The histopathological evaluation of biopsies by human experts is a gold standard in clinical disease diagnosis. While recent artificial intelligence-based (AI) approaches have reached human expert-level performance, they often display shortcomings caused by variations in sample preparation, limiting clinical applicability. This study investigates the impact of data variation on AI-based histopathological grading and explores algorithmic approaches that confer prediction robustness. To evaluate the impact of data variation in histopathology, we collected a multicentric, retrospective, observational prostate cancer (PCa) trial consisting of six cohorts in 3 countries with 25,591 patients, 83,864 images. This includes a high-variance dataset of 8,157 patients and 28,236 images with variations in section thickness, staining protocol, and scanner. This unique training dataset enabled the development of an AI-based PCa grading framework by training on patient outcome, not subjective grading. It was made robust through several algorithmic adaptations, including domain adversarial training and credibility-guided color adaptation. We named the final grading framework PCAI. We compare PCAI to a BASE model and human experts on three external test cohorts, comprising 2,255 patients and 9,437 images. Variations in sample processing, particularly section thickness and staining time, significantly reduced the performance of AI-based PCa grading by up to 8.6 percentage points in the event-ordered concordance index (EOC-Index) thus highlighting serious risks for AI-based histopathological grading. Algorithmic improvements for model robustness, credibility, and training on high-variance data as well as outcome-based severity prediction give rise to robust models with grading performance surpassing experienced pathologists. We demonstrate how our algorithmic enhancements for greater robustness lead to significantly better performance, surpassing expert grading on EOC-Index and 5-year AUROC by up to 21.2 percentage points.

MOTIVATION: The identification of biologically meaningful domains is a central step in the analysis of spatial transcriptomic data. RESULTS: Following Occam's razor, we show that a simple PCA-based algorithm for unsupervised spatial domain identification rivals the performance of ten competing state-of-the-art methods across six single-cell spatial transcriptomic datasets. Our reductionist approach, NichePCA, provides researchers with intuitive domain interpretation and excels in execution speed, robustness, and scalability. AVAILABILITY AND IMPLEMENTATION: The code is available at https://github.com/imsb-uke/nichepca.

SUMMARY: Transcript quantification tools efficiently map bulk RNA sequencing (RNA-seq) reads to reference transcriptomes. However, their output consists of transcript count estimates that are subject to multiple biases and cannot be readily used with existing differential gene expression analysis tools in Python.Here we present pytximport, a Python implementation of the tximport R package that supports a variety of input formats, different modes of bias correction, inferential replicates, gene-level summarization of transcript counts, transcript-level exports, transcript-to-gene mapping generation, and optional filtering of transcripts by biotype. pytximport is part of the scverse ecosystem of open-source Python software packages for omics analyses and includes both a Python as well as a command-line interface.With pytximport, we propose a bulk RNA-seq analysis workflow based on Bioconda and scverse ecosystem packages, ensuring reproducible analyses through Snakemake rules. We apply this pipeline to a publicly available RNA-seq dataset, demonstrating how pytximport enables the creation of Python-centric workflows capable of providing insights into transcriptomic alterations. AVAILABILITY AND IMPLEMENTATION: pytximport is licensed under the GNU General Public License version 3. The source code is available at https://github.com/complextissue/pytximport and via Zenodo with DOI: 10.5281/zenodo.13907917. A related Snakemake workflow is available through GitHub at https://github.com/complextissue/snakemake-bulk-rna-seq-workflow and Zenodo with DOI: 10.5281/zenodo.12713811. Documentation and a vignette for new users are available at: https://pytximport.readthedocs.io.