Suchergebnisse - Comprehensive Integration of Single-Cell Transcriptomic Data

Comprehensive Integration of Multiple Single-Cell Transcriptomic Data Sets Defines Distinct Cell Populations and Their Phenotypic Changes in Murine Atherosclerosis

Autoren: Disha Sharma Matthew D. Worssam Albert J. Pedroza et al.

Quelle: Arteriosclerosis, Thrombosis, and Vascular Biology. 44:391-408

Schlagwörter: 0301 basic medicine, Mice, 0303 health sciences, 03 medical and health sciences, Phenotype, Gene Expression Profiling, Macrophages, Myocytes, Smooth Muscle, Animals, Transcriptome, Atherosclerosis

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/38152886

Comprehensive integration of single-cell transcriptomic data illuminates the regulatory network architecture of plant cell fate specification

Autoren: Shanni Cao Xue Zhao Zhuojin Li et al.

Quelle: Plant Divers
Plant Diversity, Vol 46, Iss 3, Pp 372-385 (2024)

Schlagwörter: 0301 basic medicine, Single cell transcriptome, 0303 health sciences, 03 medical and health sciences, QH301-705.5, QK1-989, Gene regulatory network, Arabidopsis, Botany, Data integration, Biology (General), Plant cell atlas, Research Paper

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/38798726
https://doaj.org/article/28da028d00a44f8783f15994d35f8545

Comprehensive integration of single-cell transcriptomic data illuminates the regulatory network architecture of plant cell fate specification

Autoren: Shanni Cao Xue Zhao Zhuojin Li et al.

Quelle: Plant Diversity, Vol 46, Iss 3, Pp 372-385 (2024)

Schlagwörter: Arabidopsis, Single cell transcriptome, Gene regulatory network, Data integration, Plant cell atlas, Biology (General), QH301-705.5, Botany, QK1-989

Dateibeschreibung: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S2468265924000477; https://doaj.org/toc/2468-2659

Zugangs-URL: https://doaj.org/article/28da028d00a44f8783f15994d35f8545

Data integration of longitudinal single-cell with multi-omics data to enable precision medicine for complex diseases

Autoren: Mihajlović, Katarina

Weitere Verfasser: University/Department: Universitat Politècnica de Catalunya. Departament de Ciències de la Computació

Thesis Advisors: Przulj, Natasa, Larrosa Bondia, Francisco Javier

Quelle: TDX (Tesis Doctorals en Xarxa)

Schlagwörter: bioinformatics, network biology, multi-omics, data integration, single-cell, precision medicine, machine learning, complex diseases, Àrees temàtiques de la UPC::Informàtica, Àrees temàtiques de la UPC::Enginyeria biomèdica, 004 - Informàtica, 616 - Patologia. Medicina clínica. Oncologia

Dateibeschreibung: application/pdf

Zugangs-URL: http://hdl.handle.net/10803/694728
https://dx.doi.org/10.5821/dissertation-2117-432840

Semi-supervised integration of single-cell transcriptomics data

Autoren: Massimo Andreatta Léonard Hérault Paul Gueguen et al.

Quelle: Nat Commun
Nature Communications, Vol 15, Iss 1, Pp 1-13 (2024)
Nature communications, vol. 15, no. 1, pp. 872

Schlagwörter: Humans, Sequence Analysis, RNA/methods, Single-Cell Analysis/methods, Gene Expression Profiling/methods, Data Integration, Regulatory T Cell Development and Function, Artificial intelligence, Science, Immunology, Exosome Biology and Function in Intercellular Communication, Article, Database, FOS: Economics and business, Biochemistry, Genetics and Molecular Biology, Machine learning, Cell Heterogeneity, Business, Transcriptomics, Molecular Biology, Data mining, Immunology and Microbiology, Marketing, Geography, Sequence Analysis, RNA, Gene Expression Profiling, FOS: Clinical medicine, Macrophage Activation and Polarization, Scalability, Life Sciences, Comprehensive Integration of Single-Cell Transcriptomic Data, Computer science, Programming language, Benchmarking, Data integration, Pipeline (software), Single-Cell Analysis, Benchmark (surveying), Geodesy

