{"id":3112,"date":"2023-01-17T20:44:50","date_gmt":"2023-01-18T02:44:50","guid":{"rendered":"https:\/\/kermitmurray.com\/msblog\/?page_id=3112"},"modified":"2023-01-17T20:44:50","modified_gmt":"2023-01-18T02:44:50","slug":"biorxiv-bioinformatics","status":"publish","type":"page","link":"https:\/\/kermitmurray.com\/msblog\/links\/journal-feeds\/biochemistry-journal-feeds\/biorxiv\/biorxiv-bioinformatics\/","title":{"rendered":"BioRxiv Bioinformatics"},"content":{"rendered":"\n<div class=\"wp-block-caxton-grid relative\"><div class=\"absolute absolute--fill\"><div class=\"absolute absolute--fill cover bg-center\" style=\"background-color:;background-image:linear-gradient( );\"><\/div><div class=\"absolute absolute--fill\" style=\"background-color:;background-image:linear-gradient( );opacity:1;\"><\/div><\/div><div class=\"relative caxton-columns caxton-grid-block\" style=\"padding-top:0;padding-left:0;padding-bottom:0;padding-right:0;grid-template-columns:repeat(12, 1fr)\" data-tablet-css=\"padding-left:em;padding-right:em;\" data-mobile-css=\"padding-left:em;padding-right:em;\">\n<div class=\"wp-block-caxton-section relative\" style=\"grid-area:span 1\/span 8\"><div class=\"absolute absolute--fill\"><div class=\"absolute absolute--fill cover bg-center\" style=\"background-color:;background-image:linear-gradient( );\"><\/div><div class=\"absolute absolute--fill\" style=\"background-color:;background-image:linear-gradient( );opacity:1;\"><\/div><\/div><div class=\"relative caxton-section-block\" style=\"padding-top:5px;padding-left:5px;padding-bottom:5px;padding-right:5px\" data-mobile-css=\"padding-left:1em;padding-right:1em;\" data-tablet-css=\"padding-left:1em;padding-right:1em;\">\n<p><strong><a href=\"https:\/\/www.biorxiv.org\/alertsrss\" target=\"_blank\" rel=\"noreferrer noopener\">Journal Home<\/a><\/strong><\/p>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-caxton-section relative\" style=\"grid-area:span 1\/span 4\"><div class=\"absolute absolute--fill\"><div class=\"absolute absolute--fill cover bg-center\" style=\"background-color:;background-image:linear-gradient( );\"><\/div><div class=\"absolute absolute--fill\" style=\"background-color:;background-image:linear-gradient( );opacity:1;\"><\/div><\/div><div class=\"relative caxton-section-block\" style=\"padding-top:5px;padding-left:5px;padding-bottom:5px;padding-right:5px\" data-mobile-css=\"padding-left:1em;padding-right:1em;\" data-tablet-css=\"padding-left:1em;padding-right:1em;\">\n<p><strong><a href=\"http:\/\/connect.biorxiv.org\/biorxiv_xml.php?subject=bioinformatics\" target=\"_blank\" rel=\"noreferrer noopener\">RSS<\/a><\/strong><\/p>\n<\/div><\/div>\n<\/div><\/div>\n\n\n<ul class=\"has-dates has-authors has-excerpts wp-block-rss\"><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.04.18.719378v1?rss=1'>GeomMotif: A Benchmark for Arbitrary Geometric Preservation in Protein Generation<\/a><\/div><time datetime=\"2026-04-22T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">April 22, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Strashnov, P., Shevtsov, A., Meshchaninov, V., Kardymon, O., Vetrov, D.<\/span><div class=\"wp-block-rss__item-excerpt\">Motif scaffolding in protein design involves generating complete protein structures while preserving the 3D geometry of designated structural fragments, analogous to image outpainting in computer vision. Current benchmarks focus on functional motifs, leaving general geometric preservation capabilities largely untested. We introduce GeomMotif, a systematic benchmark that evaluates arbitrary structural fragment preservation without requiring functional specificity. We construct 57 benchmark tasks, each containing one or two motifs with up to 7 continuous fragments, by sampling from the Protein Data Bank (PDB) [&hellip;]<\/div><\/li><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.04.19.713528v1?rss=1'>Kernel Matrix Completion with Topological and Spectral Features for Multi-Modal Classification<\/a><\/div><time datetime=\"2026-04-22T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">April 22, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Rinon, E. M., Visaya, M. V., Sambayan, R.<\/span><div class=\"wp-block-rss__item-excerpt\">Kernel methods offer a robust framework for integrating multi-modal datasets into a unified representation, thereby facilitating more comprehensive data interpretation. In the presence of incomplete datasets, multiple kernel learning is employed to enhance the efficiency of data completion and integration. We investigate kernel-based approaches to address the incomplete-data problem with applications to yeast protein data. Biological data such as yeast proteins can be represented through multiple modalities, including gene expression profiles, amino acid sequences, three-dimensional structures, and protein interaction networks. [&hellip;]<\/div><\/li><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.04.21.719858v1?rss=1'>Petabase-scale Papillomavirus Discovery<\/a><\/div><time datetime=\"2026-04-22T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">April 22, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Shen, J., Chikhi, R., Korobeynikov, A., Babaian, A.<\/span><div class=\"wp-block-rss__item-excerpt\">Freely available nucleic acid sequencing databases have accumulated to a vast archive of genetic diversity, in excess of 50 petabase-pairs from tens of millions of experiments. Together, these data constitute a digital survey of Earth&#039;s genome. However, the richness of biological information contained within these repositories remains largely unexplored, in large part owing to the technical challenges of analyzing petabytes of data. Recently, Logan completed the sequence-assembly and compression of 27 million sequencing libraries from the Sequence Read Archive (SRA), [&hellip;]<\/div><\/li><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.04.18.719383v1?rss=1'>BioAutoML-FAST: an automated machine-learning platform for reusable and benchmarked biological sequence models<\/a><\/div><time datetime=\"2026-04-22T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">April 22, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Silva de Almeida, B. L., Bonidia, R., Bole, M., Avila-Santos, A., Stadler, P. F., Nunes da Rocha, U., de Carvalho, A. C. P. L. F.<\/span><div class=\"wp-block-rss__item-excerpt\">The prediction of biological sequence properties has traditionally relied on alignment-based methods that assume evolutionary homology and depend on curated reference databases. This, in turn, limits scalability and sensitivity for large or heterogeneous datasets, remote homologs, short sequences, and rapidly evolving genomic regions. Although Machine-Learning (ML) approaches offer alignment-free alternatives, their broader adoption is limited by: (i) the lack of standardized, externally validated benchmark models across diverse datasets, and (ii) the technical expertise required for feature engineering, model selection, and [&hellip;]<\/div><\/li><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.04.19.719441v1?rss=1'>Spatial-neighbour encoding enables fast RNA 3D structure search<\/a><\/div><time datetime=\"2026-04-22T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">April 22, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Wang, D., Jin, J., Qiao, J., Wei, L., Wu, S., Liu, Q.<\/span><div class=\"wp-block-rss__item-excerpt\">Experimental and predicted RNA three-dimensional structures are expanding rapidly, but RNA structure search still lacks a compact residue-level representation that supports database-scale comparison. Using family-held-out ablations across the currently available experimental RNA structure collection, we found that spatial-neighbour features are markedly more informative for family-level discrimination than conventional backbone and base descriptors. Building on this result, we developed RiboSeek, a search framework based on a 20-letter geometric alphabet (RS-20), an 80-letter structure-and-base composite alphabet (RS-80). Across family-level classification and retrieval [&hellip;]<\/div><\/li><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.04.19.719411v1?rss=1'>A Context-Aware Target Engagement and Pharmacodynamic Biomarker Resource to Accelerate Drug Discovery and Development<\/a><\/div><time datetime=\"2026-04-22T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">April 22, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Yang, Y., Zhao, L., Orouji, S., Zhu, Y., Johnson, R. L., Maxwell, D. S., Mica, I., Russell, K. P., Al-lazikani, B.<\/span><div class=\"wp-block-rss__item-excerpt\">Confirming target engagement in tumor experimental models remains a major challenge in oncology drug development. Pharmacodynamic biomarkers can help address this, but few systematic resources link drug targets to candidate biomarkers. We developed TargetTrace, a comprehensive resource to identify and prioritize pharmacodynamic biomarkers across nine key target classes, including transcription factors\/cofactors, kinases, phosphatases, ubiquitin ligases, deubiquitinases, acetyltransferases, deacetylases, methyltransferases, and demethylases. Biomarker candidates were gathered from curated molecular interaction resources and refined using external annotations to improve accuracy. For enzyme [&hellip;]<\/div><\/li><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.04.16.719066v1?rss=1'>Parametric Physics-Based Synthesis of 3D Fluorescence Organoid Images with Exact Ground Truth for Deep Learning Pipeline Development<\/a><\/div><time datetime=\"2026-04-22T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">April 22, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Bhattiprolu, S.