Cancer Genomics: Integrative and Scalable Solutions in R/Bioconductor

Abstract Bioconductor is a crucial resource for statistical analysis and data management in cancer genomics research, providing more than 2,200 open-source software and data packages. This software ecosystem is supported by core data classes and methods that provide convenient representations and efficient operations for many kinds

of high-throughput omics data. Recent technical advances enable increasingly resolved study of the molecular biology of cancer at the single-cell level, through combined assaying of DNA sequence, epigenetics, gene expression, protein, and other aspects, even with spatial information. These developments present new

challenges in the complexity, size, and interpretability of data analysis. The overarching goal of this project is to maintain and expand the core Bioconductor software infrastructure to meet these challenges, through the following aims. First, we will maintain and expand infrastructure for multimodal experiments and spatial

transcriptomics, and connect the R/Bioconductor ecosystem with non-R image analysis tools to facilitate statistical analysis of histopathology images in the context of spatial transcriptomics and other molecular data. Second, we will transition Bioconductor’s data and annotation-sharing tools to a federated, language-agnostic

system that facilitates contribution and extension by the community, improves findability, enables automated improvement in metadata, and enhances utility for non-R users. Third, we will create curated and integrated data repositories that make key datasets more findable and usable, and drive the development of the planned new

data-sharing systems. Finally, we will develop a program of user training and new outreach approaches to support the training of users and developers, including the creation of a large language model-based chatbot and a cloud-based platform with persistent disks for courses and workshops. By fostering knowledge

dissemination and practical training, we aim to empower researchers with the necessary skills and resources to leverage the enhanced capabilities of Bioconductor for advanced cancer genomics research.