Relevant bibliographies by topics / Single-Cell omics

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Academic literature on the topic 'Single-Cell omics'

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Published: 1 February 2025

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Journal articles on the topic "Single-Cell omics"

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Choi, Joung Min, Chaelin Park, and Heejoon Chae. "moSCminer: a cell subtype classification framework based on the attention neural network integrating the single-cell multi-omics dataset on the cloud." PeerJ 12 (February 26, 2024): e17006. http://dx.doi.org/10.7717/peerj.17006.

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Single-cell omics sequencing has rapidly advanced, enabling the quantification of diverse omics profiles at a single-cell resolution. To facilitate comprehensive biological insights, such as cellular differentiation trajectories, precise annotation of cell subtypes is essential. Conventional methods involve clustering cells and manually assigning subtypes based on canonical markers, a labor-intensive and expert-dependent process. Hence, an automated computational prediction framework is crucial. While several classification frameworks for predicting cell subtypes from single-cell RNA sequencing datasets exist, these methods solely rely on single-omics data, offering insights at a single molecular level. They often miss inter-omic correlations and a holistic understanding of cellular processes. To address this, the integration of multi-omics datasets from individual cells is essential for accurate subtype annotation. This article introduces moSCminer, a novel framework for classifying cell subtypes that harnesses the power of single-cell multi-omics sequencing datasets through an attention-based neural network operating at the omics level. By integrating three distinct omics datasets—gene expression, DNA methylation, and DNA accessibility—while accounting for their biological relationships, moSCminer excels at learning the relative significance of each omics feature. It then transforms this knowledge into a novel representation for cell subtype classification. Comparative evaluations against standard machine learning-based classifiers demonstrate moSCminer’s superior performance, consistently achieving the highest average performance on real datasets. The efficacy of multi-omics integration is further corroborated through an in-depth analysis of the omics-level attention module, which identifies potential markers for cell subtype annotation. To enhance accessibility and scalability, moSCminer is accessible as a user-friendly web-based platform seamlessly connected to a cloud system, publicly accessible at http://203.252.206.118:5568. Notably, this study marks the pioneering integration of three single-cell multi-omics datasets for cell subtype identification.
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Rusk, Nicole. "Multi-omics single-cell analysis." Nature Methods 16, no. 8 (July 30, 2019): 679. http://dx.doi.org/10.1038/s41592-019-0519-3.

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Chappell, Lia, Andrew J. C. Russell, and Thierry Voet. "Single-Cell (Multi)omics Technologies." Annual Review of Genomics and Human Genetics 19, no. 1 (August 31, 2018): 15–41. http://dx.doi.org/10.1146/annurev-genom-091416-035324.

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Single-cell multiomics technologies typically measure multiple types of molecule from the same individual cell, enabling more profound biological insight than can be inferred by analyzing each molecular layer from separate cells. These single-cell multiomics technologies can reveal cellular heterogeneity at multiple molecular layers within a population of cells and reveal how this variation is coupled or uncoupled between the captured omic layers. The data sets generated by these techniques have the potential to enable a deeper understanding of the key biological processes and mechanisms driving cellular heterogeneity and how they are linked with normal development and aging as well as disease etiology. This review details both established and novel single-cell mono- and multiomics technologies and considers their limitations, applications, and likely future developments.
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Xu, Xing, Junxia Wang, Lingling Wu, Jingjing Guo, Yanling Song, Tian Tian, Wei Wang, Zhi Zhu, and Chaoyong Yang. "Microfluidic Single‐Cell Omics Analysis." Small 16, no. 9 (September 23, 2019): 1903905. http://dx.doi.org/10.1002/smll.201903905.

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Wang, Le, and Bo Jin. "Single-Cell RNA Sequencing and Combinatorial Approaches for Understanding Heart Biology and Disease." Biology 13, no. 10 (September 30, 2024): 783. http://dx.doi.org/10.3390/biology13100783.

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By directly measuring multiple molecular features in hundreds to millions of single cells, single-cell techniques allow for comprehensive characterization of the diversity of cells in the heart. These single-cell transcriptome and multi-omic studies are transforming our understanding of heart development and disease. Compared with single-dimensional inspections, the combination of transcriptomes with spatial dimensions and other omics can provide a comprehensive understanding of single-cell functions, microenvironment, dynamic processes, and their interrelationships. In this review, we will introduce the latest advances in cardiac health and disease at single-cell resolution; single-cell detection methods that can be used for transcriptome, genome, epigenome, and proteome analysis; single-cell multi-omics; as well as their future application prospects.
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Mincarelli, Laura, Ashleigh Lister, James Lipscombe, and Iain C. Macaulay. "Defining Cell Identity with Single-Cell Omics." PROTEOMICS 18, no. 18 (May 28, 2018): 1700312. http://dx.doi.org/10.1002/pmic.201700312.

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Yang, Xiaoxi, Yuqi Wen, Xinyu Song, Song He, and Xiaochen Bo. "Exploring the classification of cancer cell lines from multiple omic views." PeerJ 8 (August 18, 2020): e9440. http://dx.doi.org/10.7717/peerj.9440.

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Background Cancer classification is of great importance to understanding its pathogenesis, making diagnosis and developing treatment. The accumulation of extensive omics data of abundant cancer cell line provide basis for large scale classification of cancer with low cost. However, the reliability of cell lines as in vitro models of cancer has been controversial. Methods In this study, we explore the classification on pan-cancer cell line with single and integrated multiple omics data from the Cancer Cell Line Encyclopedia (CCLE) database. The representative omics data of cancer, mRNA data, miRNA data, copy number variation data, DNA methylation data and reverse-phase protein array data were taken into the analysis. TumorMap web tool was used to illustrate the landscape of molecular classification.The molecular classification of patient samples was compared with cancer cell lines. Results Eighteen molecular clusters were identified using integrated multiple omics clustering. Three pan-cancer clusters were found in integrated multiple omics clustering. By comparing with single omics clustering, we found that integrated clustering could capture both shared and complementary information from each omics data. Omics contribution analysis for clustering indicated that, although all the five omics data were of value, mRNA and proteomics data were particular important. While the classifications were generally consistent, samples from cancer patients were more diverse than cancer cell lines. Conclusions The clustering analysis based on integrated omics data provides a novel multi-dimensional map of cancer cell lines that can reflect the extent to pan-cancer cell lines represent primary tumors, and an approach to evaluate the importance of omic features in cancer classification.
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Deng, