Abstract
High-throughput transcriptomic profiling (HTTr) enables scalable characterisation of transcriptional responses to chemical and genetic perturbations. While plate-based technologies such as MAC-Seq, TempO-seq and PLATE-seq have made HTTr more accessible, they pose unique computational challenges for data modelling and integration across modalities. We present macpie, an R package designed to streamline the analysis of HTTr data from plate-based screens. Built on the tidySeurat framework, macpie streamlines the entire analytical pipeline from preprocessing and quality control to pathway enrichment, chemical feature extraction, and multimodal data integration. The package incorporates multiple statistical frameworks and uses parallelisation for scalability. By leveraging Docker and Nextflow, macpie ensures reproducibility and ease of use for transcriptome-wide screening.Data availability
All code and example datasets used in this study are available in the macpie GitHub repository (https://github.com/PMCC-BioinformaticsCore/macpie). Additional data supporting the findings of this study are available from the corresponding author upon reasonable request.