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VisiumIO: Working with 10X Genomics Visium Data

VisiumIO: Working with 10X Genomics Visium Data Author(s): Marcel Ramos Pérez Affiliation(s): CUNY School of Public Health, New York, N.Y., USA Bioconductor’s approach to re-using existing classes enables the growth of the software ecosystem. We make use of the BiocIO and TENxIO frameworks to work with and represent 10X Visium data. The VisiumIO package provides functions that import 10X Visium and 10X Visium HD data from the Space Ranger pipeline.

SpaceTrooper, an R package for the preprocessing and quality control of imaging-based spatial transcriptomics data

SpaceTrooper, an R package for the preprocessing and quality control of imaging-based spatial transcriptomics data Author(s): Dario Righelli,Benedetta Banzi,Oriana Romano,Mattia Forcato,Silvio Bicciato,Davide Risso Affiliation(s): Department of Electrical Engineer and Information Technology, University of Naples Federico II Emerging technologies in spatially resolved single-cell omics offer high-throughput solutions for measuring the molecular characteristics of cells in situ. Single-cell spatial profiling combines highly multiplexed imaging modalities, next-generation sequencing, or mass spectrometry to assess the spatial distribution of gene and protein expression within the native context of cells.

Pathway-centric analyses of omics data with GSVA

Pathway-centric analyses of omics data with GSVA Author(s): Axel Klenk,Robert Castelo Affiliation(s): Universitat Pompeu Fabra GSVA (https://bioconductor.org/packages/GSVA) is an R/Bioconductor package that enables pathway-centric analyses of data produced by high-throughput molecular profiling technologies. The interpretation of biological findings from such data is one of the cornerstones of biomedical research, and GSVA facilitates that goal by performing a conceptually simple but powerful change in the functional unit of analysis, from genes to gene sets.

Orchestrating spot and cellular resolution Spatial Transcriptomics workflow with Bioconductor – SpatialExperimentIO, STexampleData 2.0, and ggspavis 2.0

Orchestrating spot and cellular resolution Spatial Transcriptomics workflow with Bioconductor – SpatialExperimentIO, STexampleData 2.0, and ggspavis 2.0 Author(s): Yixing Dong,Lukas M Weber Affiliation(s): Biomedical Data Science Center - Lausanne University Hospital The recent advancements in spatial transcriptomics (SRT) have been primarily catalyzed by innovative technologies. Among these, Visium CytAssist (10X Genomics) and imaging-based SRTs like Xenium (also by 10X Genomics) offer varying resolutions, catering to both spot and cellular levels of analysis, respectively.

Navigating and expanding the iSEE universe

Navigating and expanding the iSEE universe Author(s): Kevin Christophe Rue-Albrecht,Charlotte Soneson,Federico Marini,Aaron Lun Affiliation(s): MRC WIMM Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK. iSEE (interactive SummarizedExperiment Explorer) is a software package that produces an interactive, multi-purpose, and thoroughly configurable user interface for exploring any type of biological data stored in a SummarizedExperiment object. Thanks to its modular framework, a number of extension packages have been developed, adding new visualisations and functionality for both app developers and end-users.

miaverse – microbiome analysis framework within the SummarizedExperiment ecosystem

miaverse – microbiome analysis framework within the SummarizedExperiment ecosystem Author(s): Tuomas Borman,Leo M Lahti Affiliation(s): University of Turku Microbiome refers to a collection of microbes and their genetic material in a certain habitat. It has a pivotal role in human health and disease, yet the underlying mechanisms are still often unclear. The research relies on sequencing data to elucidate microbial composition and function, additionally, the adoption of multiomics approach is becoming increasingly common.

Integrating DNA methylation with RNA-seq in lungs: Demonstrating the tidyomics ecosystem

Integrating DNA methylation with RNA-seq in lungs: Demonstrating the tidyomics ecosystem Author(s): Min Hyung Ryu,Stefano Mangiola Affiliation(s): Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital & Harvard Medical School Advanced technologies in genomics, epigenomics, transcriptomics, spatial analysis, and multi-omics have transformed biomedical research, offering both opportunities and challenges in data handling, exploration, analysis, integration, and interpretation. Here, we present the tidyomics software ecosystem (https://github.com/tidyomics), bridging Bioconductor to the tidy R paradigm.

Enhancing Reproducibility: A Comprehensive Workflow for Integrating and Visualizing Biological Networks

Enhancing Reproducibility: A Comprehensive Workflow for Integrating and Visualizing Biological Networks Author(s): Florian Auer,Hryhorii Chereda,Júlia Perera-Bel,Frank Kramer Affiliation(s): IT-Infrastructure for Translational Medical Research, Faculty of Applied Computer Science, University of Augsburg, Augsburg, Germany Reliable research findings are the cornerstone of scientific progress but with the advancing reproducibility crisis, there is an increased need for the development of robust methodologies for the documentation of the used data and entire workflows. Especially network biology faces significant challenges in achieving reproducible research since diverse data types like protein interactions and gene expression from various resources are involved.

Compositional analyses of the Human Cell Atlas with sccomp

Compositional analyses of the Human Cell Atlas with sccomp Author(s): Stefano Mangiola Affiliation(s): Adelaide University Large-scale single-cell resources such as the Human Cell Atlas present an unprecedented opportunity to explore fundamental questions in biology and immunology across organs and demographics. For example, analysing the changes in immune composition across organs, in ageing, and between sexes and ethnicities can reveal important aspects of the human population's diversity that should be considered in precision medicine research.

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