that are central for proteomics data analysis.

Spectra: an expandable infrastructure to handle mass spectrometry data

Spectra: an expandable infrastructure to handle mass spectrometry data Author(s): Johannes Rainer,Sebastian Gibb,Laurent Gatto Affiliation(s): Institute for Biomedicine, Eurac Research, Bolzano, Italy Mass spectrometry (MS) data is a key technology in modern metabolomics and proteomics experiments. Continuous improvements in MS instrumentation, larger experiments and new technological developments lead to ever growing data sizes and increased number of available variables making standard in-memory data handling and processing difficult. The Spectra Bioconductor package provides a modern infrastructure for MS data handling specifically designed to enable extension to additional data resources or alternative data representations.

SPANIEL: Spatial Analysis: Integrating, Expression data with Location

SPANIEL: Spatial Analysis: Integrating, Expression data with Location Author(s): Rachel Queen,Adrienne Unsworth,Lefteris Zormpas,Simon Cockell Affiliation(s): Newcastle University Social media: https://twitter.com/rachelq_ncl Spatial transcriptomics has the potential to revolutionise our understanding of many aspects of biology. The datasets, however, can be cumbersome to analyse and it can be difficult to navigate a field where many new specialised tools are regularly released. Here we present Spaniel; an R package with Shiny app designed to interactively share analysed data between the computational biologist other researchers in the group.

rworkflows: taming the Wild West of R packages

rworkflows: taming the Wild West of R packages Author(s): Brian Schilder,Alan E Murphy,Nathan G Skene Affiliation(s): Imperial College London Social media: https://twitter.com/BMSchilder Despite calls to improve reproducibility in science, achieving this goal remains elusive even within computational fields. >50% of R packages are currently distributed exclusively through GitHub, a code repository that does not require software to adhere to any coding standards or even run at all. This has contributed to the scientific landscape becoming the “Wild West” in terms of code usability and reproducibility.

Multi-omic Integration and Analysis of cBioPortal and TCGA data with MultiAssayExperiment

Multi-omic Integration and Analysis of cBioPortal and TCGA data with MultiAssayExperiment Author(s): Marcel Ramos,Levi Waldron,Ludwig Geistlinger Affiliation(s): CUNY School of Public Health, Roswell Park Comprehensive Cancer Center Social media: https://twitter.com/M2RuseR This workshop demonstrates how to leverage public multi-omics databases, such as the cBioPortal and The Cancer Genome Atlas (TCGA). Workshop participants are given an overview of the `cBioPortalData`, `curatedTCGAData`, `terraTCGAdata`, and `SingleCellMultiModal`, data packages. It introduces users to minimal data management with `MultiAssayExperiment` and `TCGAutils`, packages that organize and manage multi-omics datasets.

Microbiome data integration workflow for population cohort studies

Microbiome data integration workflow for population cohort studies Author(s): Chouaib Benchraka,Tuomas Borman,Leo M Lahti Affiliation(s): Department of Computing, University of Turku, Finland Contemporary microbiome research draws substantially from the heterogeneous omics data sets collected from population cohorts. These data resources are used as discovery cohorts as well as for analytical methods development purposes. Systematically structuring and organizing such data for downstream integration and analyses, and developing scalable analysis methods is essential.

iSEEing is believing: the iSEE package for efficient, interactive, and reproducible exploration of data

iSEEing is believing: the iSEE package for efficient, interactive, and reproducible exploration of data Author(s): Federico Marini,Kevin Christophe Rue-Albrecht,Charlotte Soneson,Aaron Lun Affiliation(s): Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center of the Johannes Gutenberg University Mainz Social media: https://twitter.com/FedeBioinfo The iSEE (Interactive SummarizedExperiment Explorer) software package provides a multi-purpose visual interface for exploring data stored in a SummarizedExperiment object, using the shiny framework. In this package demo, we will provide an overview of its main functionality, centered around a flexible, interactive exploration and interpretation of biological datasets.

Differential Accessible Regions analysis of single-cell 10X Genomics multiome data

Differential Accessible Regions analysis of single-cell 10X Genomics multiome data Author(s): Dario Righelli,Elena Zuin,Davide Risso Affiliation(s): Department of Statistical Sciences, University of Padova, Padua, Italy Social media: https://twitter.com/drighelli Multi-modal single-cell experiments have become increasingly popular in studying biological mechanisms involved in disease and drug treatments. With the emergence of multi-modal single-cell technologies, such as the 10X Genomics Multiome platform, researchers can investigate various cellular characteristics from the same cells, including gene expression (scRNAseq/GEX), chromatin accessibility (scATACseq/ATAC), methylation, and cell surface protein expression.

CytoPipeline: Building and visualizing automated pre-processing and quality control pipelines for flow cytometry data

CytoPipeline: Building and visualizing automated pre-processing and quality control pipelines for flow cytometry data Author(s): Philippe Hauchamps,Dan Lin,Laurent Gatto Affiliation(s): Computation Biology and Bioinformatics (CBIO) Unit, de Duve Institute, UCLouvain, Belgium With the increase of the dimensionality in conventional flow cytometry data over the past years, there is a growing need to replace or complement traditional manual analysis (i.e. iterative 2D gating) with automated data analysis pipelines. Examples of such pipelines have been documented in the recent literature (e.

cytomapper/cytoviewer: R/Bioconductor packages for visualization and exploration of highly multiplexed imaging data

cytomapper/cytoviewer: R/Bioconductor packages for visualization and exploration of highly multiplexed imaging data Author(s): Lasse Meyer,Nils Eling Affiliation(s): Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland; Institute for Molecular Health Sciences, ETH Zurich, Zurich, Switzerland Highly multiplexed imaging allows simultaneous spatially and single-cell resolved detection of dozens of biological molecules (e.g. proteins) in their native tissue context. As a result, these technologies allow an in-depth analysis of complex systems and diseases such as cancer.

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