Biology subjects

Torkel, M.

Publications and source records attributed to Torkel, M..

3 recordsLinked to original sources

bioRxiv2025

Multi-view gene panel characterization for spatially resolved omics

Spatially resolved transcriptomics has transformed our ability to study complex tissues at the cellular and subcellular resolution. However, targeted spatial technologies require pre-selected gene panels, which are typically curated based on existing biological knowledge and prior research hypotheses. While current methods often prioritize capturing cell type information, we argue that an effective gene panel should also capture cell type diversity, cell states, pathway-level information, and minimize redundancy. To address these broader requirements, we developed a gene panel characterization platform that characterizes panels across multiple perspectives, thus allowing us to compare panels comprehensively. Notably, computationally constructed gene panels performed competitively in capturing major cell types when compared to our in-house manually curated panel. However, refined manual curation offered distinct advantages, particularly in capturing minor and rare cell types and exhibited lower information redundancy comparatively. Building on this framework, we integrated these metrics into a deep learning platform, panelScope, leveraging them as a loss function to design holistic gene panels. Using an acute myeloid leukemia (AML) dataset with 42 well-defined cell types and the 5K Xenium panel from 10X Genomics, we demonstrate the utility of our framework in comprehensively characterizing gene panels, enabling the design of tailored panels that address diverse research needs.

bioinformatics↗

bioRxiv2024

A Message Passing Framework for Precise Cell State Identification with scClassify2

In single-cell analysis, the ability to accurately annotate cells is crucial for downstream exploration. To date, a wide range of approaches have been developed for cell annotation, spanning from classic statistical models to the latest large language models. However, most of the current methods focus on annotating distinct cell types and overlook the identification of sequential cell populations such as transitioning cells. Here, we propose a message-passing-neural-network-based cell annotation method, scClassify2, to specifically focus on adjacent cell state identification. By incorporating prior biological knowledge through a novel dual-layer architecture and employing ordinal regression and conditional training to differentiate adjacent cell states, scClassify2 achieves superior performance compared to other state-of-the-art methods. In addition to single-cell RNA-sequencing data, scClassify2 is generalizable to annotation from different platforms including subcellular spatial transcriptomics data. To facilitate ease of use, we provide a web server hosting over 30 human tissues.

bioinformatics↗

bioRxiv2023

The current landscape and emerging challenges of benchmarking single-cell methods

With the rapid development of computational methods for single-cell sequencing data, benchmarking serves as a valuable resource. As the number of benchmarking studies surges, it is timely to assess the current state of the field. We conducted a systematic literature search and assessed 282 papers, including all 130 benchmark-only papers from the search and an additional 152 method development papers containing benchmarking. This collective effort provides the most comprehensive quantitative summary of the current landscape of single-cell benchmarking studies. We examine performances across nine broad categories, including often ignored aspects such as role of datasets, robustness of methods and downstream evaluation. Our analysis highlights challenges such as how to effectively combine knowledge across multiple benchmarking studies and in what ways can the community recognise the risk and prevent benchmarking fatigue. This paper highlights the importance of adopting a community-led research paradigm to tackle these challenges and establish best practice standards.

bioinformatics↗