MOTIVATION: Flow cytometry (FC) is a widely used technique for analysing cells or particles based on the fluorescence of specific markers. Thresholds for fluorescence are typically set manually, a laborious, subjective process that scales poorly as FC technology advances. Machine learning (ML) methods can address these issues but often require technical expertise many bench scientists do not possess. Thus, accessible, open-source, and cross-domain ML-based FC tools are needed. RESULTS: We present AutoFlow, an easy-to-use, adaptable R Shiny application for automated flow cytometry (FC) analysis. AutoFlow supports two workflows: supervised and unsupervised learning. The application automates key preprocessing steps including fluorescence compensation, debris exclusion, single-cell identification, viability marker gating, and downstream classification or clustering. Across three datasets, two publicly available (Mosmann and Nilsson Rare) and a novel bone marrow microphysiological system (BM-MPS) dataset, AutoFlow demonstrated robust performance. In the supervised workflow, multiclass classification on BM-MPS achieved 97.2% accuracy under a single-timepoint training and multi-timepoint testing scheme, with high sensitivity and specificity across major lineages. For rare populations, performance was strong: Mosmann Rare (0.03% prevalence) achieved 87.5% sensitivity, and 100% specificity, while Nilsson Rare (0.08% prevalence) achieved 87.9% sensitivity, and 99.9% specificity. The unsupervised workflow accurately grouped cells into biologically meaningful clusters, recovering known populations and identifying additional candidate populations with marker profiles consistent with true biology. AutoFlow offers a fast, reproducible, and scalable solution for FC analysis, enabling high-throughput studies and improving the discovery of rare or unexpected cell types. AVAILABILITY AND IMPLEMENTATION: The application is available at https://github.com/FERWoods/AutoFlow for download using R. An archived version is available at DOI: 10.5281/zenodo.18235796.

SUMMARY: Capture Hi-C is a powerful approach for detecting chromosomal interactions involving, at least on one end, DNA regions of interest, such as gene promoters. We present Chicdiff, an R package for robust detection of differential interactions in Capture Hi-C data. Chicdiff enhances a state-of-the-art differential testing approach for count data with bespoke normalization and multiple testing procedures that account for specific statistical properties of Capture Hi-C. We validate Chicdiff on published Promoter Capture Hi-C data in human Monocytes and CD4+ T cells, identifying multitudes of cell type-specific interactions, and confirming the overall positive association between promoter interactions and gene expression. AVAILABILITY AND IMPLEMENTATION: Chicdiff is implemented as an R package that is publicly available at https://github.com/RegulatoryGenomicsGroup/chicdiff. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Identification of genomic regions of interest in ChIP-seq data, commonly referred to as peak-calling, aims to find the locations of transcription factor binding sites, modified histones or nucleosomes. The BayesPeak algorithm was developed to model the data structure using Bayesian statistical techniques and was shown to be a reliable method, but did not have a full-genome implementation. RESULTS: In this note we present BayesPeak, an R package for genome-wide peak-calling that provides a flexible implementation of the BayesPeak algorithm and is compatible with downstream BioConductor packages. The BayesPeak package introduces a new method for summarizing posterior probability output, along with methods for handling overfitting and support for parallel processing. We briefly compare the package with other common peak-callers. AVAILABILITY: Available as part of BioConductor version 2.6. URL: http://bioconductor.org/packages/release/bioc/html/BayesPeak.html.

Abstract Summary: With their many replicates and their random layouts, Illumina BeadArrays provide greater scope fordetecting spatial artefacts than do other microarray technologies. They are also robust to artefact exclusion, yet there is a lack of tools that can perform these tasks for Illumina. We present BASH, a tool for this purpose. BASH adopts the concepts of Harshlight, but implements them in a manner that utilizes the unique characteristics of the Illumina technology. Using bead-level data, spatial artefacts of various kinds can thus be identified and excluded from further analyses. Availability: The beadarray Bioconductor package (version 1.10 onwards), www.bioconductor.org Contact: [email protected] Supplementary information: Additional information and a vignette are included in the beadarray package.