Biology subjects

Canzar, S.

Publications and source records attributed to Canzar, S..

18 recordsLinked to original sources

bioRxiv2026

GraphBG: Fast Bayesian Domain Detection via Spectral Graph Convolutions for Multi-slice and Multi-modal Spatial Transcriptomics

Spatial transcriptomics (ST) technologies enable measurement of gene expression with spatial context, offering unprecedented insight into tissue architecture and cellular microenvironments. A fundamental analysis task is the identification of spatial domains, i.e., contiguous regions with distinct molecular profiles. As ST datasets scale to larger tissue areas, multiple slices, and multiple molecular modalities, there is a growing need for clustering methods that are accurate, scalable, and capable of integrating diverse spatial and molecular signals. We present GraphBG, a unified and scalable framework for spatial domain detection in ST data. GraphBG integrates approximate spectral graph convolutions with a variational Bayesian Gaussian mixture model, enabling robust representation learning and clustering of spatially coherent domains. We extend this core model to support multi-slice analysis (GraphBG-MS) through metacell aggregation, batch correction, and joint clustering, and to multi-modal spatial omics data (GraphBG-MM) via modality-specific graph encodings and kernel canonical correlation analysis. Across diverse real and simulated datasets, GraphBG consistently outperforms existing methods in domain coherence, scalability, and biological interpretability. Notably, it accurately clusters over 370,000 cells from 31 MERFISH tissue slices in just 5 minutes and integrates spatial transcriptomic and proteomic data for improved domain resolution. Applying GraphBG-MS to mouse liver ST data, we show that it captures canonical lobular zonation and disease-specific remodeling, highlighting its ability to reveal biologically meaningful tissue organization.

bioinformatics↗

bioRxiv2026

An explanatory benchmark of spatial domain detection reveals key drivers of method performance

The spatial organization of cells within tissues is critical for understanding biological function and disease, and spatial transcriptomics enables genome-wide mapping of this organization. Numerous computational methods aim to identify spatial domains, yet their performance is often evaluated on limited datasets, leading to conflicting conclusions. Here, we present an explanatory benchmark of 26 spatial domain detection methods across 63 tissue sections from six spatial transcriptomics technologies, supplemented by over 1,000 semi-synthetic datasets that systematically vary resolution, gene panel size, and tissue architecture. By jointly analyzing real and semi-synthetic data across this broad parameter space, our benchmark uncovers systematic performance differences and sources of variability that are obscured in standard evaluations. Although most spatial methods outperform non-spatial baselines, their performance depends strongly on data resolution and cellular heterogeneity. To enable systematic analysis beyond individual methods, we introduce a modular, plug-and-play benchmarking framework that facilitates method refinement and component exchange. Using this framework, an ablation study of neural network-based approaches shows that the choice of preprocessing and clustering often has a larger impact on performance than ar-chitectural novelty alone. Together, these results provide a principled foundation for informed method selection and offer guidance for the development of robust and scalable spatial domain detection tools as spatial transcriptomics technologies continue to advance.

bioinformatics↗

bioRxiv2025

CAMP: Coreset Accelerated Metacell Partitioning enables scalable analysis of single-cell data

Scaling metacell inference to atlas-level single-cell datasets demands algorithms that are both computationally efficient and geometrically faithful. We introduce CAMP (Coreset Accelerated Metacell Partitioning), a metacell framework that preserves the intrinsic structure of the cellular manifold while enabling scalable analysis of millions of cells. CAMP leverages coreset-based sampling to construct a small, weighted subset of representative cells that approximates the full dataset with provable geometric guarantees. This formulation transforms metacell construction into a coreset inference problem, reducing runtime and memory complexity by up to an order of magnitude without loss of accuracy. Through extensive experiments, we show that CAMP produces metacells that are compact, well-separated, and biologically coherent, achieving performance on par with or exceeding existing methods including MetaCell, SuperCell, SEACells, and MetaQ. By combining theoretical efficiency with empirical robustness, CAMP establishes coreset acceleration as a principled foundation for scalable, high-fidelity metacell inference in single-cell transcriptomics.

bioinformatics↗

bioRxiv2025

Coreset-based logistic regression for atlas-scale cell type annotation

Logistic regression provides a versatile and interpretable framework for analyzing single-cell and spatial omics data. It has been shown to outperform more complex models on key association tasks, such as marker gene identification, and on predictive tasks, such as cell type annotation. Logistic regression has also been used effectively for quantifying spatial proximity between cell types and has demonstrated competitive performance in predicting tissue phenotypes. As increasingly large single-cell atlases become publicly available, training logistic regression models on these datasets offers opportunities for building accurate and robust reference models but also poses major computational challenges. Here, we adapt and extend the theory of coresets for logistic regression. Specifically, we compute a previously introduced classification complexity measure using linear programming to identify omics datasets that admit coresets, i.e. random subsets of cells that preserve logistic loss. Our main theoretical finding is that this complexity measure is close to one for PCA-transformed single-cell datasets. Moreover, we prove that logistic loss is preserved under this linear transformation, suggesting a universal sampling scheme that requires only a constant number of cells per cell type to obtain accurate representations of the data. Experiments across several cell atlases demonstrate that these theoretical guarantees translate to accurate and scalable transfer of cell types and spatial niches, outperforming more complex models, including pre-trained language models and graph isomorphism networks.

bioinformatics↗

bioRxiv2025

mclUMI: Markov clustering of unique molecular identifiers enables dynamic removal of PCR duplicates

Molecular quantification in high-throughput sequencing experiments relies on accurate identification and removal of polymerase chain reaction (PCR) duplicates. The use of Unique Molecular Identifiers (UMIs) in sequencing protocols has become a standard approach for distinguishing molecular identities. However, PCR artefacts and sequencing errors in UMIs present a significant challenge for effective UMI collapsing and accurate molecular counting. Current computational strategies for UMI collapsing often exhibit limited flexibility, providing invariable deduplicated counts that inadequately adapt to varying experimental conditions. To address these limitations, we developed mclUMI, a tool employing the Markov clustering algorithm to accurately identify original UMIs and eliminate PCR duplicates. Unlike conventional methods, mclUMI automates the detection of independent communities within UMI graphs by dynamically fine-tuning inflation and expansion parameters, enabling context-dependent merging of UMIs based on their connectivity patterns. Through in silico experiments, we demonstrate that mclUMI generates dynamically adaptable deduplication outcomes tailored to diverse experimental scenarios, particularly best-performing under high sequencing error rates. By integrating connectivity-driven clustering, mclUMI enhances the accuracy of molecular counting in noisy sequencing environments, addressing the rigidity of current UMI deduplication frameworks.

molecular biology↗

bioRxiv2025

UMIche: A platform for robust UMI-centric simulation and analysis in bulk and single-cell sequencing

Unique molecular identifiers (UMIs) have actively been utilised by various RNA sequencing (RNA-seq) protocols and technologies to remove polymerase chain reaction (PCR) duplicates, thus enhancing counting accuracy. However, errors during sequencing processes often compromise the precision of UMI-assisted quantification. To overcome this, various computational methods have been proposed for UMI error correction. Despite these advancements, the absence of a unified benchmarking and validation framework for UMI deduplication methods hinders the systematic evaluation and optimisation of these methods. Here, we present UMIche, an open-source, UMI-centric computational platform designed to improve molecular quantification by providing a systematic, integrative, and extensible framework for UMI analysis. Additionally, it supports the development of more effective UMI deduplication strategies. We show that through its integration of a broad spectrum of UMI deduplication methods and computational workflows, UMIche significantly advances the accuracy of molecular quantification and facilitates the generation of high-fidelity gene expression profiles.

molecular biology↗

bioRxiv2025

Optimal marker genes for c-separated cell types

The identification of cell types in single-cell RNA-seq studies relies on the distinct expression signature of marker genes. A small set of target genes is also needed to design probes for targeted spatial transcriptomics experiments and to target proteins in single-cell spatial proteomics or for cell sorting. While traditional approaches have relied on testing one gene at a time for differential expression between a given cell type and the rest, more recent methods have highlighted the benefits of a joint selection of markers that together distinguish all pairs of cell types simultaneously. These combinatorial methods differ mostly in the notion of discrimination between cell types, using Euclidean or Manhattan distance in the dimensions of the selected marker genes, or the difference in the fraction of cells expressing a given marker gene. The resulting combinatorial optimization problems then seeks to identify a small set of genes that yield discrimination above a given threshold between all pairs of cell types. However, existing methods either consider all pairs of individual cells which becomes intractable even for medium-sized datasets, or ignore intra-cell type expression variation entirely by collapsing all cells of a given type to a single representative. Here we address these limitations and propose to find a small set of genes such that cell types are c-separated in the selected dimensions, a notion introduced previously in learning a mixture of Gaussians. To this end, we formulate a linear program that naturally takes into account expression variation within cell types without including each pair of individual cells in the model, leading to a highly stable set of marker genes that allow to accurately discriminate between cell types and that can be computed to optimality efficiently.

bioinformatics↗

bioRxiv2024

Transcript Isoform Diversity of Y Chromosome Ampliconic Genes of Great Apes Uncovered Using Long Reads and Telomere-to-Telomere Reference Genome Assemblies

The mammalian Y chromosome is an evolutionary paradox: it cannot recombine across most of its length and has lost many genes, yet it retains multicopy gene families required for sperm production. How these genes survive has been difficult to resolve because their near-identical copies collapse in most genome assemblies and are difficult to analyze accurately with short RNA reads. Using telomere-to-telomere assemblies of seven ape genomes together with long-read testis transcriptomes, protein-structure predictions, and selection tests, we show that these gene families vary extensively but encode structurally conserved proteins. We demonstrate that, while copy number varies across and within species, palindromes and tandem arrays are equally effective in homogenizing copies via gene conversion--a result we could obtain only with complete assemblies. We detect extensive transcript variation arising from alternative splicing and inter-copy sequence differences. Yet this variation collapses into only a few predicted protein-structure clusters per family. Furthermore, we find evidence of purifying selection acting on gene families (and domains) encoding ordered protein structures--the majority of multicopy families on the human Y. Thus, copy number and (to a lesser degree) sequence, and transcript structure and sequence all vary, whereas protein structure, which is important for spermatogenic function, is held nearly constant. Together, our results show that palindrome- and array-mediated homogenization and purifying selection aimed at preserving protein structure jointly enable the survival of fertility-related genes on the non-recombining Y chromosome. These results will inform diagnostic approaches to human Y-linked infertility and studies of the conservation of non-human great apes.

genomics↗

bioRxiv2023

Combinatorial microRNA activity is essential for the transition of pluripotent cells from proliferation into dormancy

Dormancy is a key feature of stem cell function in adult tissues as well as embryonic cells in the context of diapause. The establishment of dormancy is an active process that involves extensive transcriptional, epigenetic, and metabolic rewiring1,2. How these processes are coordinated to successfully transition cells to the resting dormant state remains unclear. Here we show that microRNA activity, which is otherwise dispensable for pre-implantation development, is essential for the adaptation of early mouse embryos to the dormant state of diapause. In particular, the pluripotent epiblast depends on miRNA activity, the absence of which results in loss of pluripotent cells. Through integration of high-sensitivity small RNA expression profiling of individual embryos and protein expression of miRNA targets with public data of protein-protein interactions, we constructed the miRNA-mediated regulatory network of mouse early embryos specific to diapause. We find that individual miRNAs contribute to the combinatorial regulation by the network and the perturbation of the network compromises embryo survival in diapause. Without miRNAs, nuclear and cytoplasmic bodies show aberrant expression, concurrent with splicing defects. We identified the nutrient-sensitive transcription factor TFE3 as an upstream regulator of diapause-specific miRNAs, linking cytoplasmic mTOR activity to nuclear miRNA biogenesis. Our results place miRNAs as a critical regulatory layer for the molecular rewiring of early embryos to establish dormancy.

molecular biology↗

bioRxiv2023

Correcting PCR amplification errors in unique molecular identifiers to generate absolute numbers of sequencing molecules.

Unique Molecular Identifiers (UMIs) are random oligonucleotide sequences that remove PCR amplification biases. However, the impact that PCR associated sequencing errors have on the accuracy of generating absolute counts of RNA molecules is underappreciated. We show that PCR errors are the main source of inaccuracy in both bulk and single-cell sequencing data, and synthesizing UMIs using homotrimeric nucleotide blocks provides an error correcting solution, that allows absolute counting of sequenced molecules.

genomics↗

bioRxiv2021

Ccr4-Not complex reduces transcription efficiency in heterochromatin

Heterochromatic silencing is thought to occur through a combination of transcriptional silencing and RNA degradation, but the relative contribution of each pathway is not known. In this study we analyzed RNA Polymerase II (RNA Pol II) occupancy and levels of nascent and steady-state RNA in different strains of fission yeast, in order to quantify the contribution of each pathway to heterochromatic silencing. We found that transcriptional silencing consists of two components, reduced RNA Pol II accessibility and, unexpectedly, reduced transcriptional efficiency. Heterochromatic loci showed lower transcriptional output compared to euchromatic loci, despite the presence of comparable amounts of RNA Pol II in both types of regions. We determined that the Ccr4-Not complex and H3K9 methylation are required for reduced transcriptional efficiency in heterochromatin and that a subset of heterochromatic RNA is degraded more rapidly than euchromatic RNA. Finally, we quantified the contribution of different chromatin modifiers, RNAi and RNA degradation to each silencing pathway. Our data show that several pathways contribute to heterochromatic silencing in a locus-specific manner and reveal transcriptional efficiency as a new mechanism of silencing.

genomics↗

bioRxiv2021

Metric Multidimensional Scaling for Large Single-Cell Data Sets using Neural Networks

Metric multidimensional scaling is one of the classical methods for embedding data into low-dimensional Euclidean space. It creates the low-dimensional embedding by approximately preserving the pairwise distances between the input points. However, current state-of-the-art approaches only scale to a few thousand data points. For larger data sets such as those occurring in single-cell RNA sequencing experiments, the running time becomes prohibitively large and thus alternative methods such as PCA are widely used instead. Here, we propose a neural network based approach for solving the metric multidimensional scaling problem that is orders of magnitude faster than previous state-of-the-art approaches, and hence scales to data sets with up to a few million cells. At the same time, it provides a non-linear mapping between high- and low-dimensional space that can place previously unseen cells in the same embedding.

bioinformatics↗

bioRxiv2021

A generalization of t-SNE and UMAP to single-cell multimodal omics

Emerging single-cell technologies profile multiple types of molecules within individual cells. A fundamental step in the analysis of the produced high-dimensional data is their visualization using dimensionality reduction techniques such as t-SNE and UMAP. We introduce j-SNE and j-UMAP as their natural generalizations to the joint visualization of multimodal omics data. Our approach automatically learns the relative contribution of each modality to a concise representation of cellular identity that promotes discriminative features but suppresses noise. On eight datasets, j-SNE and j-UMAP produce unified embeddings that better agree with known cell types and that harmonize RNA and protein velocity landscapes. j-SNE and j-UMAP are available in the JVis Python package.

bioinformatics↗

bioRxiv2020

McSplicer: a probabilistic model for estimating splice site usage from RNA-seq data

Alternative splicing removes intronic sequences from transcripts in alternative ways to produce different forms (isoforms) of mature mRNA. The composition of expressed transcripts and their alternative forms give specific functionalities to cells in a particular condition or developmental stage. In addition, a large fraction of human disease mutations affect splicing and lead to aberrant mRNA and protein products. Current methods that interrogate the transcriptome based on RNA-seq either suffer from short read length when trying to infer full-length transcripts, or are restricted to predefined units of alternative splicing that they quantify from local read evidence. Instead of attempting to quantify individual outcomes of the splicing process such as local splicing events or full-length transcripts, we propose to quantify alternative splicing using a simplified probabilistic model of the underlying splicing process. Our model is based on the usage of individual splice sites and can generate arbitrarily complex types of splicing patterns. In our method, McSplicer, we estimate the parameters of our model using all read data at once and we demonstrate in our experiments that this yields more accurate estimates compared to competing methods. Our model is able to describe multiple effects of splicing mutations using few, easy to interpret parameters, as we illustrate in an experiment on RNA-seq data from autism spectrum disorder patients. McSplicer is implemented in Python and available as open-source at https://github.com/canzarlab/McSplicer.

bioinformatics↗

bioRxiv2020

Linear-time cluster ensembles of large-scale single-cell RNA-seq and multimodal data

A fundamental task in single-cell RNA-seq (scRNA-seq) analysis is the identification of transcriptionally distinct groups of cells. Numerous methods have been proposed for this problem, with a recent focus on methods for the cluster analysis of ultra-large scRNA-seq data sets produced by droplet-based sequencing technologies. Most existing methods rely on a sampling step to bridge the gap between algorithm scalability and volume of the data. Ignoring large parts of the data, however, often yields inaccurate groupings of cells and risks overlooking rare cell types. We propose method Specter that adopts and extends recent algorithmic advances in (fast) spectral clustering. In contrast to methods that cluster a (random) subsample of the data, we adopt the idea of landmarks that are used to create a sparse representation of the full data from which a spectral embedding can then be computed in linear time. We exploit Specters speed in a cluster ensemble scheme that achieves a substantial improvement in accuracy over existing methods and that is sensitive to rare cell types. Its linear time complexity allows Specter to scale to millions of cells and leads to fast computation times in practice. Furthermore, on CITE-seq data that simultaneously measures gene and protein marker expression we demonstrate that Specter is able to utilize multimodal omics measurements to resolve subtle transcriptomic differences between subpopulations of cells. Specter is open source and available at https://github.com/canzarlab/Specter.

bioinformatics↗

bioRxiv2020

BiCoN: Network-constrained biclustering of patients and omics data

MotivationUnsupervised learning approaches are frequently employed to identify patient subgroups and biomarkers such as disease-associated genes. Thus, clustering and biclustering are powerful techniques often used with expression data, but are usually not suitable to unravel molecular mechanisms along with patient subgroups. To alleviate this, we developed the network-constrained biclustering approach BiCoN (Biclustering Constrained by Networks) which (i) restricts biclusters to functionally related genes connected in molecular interaction networks and (ii) maximizes the difference in gene expression between two subgroups of patients. ResultsOur analyses of non-small cell lung and breast cancer gene expression data demonstrate that BiCoN clusters patients in agreement with known cancer subtypes while discovering gene subnetworks pointing to functional differences between these subtypes. Furthermore, we show that BiCoN is robust to noise and batch effects and can distinguish between high and low load of tumor-infiltrating leukocytes while identifying subnetworks related to immune cell function. In summary, BiCoN is a powerful new systems medicine tool to stratify patients while elucidating the responsible disease mechanism. AvailabilityPyPI package: https://pypi.org/project/bicon Web interface: https://exbio.wzw.tum.de/bicon Contactolga.lazareva@tum.de Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2019

Dynamic pseudo-time warping of complex single-cell trajectories

Single-cell RNA sequencing enables the construction of trajectories describing the dynamic changes in gene expression underlying biological processes such as cell differentiation and development. The comparison of single-cell trajectories under two distinct conditions can illuminate the differences and similarities between the two and can thus be a powerful tool. Recently developed methods for the comparison of trajectories rely on the concept of dynamic time warping (dtw), which was originally proposed for the comparison of two time series. Consequently, these methods are restricted to simple, linear trajectories. Here, we adopt and theoretically link arboreal matchings to dtw and propose an algorithm to compare complex trajectories that more realistically contain branching points that divert cells into different fates. We implement a suite of exact and heuristic algorithms suitable for the comparison of trajectories of different characteristics in our tool Trajan. Trajan automatically pairs similar biological processes between conditions and aligns them in a globally consistent manner. In an alignment of singlecell trajectories describing human muscle differentiation and myogenic reprogramming, Trajan identifies and aligns the core paths without prior information. From Trajans alignment, we are able to reproduce recently reported barriers to reprogramming. In a perturbation experiment, we demonstrate the benefits in terms of robustness and accuracy of our model which compares entire trajectories at once, as opposed to a pairwise application of dtw. Trajan is available at https://github.com/canzarlab/Trajan.

bioinformatics↗

bioRxiv2017

Genome wide association study of behavioral, physiological and gene expression traits in a multigenerational mouse intercross

The LG/J x SM/J advanced intercross line of mice (LG x SM AIL) is a multigenerational outbred population. High minor allele frequencies, a simple genetic background, and the fully sequenced LG and SM genomes make it a powerful population for genome-wide association studies. Here we use 1,063 AIL mice to identify 126 significant associations for 50 traits relevant to human health and disease. We also identify thousands of cis- and trans-eQTLs in the hippocampus, striatum, and prefrontal cortex of [~]200 mice. We replicate an association between locomotor activity and Csmd1, which we identified in an earlier generation of this AIL, and show that Csmd1 mutant mice recapitulate the locomotor phenotype. Our results demonstrate the utility of the LG x SM AIL as a mapping population, identify numerous novel associations, and shed light on the genetic architecture of mammalian behavior.

genetics↗