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Statistical signature of subtle behavioral changes in large-scale assays

PLoS Computational Biology 2025年第4 April期21卷 e1012990页

作者： Blanc, Alexandre Laurent, François Barbier-Chebbah, Alex Van Assel, Hugues Cocanougher, Benjamin T. Jones, Benjamin M.W. Hague, Peter Zlatic, Marta Chikhi, Rayan Vestergaard, Christian L. Jovanic, Tihana Masson, Jean-Baptiste Barré, Chloé Institut Pasteur Université Paris Cité CNRS UMR 3751 Decision and Bayesian Computation Paris France Epiméthée INRIA Paris France Institut Pasteur Université Paris Cité Bioinformatics and Biostatistics Hub Paris France École Normale Supérieure de Lyon UMPA Lyon France University of Cambridge Department of Zoology Cambridge United Kingdom MRC Laboratory of Molecular Biology Neurobiology Division Cambridge United Kingdom Janelia Research Campus Howard Hughes Medical Institute AshburnVA United States G5 Sequence Bioinformatics Department of Computational Biology Institut Pasteur Paris France Institut des Neurosciences Paris-Saclay Université Paris-Saclay Centre National de la Recherche Scientifique UMR 9197 Saclay France

The central nervous system can generate various behaviors, including motor responses, which we can observe through video recordings. Recent advances in gene manipulation, automated behavioral acquisition at scale, and machine learning enable us to causally link behaviors to their underlying neural mechanisms. Moreover, in some animals, such as the Drosophila melanogaster larva, this mapping is possible at the unprecedented scale of single neurons, allowing us to identify the neural microcircuits generating particular behaviors. These high-throughput screening efforts, linking the activation or suppression of specific neurons to behavioral patterns in millions of animals, provide a rich dataset to explore the diversity of nervous system responses to the same stimuli. However, important challenges remain in identifying subtle behaviors, including immediate and delayed responses to neural activation or suppression, and understanding these behaviors on a large scale. We here introduce several statistically robust methods for analyzing behavioral data in response to these challenges: 1) A generative physical model that regularizes the inference of larval shapes across the entire dataset. 2) An unsupervised kernel-based method for statistical testing in learned behavioral spaces aimed at detecting subtle deviations in behavior. 3) A generative model for larval behavioral sequences, providing a benchmark for identifying higher-order behavioral changes. 4) A comprehensive analysis technique using suffix trees to categorize genetic lines into clusters based on common action sequences. We showcase these methodologies through a behavioral screen focused on responses to an air puff, analyzing data from 280 716 larvae across 569 genetic lines. © 2025 Blanc et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and sour

关键词： Neurons

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Limited impact of the siRNA pathway on transposable element expression in Aedes aegypti

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BMC biology 2025年第1期23卷 130页

作者： Alexander Bergman Anna B Crist Hélène Lopez-Maestre Hervé Blanc Mauro Castelló-Sanjuán Lionel Frangeul Hugo Varet Josquin Daron Sarah H Merkling Maria-Carla Saleh Louis Lambrechts Institut Pasteur Université Paris Cité CNRS UMR2000 Insect-Virus Interactions Unit Paris France. alexander.bergman@pasteur.fr. Institut Pasteur Université Paris Cité CNRS UMR2000 Insect-Virus Interactions Unit Paris France. Institut Pasteur Université Paris Cité Bioinformatics and Biostatistics Hub Paris France. Institut Pasteur Université Paris Cité Viruses and RNA Interference Unit Paris France. Institut Pasteur Université Paris Cité CNRS UMR2000 Insect-Virus Interactions Unit Paris France. louis.lambrechts@pasteur.fr.

BACKGROUND:Transposable elements (TEs) are DNA sequences that can change their position within a genome. In insects, small RNA pathways are central to the transcriptional and post-transcriptional regulation of TE expression. The Piwi-interacting RNA (piRNA) pathway is particularly important in germline tissues, where it silences TE transcripts via small RNAs of 24-30 nucleotides (nt) in length produced from genomic precursor transcripts as well as through a "ping-pong" amplification cycle. The small interfering RNA (siRNA) pathway helps restrict TE expression in somatic tissues via 21nt small RNAs produced from double-stranded RNA by the endonuclease Dicer2, which guide an RNA-induced silencing complex to degrade complementary RNAs. However, much of this knowledge comes from studies of the model insect Drosophila melanogaster. In the mosquito Aedes aegypti, a medically significant vector species, the siRNA pathway has mainly been investigated in connection with its antiviral role, leaving open whether it also regulates TE expression. RESULTS:We investigated the expression of TEs and small RNAs in both somatic and gonadal tissues of a Dicer2 mutant line of Ae. aegypti and its wild-type counterpart. Our results show a modified pattern of TE expression and a decrease in TE-derived 21nt RNAs in the Dicer2 mutant, but no major shift of TE transcript abundance. The lack of a functional siRNA pathway also causes perturbations in piRNA ping-pong signatures and the expression of certain piRNA-associated genes, but without clear evidence for compensation by increased piRNA pathway activity. CONCLUSIONS:The mosquito Ae. aegypti produces siRNAs derived from TEs but these lack a critical role in the regulation of TE expression both in somatic and in gonadal tissues.

