Stefan Canzar

GraphBG detects spatial domains in multi-slice and multi-modal spatial transcriptomics data using spectral graph convolutions and Bayesian modeling, achieving high accuracy and scalability.

This paper benchmarks 26 spatial domain detection methods across real and semi-synthetic spatial transcriptomics data to identify performance drivers and introduce a modular benchmarking framework.

CAMP accelerates metacell partitioning for large single-cell datasets by using coreset-based sampling, achieving scalability and accuracy comparable to existing methods.

The paper develops a coreset-based logistic regression method for scalable single-cell and spatial omics analysis, achieving accurate results with reduced computational demands.

UMIche provides a unified platform for UMI-centric simulation and analysis in bulk and single-cell sequencing to improve molecular quantification accuracy.

mclUMI improves UMI deduplication by using Markov clustering to dynamically remove PCR duplicates, outperforming existing methods in noisy sequencing conditions.

The paper proposes a method to identify a small set of marker genes that optimally separate cell types in single-cell RNA-seq data by considering expression variation within cell types.

The paper investigates how multicopy gene families on the human Y chromosome survive despite lack of recombination, showing that homogenization via palindromes and arrays, along with purifying selection, preserve protein structure essential for fertility.

The paper shows that microRNA activity is essential for early mouse embryos to transition into diapause, with miRNAs regulating gene expression and linking mTOR activity to miRNA biogenesis.

The paper demonstrates that PCR errors significantly affect UMI-based absolute counting in sequencing data and proposes a solution using homotrimeric nucleotide blocks to correct these errors.

This study quantifies the contribution of transcriptional silencing and RNA degradation to heterochromatic silencing in fission yeast, revealing transcriptional efficiency as a new mechanism.

The paper introduces a neural network method for metric multidimensional scaling that scales to large single-cell datasets and provides non-linear embeddings.

The paper introduces j-SNE and j-UMAP for visualizing multimodal single-cell omics data, showing they better align with known cell types and harmonize RNA and protein velocity landscapes.

Specter improves clustering accuracy for large-scale scRNA-seq and multimodal data by using landmarks for linear-time spectral clustering and is sensitive to rare cell types.

McSplicer estimates splice site usage from RNA-seq data using a probabilistic model, achieving more accurate results than existing methods.

BiCoN identifies patient subgroups and functional gene subnetworks by integrating network constraints with biclustering of omics data.

This paper introduces Trajan, a tool for comparing complex single-cell trajectories with branching points, demonstrating its ability to align biological processes and identify reprogramming barriers.

The paper uses a mouse advanced intercross line to identify genetic associations and eQTLs for traits related to human health and disease.