Biology subjects

Tjaernberg, A.

Publications and source records attributed to Tjaernberg, A..

4 recordsLinked to original sources

bioRxiv2026

scVIP: personalized modeling of single-cell transcriptomes for developmental and disease phenotypes

Single-cell transcriptomics resolves cellular heterogeneity within individuals, but connecting molecular states to individual-level phenotypes requires frameworks that explicitly bridge these scales. We present scVIP, a generative model that links gene expression, cell-type composition, and phenotypic measurements within a single probabilistic model, which enables accurate phenotype prediction and interpretable trajectory inference. A cell-type-aware multi-instance learning architecture learns donor embeddings that capture progression while localizing phenotype-associated signals to specific cell populations. Applied across four settings, scVIP accurately predicts cortical developmental age (Pearson r = 0.95), characterizes Huntingtons disease progression (concordance correlation coefficient = 0.90), integrates two Alzheimers disease cohorts recovering disease-relevant microglial and astrocytic programs, and distinguishes healthy from ACPA-positive individuals and non-progressors from early RA individuals, identifying inflammatory T cell programs associated with disease. scVIP enables principled analysis of how cellular states collectively shape organism-level phenotypes across development and disease.

bioinformatics↗

bioRxiv2026

MapMyCells: High-performance mapping of unlabeled cell-by-gene data to reference brain taxonomies

Single-cell mapping methods convert raw, heterogeneous single-cell datasets into interpretable and comparable representations of biological identity. As reference cell-type taxonomies mature, mapping new datasets to shared references has become a central strategy for enabling cross-study integration, reproducible annotation, and cumulative biological knowledge. Here we present MapMyCells, an open-source framework designed to align diverse single-cell omics datasets to hierarchical reference taxonomies with minimal preprocessing. MapMyCells provides out-of-the-box support for an expanding set of high-quality brain cell-type references generated by the Allen Institute for Brain Science, the BRAIN Initiative, and the Seattle Alzheimers Disease Brain Cell Atlas, including whole-brain mouse and human atlases, aging and Alzheimers disease cohorts, and a cross-species consensus taxonomy initially focused on the basal ganglia. MapMyCells enables efficient mapping of hundreds of thousands of cells on standard workstations without specialized hardware, providing a deterministic, scalable, and modality-agnostic approach that is robust across species and molecular assays. The framework produces interpretable confidence metrics and quantitative summaries of mapping performance, allowing users to evaluate assignment precision and accuracy. We demonstrate the mapping of unlabeled transcriptomic, epigenomic, and spatial datasets to reference taxonomies and describe a general workflow for preparing arbitrary hierarchical taxonomies for reference-based mapping. As the ecosystem of single-cell reference atlases expands, MapMyCells offers a practical and reproducible solution for community-scale cell-type annotation and cross-dataset integration, supporting the development of unified and extensible brain cell atlases.

bioinformatics↗

bioRxiv2025

Genome-wide expression gradient estimation based on local pseudotime in single cell RNA sequencing

Single cell genomics measures the internal state of individual cells and makes it possible to describe biological phenomena such as cell type heterogeneity, developmental progression, and dynamic differentiation using computational methods. A prominent approach to facilitate downstream analysis, for large collection of cells, is to connect individual cells by a k-Nearest Neighbor Graph (kNN-G). The kNN-G is among other things used by methods that derive pseudotime, a temporal ordering of cells. However, pseudotime methods require knowledge of initial states of the trajectories created. Alternatives have been invented that estimate gene rate of transcription e.g. by RNA velocity but these methods dont generalize to all individual genes for estimating transcriptional rate of change along pseudotime. While these methods can derive pseudo progression de novo by extrapolating transcription velocity vectors they are limited to subsets of genes where intronic reads are captured with sufficient magnitude. Here we present pseudovelocity, an alternative method for calculating a local rate of change in transcription based on the kNN-G and diffusion-based pseudotime, to derive RNA velocity estimates for individual genes, and demonstrate how it improves the gene-based estimation of RNA velocity for downstream analysis. This package can be accessed here: https://gitlab.com/Xparx/pseudovelocity.

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bioRxiv2020

Optimal tuning of weighted kNN- and diffusion-based methods for denoising single cell genomics data

The analysis of single-cell genomics data presents several statistical challenges, and extensive efforts have been made to produce methods for the analysis of this data that impute missing values, address sampling issues and quantify and correct for noise. In spite of such efforts, no consensus on best practices has been established and all current approaches vary substantially based on the available data and empirical tests. The k-Nearest Neighbor Graph (kNN-G) is often used to infer the identities of, and relationships between, cells and is the basis of many widely used dimensionality-reduction and projection methods. The kNN-G has also been the basis for imputation methods using, e.g., neighbor averaging and graph diffusion. However, due to the lack of an agreed-upon optimal objective function for choosing hyperparameters, these methods tend to oversmooth data, thereby resulting in a loss of information with regard to cell identity and the specific gene-to-gene patterns underlying regulatory mechanisms. In this paper, we investigate the tuning of kNN- and diffusion-based denoising methods with a novel non-stochastic method for optimally preserving biologically relevant informative variance in single-cell data. The framework, Denoising Expression data with a Weighted Affinity Kernel and Self-Supervision (DEWAKSS), uses a self-supervised technique to tune its parameters. We demonstrate that denoising with optimal parameters selected by our objective function (i) is robust to preprocessing methods using data from established benchmarks, (ii) disentangles cellular identity and maintains robust clusters over dimension-reduction methods, (iii) maintains variance along several expression dimensions, unlike previous heuristic-based methods that tend to oversmooth data variance, and (iv) rarely involves diffusion but rather uses a fixed weighted kNN graph for denoising. Together, these findings provide a new understanding of kNN- and diffusion-based denoising methods and serve as a foundation for future research. Code and example data for DEWAKSS is available at https://gitlab.com/Xparx/dewakss/-/tree/Tjarnberg2020branch.

bioinformatics↗