关键词： Aedes aegypti Dicer2 Mosquito Transposable element piRNA pathway siRNA pathway

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STATISTICAL DESIGN AND RATIONALE OF THE BIOMARKERS FOR EVALUATING SPINE TREATMENTS (BEST) TRIAL

arXiv

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arXiv 2025年

作者： Sperger, John Kidwell, Kelley M. Mauck, Matthew C. Zhao, Beibo Anstrom, Kevin J. Batorsky, Anna Carey, Timothy S. Clauw, Daniel J. Freeman, Nikki L.B. Greco, Carol M. Ivanova, Anastasia Berkeley, Sara Jones McLean, Samuel A. Psioda, Matthew A. Rowland, Bryce Sowa, Gwendolyn A. Wasan, Ajay D. Zitovsky, Joshua P. Kosorok, Michael R. Department of Biostatistics Gillings School of Global Public Health University of North Carolina Chapel Hill United States Department of Biostatistics School of Public Health University of Michigan United States Department of Anesthesiology University of North Carolina Chapel Hill United States Collaborative Studies Coordinating Center Department of Biostatistics Gillings School of Global Public Health University of North Carolina Chapel Hill United States Department of Medicine University of North Carolina Chapel Hill United States Department of Anesthesiology Chronic Pain & Fatigue Research Center University of Michigan Ann Arbor United States Department of Biostatistics and Bioinformatics Duke University United States Department of Psychiatry University of Pittsburgh School of Medicine United States Department of Epidemiology Gillings School of Global Public Health University of North Carolina Chapel Hill United States Department of Psychiatry University of North Carolina Chapel Hill United States Statistics and Data Science Innovation Hub Biostatistics GSK Department of Physical Medicine and Rehabilitation University of Pittsburgh School of Medicine United States Departments of Anesthesiology and Perioperative Medicine and Psychiatry University of Pittsburgh School of Medicine United States Amazon Postdoctoral Scientist United States Ferguson Laboratory for Orthopaedic and Spine Research Bethel Musculoskeletal Research Center Department of Orthopaedic Surgery University of Pittsburgh School of Medicine United States Department of Bioengineering University of Pittsburgh Swanson School of Engineering United States Clinical and Translational Science Institute University of Pittsburgh United States Carolina Pragmatic Trials Laboratory North Carolina Translational and Clinical Sciences Institute UNC School of Medicine United States Duke Clinical Research Institute United States

Background/Aims: Chronic low back pain (cLBP) is a widely prevalent condition with potentially severe impacts on day-to-day functioning and quality of life. A variety of evidence-based treatment modalities exist, but all approaches have modest average treatment effects—potentially due to individual variation in treatment response and the diverse etiologies of cLBP. The Biomarkers for Evaluating Spine Treatments (BEST) trial was designed to investigate the potential for using patient features including biomarkers and phenotypic measurements to inform cLBP treatment. Methods: We provide an overview of the BEST trial and detail the statistical rationale for key study design decisions. We discuss the salient features of the scientific and clinical context of cLBP treatment that motivated the study design and planned analysis, describe modifications to existing methods that were needed because of the setting, and present simulation studies that were used to inform the study design. Results: The BEST trial was designed as a multi-site sequential, multiple-assignment randomized trial (SMART) of four cLBP treatment modalities. The primary objective was to inform a precision medicine approach to the treatment of cLBP by estimating the optimal treatment or combination of treatments based on patient features and individual patient’s response to the initial treatment. The primary analysis includes estimating an interpretable dynamic treatment regime for recommending treatments based on patient features and investigating the subgroups that respond best to each one of the treatment modalities. The trial aimed to enroll 630 protocol completers. Broad eligibility criteria were used to improve the generalizability of study results. Notably, participants could be excluded from one of the four interventions and still be eligible for the study. We modified the minimization method for reducing covariate imbalance to allow for these exclusions. Conclusion: The BEST trial may pave the way

关键词： Patient treatment

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Enhanced identification of Morganella spp. using MALDI-TOF mass spectrometry

Journal of Mass Spectrometry and Advances in the Clinical La...

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Journal of Mass Spectrometry and Advances in the Clinical Lab 2025年 37卷 9-13页

作者： Duque, Mathilde Emeraud, Cécile Bonnin, Rémy A. Giai-Gianetto, Quentin Dortet, Laurent Godmer, Alexandre Team “Resist” UMR1184 “Immunology of Viral Auto-Immune Hematological and Bacterial diseases (IMVA-HB)” INSERM Université Paris-Saclay CEA LabEx LERMIT Faculty of Medicine Le Kremlin-Bicêtre France Bacteriology-Hygiene Unit Bicêtre Hospital Assistance Publique-Hôpitaux de Paris Le Kremlin-Bicêtre France Associated French National Reference Center for Antibiotic Resistance: Carbapenemase-Producing Enterobacteriaceae Le Kremlin-Bicêtre France Institut Pasteur Université Paris Cité Bioinformatics and Biostatistics HUB Paris France Institut Pasteur Université Paris Cité Proteomics Platform Mass Spectrometry for Biology Unit UAR CNRS 2024 Paris France U1135 Centre d'Immunologie et des Maladies Infectieuses (Cimi-Paris) Sorbonne Université Paris France Bacteriology Deparment Saint-Antoine Hospital AP-HP. Sorbonne Université (Assistance Publique Hôpitaux de Paris) Paris France

Introduction: The genus Morganella, including clinically isolated species M. sibonii and M. morganii, has a still underexplored role in clinical microbiology. Despite the clinical relevance of Morganella spp., current MALDI-TOF commercial systems fail to differentiate these species. Whole genome sequencing (WGS) remains the most effective method to distinguish species. However, this method is not adapted for routine lab workflow. Enhancing MALDI-TOF's accuracy could make it a rapid and effective approach for distinguishing Morganella species in routine laboratory diagnostics. Objectives: This study aims to improve the performance of MALDI-TOF for identifying Morganella spp. using WGS as the gold-standard reference method. Methods: We applied Machine Learning (ML) algorithms to a collection of 235 clinicial Morganella spp. strains to develop an optimized identification model. Whole genome sequencing was used to characterize these strains and perform phylogenetic analysis, categorizing 209 strains as M. morganii and 26 as M. sibonii. Results: The ML-based classifiers showed improved identification accuracy (44 of the 160 designed with accuracy at 1). Also, MS analysis identified 11 peaks able to discriminate between M. morganii and M. sibonii. Conclusion: Through development of a publicly-available online ML-based classifier, this study has improved the capacity of MALDI-TOF for distinguishing Morganella spp., providing a reliable, user-friendly solution suited to routine clinical diagnostics and supporting a better understanding of the roles of M. morganii and M. sibonii in human pathology. © 2025

关键词： α-HCCA Alpha-Cyano-4-Hydroxycinnamic Acid blaDHA Beta-Lactamase DHA-type CE-IVD Conformité Européenne - In Vitro Diagnostic Kappa Cohen’s Kappa Coefficient KPC Klebsiella Pneumoniae Carbapenemase, M . morganii Morganella morganii M. sibonii Morganella sibonii MALDI-TOF MS Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry m / z Mass-to-Charge Ratio mda Mean Decrease Accuracy ML Machine Learning MS Mass Spectrometry MSclassifR Machine Learning Classifier R Package (for mass spectra) multinom Multinomial Logistic Regression NDM New Delhi Metallo-beta-lactamase NDM-1 NDM-5, NDM-7, Variants of New Delhi Metallo-beta-lactamase nnet Single Hidden Layer Neural Network OXA Oxacillinase RF Random Forest RFE-RF Recursive Feature Elimination with Random Forest SelectionVarStat Variable Selection Statistic SVM Support Vector Machine T3SS Type III Secretion System T6SS Type VI Secretion System tetD Tetracycline Resistance Gene D-like VIM Verona Integron-encoded Metallo-beta-lactamase WGS Whole Genome Sequencing

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Antibody-secreting cell repertoires hold high-affinity anti-rocuronium specificities that can induce anaphylaxis in vivo

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Journal of Allergy and Clinical Immunology 2025年第5期155卷 1557-1574页

作者： Dejoux, Alice Zhu, Qianqian Woolfe, Adam Godon, Ophélie Ellouze, Sami Mottet, Guillaume Castrillon, Carlos Gillis, Caitlin Pecalvel, Cyprien Ganneau, Christelle Iannascoli, Bruno Lemoine, Frédéric Saul, Frederick England, Patrick Reber, Laurent L. Gouel-Chéron, Aurélie de Chaisemartin, Luc Haouz, Ahmed Millot, Gaël A. Bay, Sylvie Gérard, Annabelle Jönsson, Friederike Chollet-Martin, Sylvie Bruhns, Pierre Institut Pasteur Université Paris Cité Institut National de la Santé et de la Recherche Médicale (INSERM) UMR1222 Antibodies in Therapy and Pathology Paris France Collège Doctoral Sorbonne Université Paris France Université Paris-Saclay INSERM Inflammation Microbiome Immunosurveillance Orsay France HiFiBiO Therapeutics SAS Paris France Toulouse Institute for Infectious and Inflammatory Diseases (Infinity) INSERM UMR1291 Centre National de la Recherche Scientifique (CNRS) UMR5051 University Toulouse III Toulouse France Institut Pasteur Université Paris Cité CNRS UMR3523 Chimie des Biomolécules Paris France Institut Pasteur Université Paris Cité Bioinformatics and Biostatistics Hub Paris France Institut Pasteur Université Paris Cité CNRS UMR3528 Plate-forme Cristallographie-C2RT Paris France Institut Pasteur Université Paris Cité CNRS UMR3528 Molecular Biophysics Core Facility Paris France Anaesthesiology and Critical Care Medicine Department DMU Parabol Bichat-Claude Bernard Hospital Assistance Publique-Hôpitaux de Paris Paris France Université Paris Cité Paris France Service d'immunologie Hôpital Bichat Assistance Publique Hôpitaux de Paris Paris France CNRS F-75015 Paris France INSERM 1152 Département Hospitalo-Universitaire FIRE Labex Inflamex Université Paris Diderot Paris France

Background: Neuromuscular blocking agents (NMBAs) are muscle relaxants used to assist mechanical ventilation but lead in 1 per 10,000 anesthesia cases to severe acute hypersensitivity reactions—that is, anaphylaxis. Incidences vary between types of NMBAs. Rocuronium, a widely used nondepolarizing aminosteroid NMBA, induces among the highest anaphylaxis rates. Rocuronium-induced anaphylaxis is proposed to rely on preexisting rocuronium-binding antibodies, but no such antibodies have ever been identified. Objectives: We sought to identify rocuronium-specific antibody repertoires from plasma cells or plasmablasts of rocuronium-immunized mice to determine the affinities, structures, and anaphylactogenic potential of these antibodies for rocuronium. Methods: We engrafted rocuronium onto carrier proteins allowing immunization of mice against rocuronium, screening for rocuronium-specific antibody responses, and sorting of rocuronium-specific plasma cells using droplet microfluids coupled to single-cell antibody gene (variable heavy chain [VH] and variable light chain [VL]) sequencing. Results: The 2 different repertoires of >500 VH-VL pairs were oligoclonal, comprised 3 major clonal families, and displayed convergence. Expressed as human IgG1, these antibodies demonstrated subnanomolar affinities for rocuronium with families either monospecific for rocuronium or cross-reactive only for closely related NMBAs. Expressed as human IgE, they triggered human mast cell and basophil activation, and severe passive systemic anaphylaxis in mice humanized for the IgE receptor FcεRI. Cocrystal structures between rocuronium and antibody representatives of 3 different VH-VL families revealed distinct interaction modes, with the ammonium group involved systematically in the binding interface. Conclusions: This work identifies the epitopes of antibody reactivity to rocuronium, demonstrates anaphylactogenic potential of anti-rocuronium IgE, and establishes the first mouse model of NMBA anap

关键词： allergy anaphylaxis Antibodies B cells neuromuscular blocking agents plasma cells rocuronium therapy

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Representing bioinformatics Nextflow workflows in RO-Crate: challenges and opportunities 15

Representing bioinformatics Nextflow workflows in RO-Crate: ...

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15th International Conference on Semantic Web Applications and Tools for Health Care and Life Sciences, SWAT4HCLS 2024

作者： Marchment, George Schmit, Marie Sebe, Clémence Lemoine, Frédéric Ménager, Hervé Cohen-Boulakia, Sarah Université Paris-Saclay CNRS Laboratoire Interdisciplinaire des Sciences du Numérique Orsay91405 France Institut Pasteur Université Paris Cité Bioinformatics and Biostatistics Hub 28 rue du Dr Roux Paris75015 France

This research was conducted as part of the ShareFAIR project dedicated to assisting bioinformaticians in creating, comparing, and exchanging robust analysis workflows for multi-scale datasets related to neuro-vascular pathologies. ShareFAIR partners use workflows from diverse types of systems. One important challenge lies in uniformly and simply representing workflows such that partners can understand, share and reuse them. The aim of this research is to evaluate how the standards currently available, especially RO-Crate, allow to represent workflows. © 2024 Copyright for this paper by its authors.

关键词： bioinformatics

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Artificial intelligence for modelling infectious disease epidemics

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Nature 2025年第8051期638卷 623-635页

作者： Kraemer, Moritz U. G. Tsui, Joseph L.-H. Chang, Serina Y. Lytras, Spyros Khurana, Mark P. Vanderslott, Samantha Bajaj, Sumali Scheidwasser, Neil Curran-Sebastian, Jacob Liam Semenova, Elizaveta Zhang, Mengyan Unwin, H. Juliette T. Watson, Oliver J. Mills, Cathal Dasgupta, Abhishek Ferretti, Luca Scarpino, Samuel V. Koua, Etien Morgan, Oliver Tegally, Houriiyah Paquet, Ulrich Moutsianas, Loukas Fraser, Christophe Ferguson, Neil M. Topol, Eric J. Duchêne, David A. Stadler, Tanja Kingori, Patricia Parker, Michael J. Dominici, Francesca Shadbolt, Nigel Suchard, Marc A. Ratmann, Oliver Flaxman, Seth Holmes, Edward C. Gomez-Rodriguez, Manuel Schölkopf, Bernhard Donnelly, Christl A. Pybus, Oliver G. Cauchemez, Simon Bhatt, Samir Pandemic Sciences Institute University of Oxford Oxford United Kingdom Department of Biology University of Oxford Oxford United Kingdom Department of Electrical Engineering and Computer Science University of California Berkeley Berkeley CA United States UCSF UC Berkeley Joint Program in Computational Precision Health Berkeley CA United States Division of Systems Virology Department of Microbiology and Immunology The Institute of Medical Science The University of Tokyo Tokyo Japan Section of Epidemiology Department of Public Health University of Copenhagen Copenhagen Denmark Oxford Vaccine Group University of Oxford and NIHR Oxford Biomedical Research Centre Oxford United Kingdom Department of Epidemiology and Biostatistics Imperial College London London United Kingdom Department of Computer Science University of Oxford Oxford United Kingdom School of Mathematics University of Bristol Bristol United Kingdom MRC Centre for Global Infectious Disease Analysis School of Public Health Imperial College London London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Doctoral Training Centre University of Oxford Oxford United Kingdom Institute for Experiential AI Northeastern University MA Boston Thailand Santa Fe Institute Santa Fe NM United States World Health Organization Regional Office for Africa Brazzaville Congo WHO Hub for Pandemic and Epidemic Intelligence Health Emergencies Programme World Health Organization Berlin Germany Centre for Epidemic Response and Innovation (CERI) School for Data Science and Computational Thinking Stellenbosch University Stellenbosch South Africa African Institute for Mathematical Sciences (AIMS) South Africa Muizenberg Cape Town South Africa Genomics England London United Kingdom Scripps Research La Jolla CA United States Department of Biosystems Science and Engineering ETH Zürich Basel Switzerland Swiss Institute of Bioinformatics Lausanne Switzerland The Ethox Centre Nuffield

Infectious disease threats to individual and public health are numerous, varied and frequently unexpected. Artificial intelligence (AI) and related technologies, which are already supporting human decision making in economics, medicine and social science, have the potential to transform the scope and power of infectious disease epidemiology. Here we consider the application to infectious disease modelling of AI systems that combine machine learning, computational statistics, information retrieval and data science. We first outline how recent advances in AI can accelerate breakthroughs in answering key epidemiological questions and we discuss specific AI methods that can be applied to routinely collected infectious disease surveillance data. Second, we elaborate on the social context of AI for infectious disease epidemiology, including issues such as explainability, safety, accountability and ethics. Finally, we summarize some limitations of AI applications in this field and provide recommendations for how infectious disease epidemiology can harness most effectively current and future developments in AI. © Springer Nature Limited 2025.

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Compression Algorithm for Colored de Bruijn Graphs 23

Compression Algorithm for Colored de Bruijn Graphs

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23rd International Workshop on Algorithms in bioinformatics, WABI 2023

作者： Rahman, Amatur Dufresne, Yoann Medvedev, Paul Department of Computer Science and Engineering The Pennsylvania State University University ParkPA United States Institut Pasteur Université Paris Cité G5 Sequence Bioinformatics Paris France Institut Pasteur Université Paris Cité Bioinformatics and Biostatistics Hub ParisF-75015 France Department of Biochemistry and Molecular Biology The Pennsylvania State University University ParkPA United States Huck Institutes of the Life Sciences The Pennsylvania State University University ParkPA United States

ISBN: (纸本)9783959772945

A colored de Bruijn graph (also called a set of k-mer sets), is a set of k-mers with every k-mer assigned a set of colors. Colored de Bruijn graphs are used in a variety of applications, including variant calling, genome assembly, and database search. However, their size has posed a scalability challenge to algorithm developers and users. There have been numerous indexing data structures proposed that allow to store the graph compactly while supporting fast query operations. However, disk compression algorithms, which do not need to support queries on the compressed data and can thus be more space-efficient, have received little attention. The dearth of specialized compression tools has been a detriment to tool developers, tool users, and reproducibility efforts. In this paper, we develop a new tool that compresses colored de Bruijn graphs to disk, building on previous ideas for compression of k-mer sets and indexing colored de Bruijn graphs. We test our tool, called ESS-color, on various datasets, including both sequencing data and whole genomes. ESS-color achieves better compression than all evaluated tools and all datasets, with no other tool able to consistently achieve less than 44% space overhead. © Amatur Rahman, Yoann Dufresne, and Paul Medvedev;licensed under Creative Commons License CC-BY 4.0.

关键词： Color

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Check My Sample Sheet: a Web Application for Validating Illumina Sample Sheets Before Demultiplexing

Research Square

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Research Square 2024年

作者： Lemée, Laure Ouazahrou, Rania Kornobis, Etienne Cokelaer, Thomas Institut Pasteur Université Paris Cité Plate-forme Technologique Biomics ParisF-75015 France Institut Pasteur Université Paris Cité Bioinformatics and Biostatistics Hub ParisF-75015 France

The proper configuration of an Illumina sequencing run is paramount to its success, and the sample sheet, which summarizes sample metadata, is a critical component. To ensure accurate demultiplexing and downstream analysis, validation of this sample sheet is essential. Here, we present a user-friendly web application built with Streamlit, leveraging the IEM Python module from the Sequana Python library, designed for thorough sanity checks of Illumina sample sheets. Our application offers a streamlined solution for sequencing platforms seeking to validate sample sheets prior to initiating demultiplexing processes. Through an intuitive interface, users can easily upload sample sheet files, triggering a suite of validation checks that include crucial parameters such as sample duplication, consistency, and adherence to Illumina formatting standards. The Web application Check My Sample Sheet empowers users to identify and rectify potential problems preemptively, mitigate errors, and optimize sequencing outcomes. Furthermore, our application’s open-source nature facilitates extensibility, allowing for customization to specific workflow requirements or other types of sample sheet formats. © 2024, CC BY.

关键词： Demultiplexing

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Prediction regions through inverse regression

arXiv

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arXiv 2018年

作者： Devijver, Emilie Perthame, Emeline Univ. Grenoble Alpes CNRS Grenoble INP LIG Grenoble38000 France Institut Pasteur Bioinformatics and Biostatistics Hub C3BI USR 3756 IP CNRS Paris France

Predict a new response from a covariate is a challenging task in regression, which raises new question since the era of high-dimensional data. In this paper, we are interested in the inverse regression method from a theoretical viewpoint. Theoretical results have already been derived for the well-known linear model, but recently, the curse of dimensionality has increased the interest of practitioners and theoreticians into generalization of those results for various estimators, calibrated for the high-dimension context. To deal with high-dimensional data, inverse regression is used in this paper. It is known to be a reliable and efficient approach when the number of features exceeds the number of observations. Indeed, under some conditions, dealing with the inverse regression problem associated to a for-ward regression problem drastically reduces the number of parameters to estimate and make the problem tractable. When both the responses and the covariates are multivariate, estimators constructed by the inverse regression are studied in this paper, the main result being explicit asymptotic prediction regions for the response. The performances of the proposed estimators and prediction regions are also analyzed through a simulation study and compared with usual *** Codes 62F12, 62F25, 62J05, 62E20 Copyright © 2018, The Authors. All rights reserved.

关键词： Forecasting

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