<\/span><div class=\"wp-block-rss__item-excerpt\">Three-dimensional organoid cultures have emerged as powerful models for studying human tissue biology, disease mechanisms, and drug responses. Fluorescence confocal microscopy of organoids generates complex volumetric image data that is increasingly analyzed using deep learning pipelines for cell segmentation, morphometry, and phenotyping. However, training and benchmarking such pipelines requires large annotated datasets, the manual curation of which is prohibitively expensive and time-consuming. Here we present a parametric, physics-based computational framework for generating synthetic 3D fluorescence organoid images with exact ground-truth [&hellip;]<\/div><\/li><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.04.20.719117v1?rss=1'>Comparative benchmarking of single-cell transcriptomes and immune repertoires across technologies<\/a><\/div><time datetime=\"2026-04-22T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">April 22, 2026<\/time> <span class=\"wp-block-rss__item-author\">by King, C., Iqbal, M., Shokati, E., Man Ying Li, C., Li, R., Tomita, Y., Smith, E., Kawecka, J. A., Wang, S., Fenix, K.<\/span><div class=\"wp-block-rss__item-excerpt\">Immune receptor profiling enables tracking of individual T or B cell clones across time and tissues, providing insight into immune responses, cancer, and autoimmunity. When combined with single-cell transcriptomics, it links clonotype identity to cellular function, revealing the diversity and dynamics of immune cell populations. This study presents a head-to-head benchmarking comparison of two single-cell immune profiling technologies: droplet-based microfluidics from 10x Genomics (10x) and combinatorial barcoding from Parse Biosciences (Parse). Using matched human samples from PBMC&#039;s, the analysis evaluates [&hellip;]<\/div><\/li><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.04.19.719454v1?rss=1'>Expanding P-NET, a multi-purpose biologically informed deep learning framework<\/a><\/div><time datetime=\"2026-04-22T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">April 22, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Elmarakeby, H., Glettig, M., Zhou, A., Zhou, C., Tarantino, G., Aprati, T., Van Allen, E., Liu, D.<\/span><div class=\"wp-block-rss__item-excerpt\">We present expanded P-NET, a versatile framework for deep learning in computational biology based on P-NET, leveraging biological pathways for interpretable predictions. Our framework achieves competitive performance in genomic &amp; transcriptomic prediction tasks. We demonstrate its stability and interpretability compared to traditional machine learning models. P-NET 2.0 incorporates gene and pathway information, providing valuable insights into complex biological processes. The framework is publicly available, enabling its application to various computational biology tasks.<\/div><\/li><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.04.16.719017v1?rss=1'>ISPAT-3D: Spatially Varying Conditional Volumetric Network Estimation for 3D Tumor Imaging<\/a><\/div><time datetime=\"2026-04-21T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">April 21, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Bhadury, S., Rao, A.<\/span><div class=\"wp-block-rss__item-excerpt\">The spatial organization of the tumor microenvironment shapes immune function and disease progression, yet existing methods for cell-type interaction networks from multiplexed tissue images operate in two dimensions and ignore spatial autocorrelation. We introduce ISPat-3D (Informed Spatially Aware Patterns in 3D), a hierarchical Bayesian framework that recovers spatially varying, zone-specific interaction networks from 3D multiplexed cancer imaging data. The method partitions the tissue volume into tumor intensity zones, fits an anisotropic Gaussian process per cell type and zone with separate [&hellip;]<\/div><\/li><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.04.16.718997v1?rss=1'>scSketch: Interactive Sketch-based Trajectory Exploration and Pathway-Aware Analysis of Single-Cell Data<\/a><\/div><time datetime=\"2026-04-21T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">April 21, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Temirbek, A., Lekschas, F., Sankaran, K., Colubri, A.<\/span><div class=\"wp-block-rss__item-excerpt\">Interactively exploring gene expression gradients across low-dimensional cell embeddings is central to single-cell RNA sequencing analysis, yet there are not tools that allow users to sketch trajectories and interactively compute pathway-level interpretation. We present scSketch, a tool that enables users to iteratively explore and test trajectory hypotheses in single-cell data while maintaining statistical validity and biological interpretability. Specifically, users apply interactive directional sketching to draw trajectories across embeddings and probe continuous processes such as cellular differentiation and cell state transitions. [&hellip;]<\/div><\/li><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.04.16.719113v1?rss=1'>NETWORK-BASED FUNCTIONAL FRAGILITY REVEALS SYSTEM-LEVEL REORGANIZATION OF THE GUT MICROBIOME IN INFLAMMATORY BOWEL DISEASE<\/a><\/div><time datetime=\"2026-04-21T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">April 21, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Kenavdekar, M. V., Natarajan, E.<\/span><div class=\"wp-block-rss__item-excerpt\">The human gut microbiome plays a critical role in host health, yet its functional organization in disease remains poorly understood. Most studies focus on taxonomic composition or pathway abundance, which fail to capture higher-order interactions governing system-level behavior. Here, we investigated microbiome functional organization in inflammatory bowel disease (IBD), including Crohns disease (CD), ulcerative colitis (UC), and healthy controls (HC), using a network-based framework across 60 metagenomic samples. Functional pathway profiles were used to construct correlation-based interaction networks, followed by [&hellip;]<\/div><\/li><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.04.17.719243v1?rss=1'>Bi-level diversity optimisation for representative protein panel selection<\/a><\/div><time datetime=\"2026-04-21T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">April 21, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Ou, Z., James, K., Charnock, S., Wipat, A.<\/span><div class=\"wp-block-rss__item-excerpt\">Selecting representative subsets from large protein sequence datasets is a common challenge in enzyme discovery and related tasks under limited screening capacity. In practice, candidate panels are often constructed using clustering-based redundancy reduction or manual selection guided by phylogenetic or similarity-network analyses, which do not directly optimise subset diversity and require threshold tuning or expert interpretation. Here, we present a bi-level diversity-optimisation framework for representative protein panel selection implemented using a local search heuristic that iteratively updates panel composition to [&hellip;]<\/div><\/li><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.04.17.719146v1?rss=1'>Automated landmark and semilandmark annotation for wing geometric morphometrics in Diptera using deep learning<\/a><\/div><time datetime=\"2026-04-21T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">April 21, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Nolte, K., Baumbach, J., Kollmannsberger, P., Sauer, F. G., Luehken, R.<\/span><div class=\"wp-block-rss__item-excerpt\">Diptera represent a diverse insect order, including vectors of human and animal pathogens. Their accurate species identification remains a major bottleneck in ecological and epidemiological studies. Morphological identification requires taxonomic expertise, while molecular methods are costly and not universally reliable. Wing geometric morphometrics offers an alternative, but manual landmark annotation is time-consuming and introduces observer bias. We developed ITHILDIN, an automated pipeline for landmark and semilandmark annotation of Diptera wings, combining UNet++ segmentation and an Hourglass landmark prediction model. Using [&hellip;]<\/div><\/li><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.04.17.719144v1?rss=1'>Large-scale analysis of ligand binding mode similarities in the PDB using interaction fingerprints<\/a><\/div><time datetime=\"2026-04-21T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">April 21, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Kunnakkattu, I. R., Choudhary, P., Midlik, A., Fleming, J. R., Balasubramaniyan, B., Sasidharan Nair, S., Velankar, S.<\/span><div class=\"wp-block-rss__item-excerpt\">Three-dimensional structures of protein-ligand complexes are essential for insights into the molecular principles that govern ligand recognition and binding. With more than 180,000 ligand-bound entries in the Protein Data Bank (PDB), representing over two million individual complexes, the volume of available structural data offers unprecedented opportunities for large-scale analysis of interaction patterns. Analysis of interaction patterns across the PDB archive can help discover similarities and differences in the binding modes of ligands, assisting in drug discovery. However, large-scale analysis of [&hellip;]<\/div><\/li><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.04.17.718988v1?rss=1'>Ancestra: A lineage-explicit simulator for benchmarking B-cell receptor repertoire and lineage inference methods<\/a><\/div><time datetime=\"2026-04-21T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">April 21, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Hassanzadeh, R., Abdollahi, N., Kossida, S., Giudicelli, V., Eslahchi, C.<\/span><div class=\"wp-block-rss__item-excerpt\">High-throughput B-cell receptor sequencing has transformed the analysis of adaptive immunity, but benchmarking clonal grouping and lineage reconstruction methods remains limited by the absence of datasets with known evolutionary histories. Here we present Ancestra, a lineage-explicit simulator of B-cell receptor heavy-chain affinity maturation. Ancestra models stochastic V(D)J recombination, context-dependent somatic hypermutation, affinity-based selection and clonal expansion while recording complete parent-child relationships and mutation events. The framework generates BCR heavy-chain sequence datasets together with their corresponding ground-truth lineage trees, enabling direct [&hellip;]<\/div><\/li><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.04.17.719212v1?rss=1'>Pan1c : a pipeline to easily build chromosome-level pangenome graphs<\/a><\/div><time datetime=\"2026-04-21T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">April 21, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Mergez, A., Racoupeau, M., Bardou, P., Linard, B., Legeai, F., Choulet, F., Gaspin, C., Klopp, C.<\/span><div class=\"wp-block-rss__item-excerpt\">The advances of sequencing technologies and the availability of high-quality genome assemblies for many genotypes per species, give the opportunity to improve sequence alignment rate and quality, and the variant calling accuracy by including all genomic variations in a graph reference, called a pangenome graph. Because the process of building and analysing a pangenome graph is still complex, with related software packages under development, there is an important need for releasing user-friendly pipelines for this emerging research area. Pan1C is [&hellip;]<\/div><\/li><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.04.18.719315v1?rss=1'>Foundation cell segmentation models performance on live microscopy and spatial-omics data<\/a><\/div><time datetime=\"2026-04-21T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">April 21, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Miao, Y., Surguladze, N., Lerner, J., Poysungnoen, K., Ariano, K., Li, Y., Zhu, Y., Van Batavia, K., Jepson, J., Van De Klashorst, J., Ni, B. Y. X., Armstrong, A., Rahman, R., Horstmeyer, R., Hickey, J. W.<\/span><div class=\"wp-block-rss__item-excerpt\">Accurate cell segmentation is an essential step for quantitative analysis of biological imaging data. Recent advances in deep learning have led to the development of generalist segmentation models that perform robustly across multiple imaging modalities, including label-free phase contrast, fluorescence cell culture, and multiplexed fluorescence tissue imaging. However, systematic comparisons of these models at the level of downstream biological analysis remain limited. To address this gap, we evaluated several recent segmentation models, including Cellpose cyto3, Cellpose-SAM, SAM, and CellSAM, on [&hellip;]<\/div><\/li><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.04.17.719309v1?rss=1'>Dissecting the coordinated progression of cell states in spatial transcriptomics with CoPro<\/a><\/div><time datetime=\"2026-04-21T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">April 21, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Miao, Z., Qu, Y., Huang, S., Laux, L., Peters, S., Aristel, A., Zhang, Z., Niedernhofer, L. J., McMahon, A., Kim, J., Zhang, N.<\/span><div class=\"wp-block-rss__item-excerpt\">Spatial transcriptomics enables the study of how cells coordinate their molecular states within tissue, providing insight into both normal function and disease processes. A key challenge is to identify gene expression programs that vary continuously across space and are coordinated between cell types. We present CoPro, a computational framework for detecting the spatially coordinated progression of cellular states. CoPro can operate in both supervised and unsupervised modes to identify gene programs that co-vary within or between cell types, and to [&hellip;]<\/div><\/li><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.04.18.719420v1?rss=1'>Novel Parameter-Free and Interpretable Integration of CITE-seq RNA and ADT Profiles via Tensor Decomposition-Based Unsupervised Feature Extraction<\/a><\/div><time datetime=\"2026-04-21T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">April 21, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Taguchi, Y.-h., Turki, T.<\/span><div class=\"wp-block-rss__item-excerpt\">CITE-seq jointly profiles cellular transcripts and surface proteins, but integrating RNA and antibody-derived tags (ADTs) remains challenging because the two modalities differ markedly in dimensionality, sparsity, and noise characteristics. We present a tensor-decomposition-based unsupervised feature extraction framework for the parameter-free integration of CITE-seq data. By constructing a gene x cell x protein tensor and applying HOSVD, this method derives the shared latent representations of genes, cells, and proteins without prior gene filtering or modality-weight tuning. 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