Dateibeschreibung: application/pdf

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/38287014
https://doaj.org/article/d35b657fcd2e4fe8aefe46b618827fe8
https://serval.unil.ch/notice/serval:BIB_E4C01949C6C2
https://serval.unil.ch/resource/serval:BIB_E4C01949C6C2.P001/REF.pdf
http://nbn-resolving.org/urn/resolver.pl?urn=urn:nbn:ch:serval-BIB_E4C01949C6C29

Integration of Single-Cell and Spatial Transcriptomic Data Reveals Spatial Architecture and Potential Biomarkers in Alzheimer's Disease.

Autoren: Fan X Li H

Quelle: Molecular neurobiology [Mol Neurobiol] 2025 May; Vol. 62 (5), pp. 5395-5412. Date of Electronic Publication: 2024 Nov 15.

Publikationsart: Journal Article

Info zur Zeitschrift: Publisher: Humana Press Country of Publication: United States NLM ID: 8900963 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1559-1182 (Electronic) Linking ISSN: 08937648 NLM ISO Abbreviation: Mol Neurobiol Subsets: MEDLINE

MeSH-Schlagworte: Alzheimer Disease*/genetics , Alzheimer Disease*/pathology , Alzheimer Disease*/metabolism , Single-Cell Analysis*/methods , Transcriptome*/genetics, Humans ; Biomarkers/metabolism ; Oligodendroglia/metabolism ; Oligodendroglia/pathology ; Aged ; Prefrontal Cortex/pathology ; Prefrontal Cortex/metabolism ; Male ; Female ; Gene Expression Profiling ; White Matter/pathology ; White Matter/metabolism ; Aged, 80 and over

Machine learning predicts putative hematopoietic stem cells within large single-cell transcriptomics data sets

Autoren: Fiona Hamey Berthold Göttgens DSpace at Cambridge pro (8.1)

Weitere Verfasser: Fiona Hamey Berthold Göttgens DSpace at Cambridge pro (8.1)

Quelle: Exp Hematol

Schlagwörter: 0301 basic medicine, Cell biology, Immunology, Hematopoietic stem cell, Gene, Article, Single-Cell, Machine Learning, Computational biology, Mice, Identification (biology), 03 medical and health sciences, Biochemistry, Genetics and Molecular Biology, Health Sciences, Genetics, Animals, Cell Heterogeneity, Bone marrow, Hematopoietic Cell Transplantation, Flow cytometry, Transcriptomics, Molecular Biology, Biology, Mice, Knockout, Immunology and Microbiology, Transplantation, 0303 health sciences, Stem cell, Sequence Analysis, RNA, Gene Expression Profiling, Macrophage Activation and Polarization, FOS: Clinical medicine, Botany, Life Sciences, Hematology, Comprehensive Integration of Single-Cell Transcriptomic Data, Hematopoietic Stem Cells, Haematopoiesis, Function (biology), FOS: Biological sciences, Medicine, Hematopoietic Stem Cell Biology, Surgery, Gene expression, Transcriptome

Dateibeschreibung: application/pdf; application/octet-stream; text/plain

Zugangs-URL: http://www.exphem.org/article/S0301472X19309956/pdf
https://pubmed.ncbi.nlm.nih.gov/31513832
https://pubmed.ncbi.nlm.nih.gov/31513832/
https://www.exphem.org/article/S0301-472X(19)30995-6/fulltext
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6900257
https://www.sciencedirect.com/science/article/pii/S0301472X19309956
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6900257
https://www.repository.cam.ac.uk/handle/1810/296436
https://doi.org/10.17863/cam.43486
https://doi.org/10.1016/j.exphem.2019.08.009

Monod: model-based discovery and integration through fitting stochastic transcriptional dynamics to single-cell sequencing data.

Autoren: Gorin G Chari T Carilli M et al.

Quelle: Nature methods [Nat Methods] 2025 Nov; Vol. 22 (11), pp. 2286-2300. Date of Electronic Publication: 2025 Nov 07.

Publikationsart: Journal Article

Info zur Zeitschrift: Publisher: Nature Pub. Group Country of Publication: United States NLM ID: 101215604 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1548-7105 (Electronic) Linking ISSN: 15487091 NLM ISO Abbreviation: Nat Methods Subsets: MEDLINE

MeSH-Schlagworte: Single-Cell Analysis*/methods , Sequence Analysis, RNA*/methods , Transcription, Genetic*, Stochastic Processes ; Humans ; Gene Expression Regulation ; Software ; Gene Expression Profiling/methods

Consensus Nature Inspired Clustering of Single-Cell RNA-Sequencing Data

Autoren: Amany H. Abou El-Naga Akram Salah Sabah Sayed et al.

Quelle: IEEE Access, Vol 10, Pp 98079-98094 (2022)

Schlagwörter: 0301 basic medicine, Artificial intelligence, 0206 medical engineering, 02 engineering and technology, unsupervised learning, Clustering, Single-Cell, Computational biology, 03 medical and health sciences, Cluster analysis, Biochemistry, Genetics and Molecular Biology, Microarray Data Analysis and Gene Expression Profiling, Droplet-based Sequencing, Cell Heterogeneity, RNA-Seq, Molecular Biology, Data mining, Biology, Single-cell RNA-seq, automatic clustering, swarm intelligence, consensus clustering, Life Sciences, Comprehensive Integration of Single-Cell Transcriptomic Data, Computer science, metaheuristic algorithms, TK1-9971, Analysis of Gene Interaction Networks, 3. Good health, Electrical engineering. Electronics. Nuclear engineering

Zugangs-URL: https://doaj.org/article/485c5ea17339418490afa6072d69bc87

Reconstructing unobserved cellular states from paired single-cell lineage tracing and transcriptomics data

Autoren: Matthew G. Jones Nir Yosef Ouardini K et al.

Schlagwörter: 0301 basic medicine, Cancer Research, Artificial intelligence, Population, Evolutionary biology, Bayesian probability, Gene, Computational biology, 03 medical and health sciences, Tracing, Sociology, Biochemistry, Genetics and Molecular Biology, Microarray Data Analysis and Gene Expression Profiling, Genetics, Transcriptomics, Molecular Biology, Biology, Data mining, Probabilistic logic, Demography, Lineage (genetic), Lineage Tracking, Life Sciences, Measure (data warehouse), Comprehensive Integration of Single-Cell Transcriptomic Data, 15. Life on land, Computer science, Genomic Landscape of Cancer and Mutational Signatures, FOS: Sociology, 3. Good health, Operating system, FOS: Biological sciences, Gene expression, Transcriptome, Phylogenetic tree

Zugangs-URL: https://www.biorxiv.org/content/biorxiv/early/2021/05/30/2021.05.28.446021.full.pdf
https://europepmc.org/article/PPR/PPR350046
https://www.biorxiv.org/content/10.1101/2021.05.28.446021v1
https://biorxiv.org/cgi/content/short/2021.05.28.446021v1
https://biorxiv.org/content/10.1101/2021.05.28.446021v1.full.pdf

High‐resolution single‐cell transcriptomic survey of cardiomyocytes from patients with hypertrophic cardiomyopathy

Autoren: Jiansen Lu Jie Ren Jie Liu et al.

Quelle: Cell Prolif

Schlagwörter: Cell biology, Cardiomyopathy, Cardiomyocyte Renewal, Immunology, Heart failure, Pathogenesis, Gene, Biochemistry, Computational biology, Biochemistry, Genetics and Molecular Biology, Heart Regeneration, Health Sciences, Genetics, Humans, Myocytes, Cardiac, Cell Heterogeneity, RNA-Seq, Cardiac Imaging, Molecular Mechanisms of Cardiac Development and Regeneration, Transcriptomics, Molecular Biology, Biology, Internal medicine, Gene Expression Profiling, FOS: Clinical medicine, Diagnosis and Management of Hypertrophic Cardiomyopathy, Life Sciences, Original Articles, Extracellular matrix, Cardiomyopathy, Hypertrophic, Comprehensive Integration of Single-Cell Transcriptomic Data, 3. Good health, Hypertrophic cardiomyopathy, FOS: Biological sciences, Myocyte, Medicine, Cell, Gene expression, Cardiology and Cardiovascular Medicine, Transcriptome

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/37766635

Single-cell analyses implicate ascites in remodeling the ecosystems of primary and metastatic tumors in ovarian cancer

Autoren: Xiaodong Zheng Xinjing Wang Xueqing Cheng et al.

Quelle: Nat Cancer

Schlagwörter: Immunology, Cancer research, CD8-Positive T-Lymphocytes, Article, Single-Cell, Monocytes, Metastasis, Primary tumor, Ovarian cancer, Biochemistry, Genetics and Molecular Biology, Pathology, Humans, Immunological Mechanisms in Pregnancy and Fetal-Maternal Interface, Molecular Biology, Biology, Internal medicine, Ecosystem, Cancer, Ovarian Neoplasms, Immunology and Microbiology, Macrophage Activation and Polarization, FOS: Clinical medicine, Life Sciences, Ascites, CD8, Comprehensive Integration of Single-Cell Transcriptomic Data, Tumor progression, 3. Good health, Immune system, Medicine, Female, Single-Cell Analysis

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/37488416

ScribbleDom: using scribble-annotated histology images to identify domains in spatial transcriptomics data

Autoren: Mohammad Nuwaisir Rahman Abdullah Al Noman Abir Mohammad Turza et al.

Quelle: Bioinformatics

Schlagwörter: Advanced Techniques in Bioimage Analysis and Microscopy, Artificial neural network, Artificial intelligence, Medical Sciences, Phenotypic Profiling, Biophysics, Convolutional neural network, Pattern recognition (psychology), Biochemistry, Mathematical analysis, Spatial Profiling, Identification (biology), Convolution (computer science), Biochemistry, Genetics and Molecular Biology, Field (mathematics), Microarray Data Analysis and Gene Expression Profiling, Machine learning, Medical Specialties, Medicine and Health Sciences, Image (mathematics), FOS: Mathematics, Droplet-based Sequencing, Similarity (geometry), Transcriptomics, Molecular Biology, Data mining, Biology, Original Paper, Domain (mathematical analysis), Geography, Physics, Pure mathematics, Botany, Life Sciences, Deep learning, Optics, Comprehensive Integration of Single-Cell Transcriptomic Data, Focus (optics), Computer science, and Structural Biology, Benchmark (surveying), Mathematics, Geodesy

Dateibeschreibung: application/pdf

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/37756699

Single‐cell transcriptomics identify TNFRSF1B as a novel T‐cell exhaustion marker for ovarian cancer

Autoren: Yan Gao Hui Shi Hongyu Zhao et al.

Quelle: Clin Transl Med
Clinical and Translational Medicine, Vol 13, Iss 9, Pp n/a-n/a (2023)

Schlagwörter: Medicine (General), CD3 Complex, TNFRSF1B, Immunology, Cancer research, CD8-Positive T-Lymphocytes, Mice, R5-920, Ovarian cancer, Biochemistry, Genetics and Molecular Biology, Tumor Microenvironment, Animals, Humans, Receptors, Tumor Necrosis Factor, Type II, Flow cytometry, single‐cell RNA‐seq, Molecular Biology, Biology, Internal medicine, Research Articles, Ecosystem, Cancer, Ovarian Neoplasms, Immunology and Microbiology, FOS: Clinical medicine, Macrophage Activation and Polarization, NK Cell Recognition, Life Sciences, CD8, Comprehensive Integration of Single-Cell Transcriptomic Data, T-Cell Exhaustion, 3. Good health, Immune system, Tumor microenvironment, Medicine, Female, tumour microenvironment, Transcriptome, Natural Killer Cells in Immunity

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/37712139
https://doaj.org/article/55e83b2aec234c0d8fcd541fb5a411b1

Camtrap DP: An open standard for the FAIR exchange and archiving of camera trap data: an open standard for the FAIR exchange and archiving of camera trap data

Autoren: Jakub W. Bubnicki Ben Norton Steven J. Baskauf et al.

Weitere Verfasser: Jakub W. Bubnicki Ben Norton Steven J. Baskauf et al.

Quelle: Remote Sensing in Ecology and Conservation, Vol 10, Iss 3, Pp 283-295 (2024)
Remote Sensing in Ecology and Conservation

Schlagwörter: Data Integration, Technology, data sharing, SOFTWARE, Wildlife, VDP::Matematikk og Naturvitenskap: 400::Informasjons- og kommunikasjonsvitenskap: 420, Data science, Remote Sensing, 0502 Environmental Science and Management, Camera trap, QH540-549.5, Nature and Landscape Conservation, Computers in Earth Sciences, Ecology, Ecology, Evolution, Behavior and Systematics, Species Distribution Modeling and Climate Change Impacts, CHALLENGES, camera trap, Ecological Modeling, Camera traps, 3103 Ecology, BIG-DATA, Life Sciences, Comprehensive Integration of Single-Cell Transcriptomic Data, Interoperability, Programming language, World Wide Web, Physical Sciences, Life Sciences & Biomedicine, biodiversity data, Environmental Sciences & Ecology, Citizen science, ECOLOGY, Data management, ROE DEER, Database, 4104 Environmental management, camera traps, Settore BIO/07 - ECOLOGIA, Biochemistry, Genetics and Molecular Biology, MANAGEMENT, Molecular Biology, Biology, Metadata, Science & Technology, 0602 Ecology, Botany, information standards, Data exchange, NEED, data exchange, VDP::Matematikk og Naturvitenskap: 400, 15. Life on land, Biodiversity data, Computer science, Information standard, FOS: Biological sciences, Environmental Science, remote sensing, Data sharing, BIODIVERSITY, Environmental DNA in Biodiversity Monitoring, Software

Dateibeschreibung: application/pdf; pdf

Zugangs-URL: https://doaj.org/article/6c197198f4a745c6846b1fd4684f5199
https://hdl.handle.net/11250/3107083
https://serval.unil.ch/notice/serval:BIB_4F1A622D0524
http://nbn-resolving.org/urn/resolver.pl?urn=urn:nbn:ch:serval-BIB_4F1A622D05244
https://serval.unil.ch/resource/serval:BIB_4F1A622D0524.P001/REF.pdf
https://repository.publisso.de/resource/frl:6482774
https://hdl.handle.net/11250/3107077
https://discovery-pp.ucl.ac.uk/id/eprint/10186308/

Transcriptome landscape of high and low responders to an inactivated COVID-19 vaccine after 4 months using single-cell sequencing

Autoren: Zhongyi Zhu Yaling Huang Jun Sun et al.

Schlagwörter: Regulatory T Cell Development and Function, Immunobiology of Dendritic Cells, Immunology, Population, Gene, Single-Cell, Computational biology, In vitro, Biochemistry, Genetics and Molecular Biology, Virology, Genetics, Transcriptomics, Molecular Biology, Biology, Antibody, Immunology and Microbiology, FOS: Clinical medicine, Innate immune system, Vaccination, Immunity, Life Sciences, Comprehensive Integration of Single-Cell Transcriptomic Data, Peripheral blood mononuclear cell, 3. Good health, Immune system, Environmental health, FOS: Biological sciences, Medicine, Cell, Gene expression, Transcriptome

NoVaTeST: identifying genes with location-dependent noise variance in spatial transcriptomics data

Autoren: Mohammed Abid Abrar M Kaykobad M Saifur Rahman et al.

Quelle: Bioinformatics

Schlagwörter: Artificial intelligence, Noise (video), Real-Time Polymerase Chain Reaction, Gene, Spatial Profiling, Computational biology, Variance (accounting), Biochemistry, Genetics and Molecular Biology, Accounting, Microarray Data Analysis and Gene Expression Profiling, FOS: Mathematics, Genetics, Image (mathematics), Business, Molecular Biology, Data mining, Biology, Original Paper, Gene Expression Profiling, Python (programming language), Statistics, Spatial analysis, Life Sciences, Comprehensive Integration of Single-Cell Transcriptomic Data, Computer science, Programming language, Operating system, Function (biology), FOS: Biological sciences, Pipeline (software), Transcriptome, Software, Mathematics

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/37285319

Identification of ligand and receptor interactions in CKD and MASH through the integration of single cell and spatial transcriptomics

Autoren: Moreno, Jaime Gluud, Lise Lotte Galsgaard, Elisabeth D. et al.

Quelle: Moreno , J , Gluud , L L , Galsgaard , E D , Hvid , H , Mazzoni , G & Das , V 2024 , ' Identification of ligand and receptor interactions in CKD and MASH through the integration of single cell and spatial transcriptomics ' , PLoS ONE , vol. 19 , no. 5 , e0302853 .

Index Begriffe: article

URL: https://researchprofiles.ku.dk/da/publications/identification-of-ligand-and-receptor-interactions-in-ckd-and-mash-through-the-integration-of-single-cell-and-spatial-transcriptomics(f39eea8b-e2cc-474a-8f85-82b42cbc495f).html
https://doi.org/10.1371/journal.pone.0302853
https://curis.ku.dk/ws/files/392976499/journal.pone.0302853.pdf

ShIVA – A user-friendly and interactive interface giving biologists control over their single-cell RNA-seq data

Autoren: Rudy Aussel Muhammad Asif Sabrina Chenag et al.

Weitere Verfasser: Rudy Aussel Muhammad Asif Sabrina Chenag et al.

Quelle: Sci Rep
Scientific Reports, Vol 13, Iss 1, Pp 1-7 (2023)

Schlagwörter: Data Analysis, Advanced Techniques in Bioimage Analysis and Microscopy, Parallel computing, 0301 basic medicine, Cancer Research, Interface (matter), Science, Health Personnel, Biophysics, Gene, Biochemistry, Article, Single-Cell, Data science, 03 medical and health sciences, Biochemistry, Genetics and Molecular Biology, Humans, Information retrieval, RNA-Seq, Molecular Biology, Data mining, Biology, Bubble, 0303 health sciences, Human–computer interaction, Gene Expression Profiling, Life Sciences, Genomics, Comprehensive Integration of Single-Cell Transcriptomic Data, Multiomics, Computer science, Single-Cell Gene Expression Analysis, Genomic Landscape of Cancer and Mutational Signatures, Process (computing), [SDV] Life Sciences [q-bio], Operating system, Medicine, Bioimage Analysis, Gene expression, Maximum bubble pressure method, Transcriptome, Software

Dateibeschreibung: application/pdf

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/37658061
https://doaj.org/article/65d5944731514148b011c5306d696180

Comprehensive multi-omics single-cell data integration reveals greater heterogeneity in the human immune system

Autoren: Congmin Xu Junkai Yang Astrid Kosters et al.

Quelle: iScience, Vol 25, Iss 10, Pp 105123- (2022)

Schlagwörter: Biocomputational method, Systems biology, Omics, Science

Dateibeschreibung: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S2589004222013955; https://doaj.org/toc/2589-0042

Zugangs-URL: https://doaj.org/article/372f05bd44cd4854825e14b536a1bcfb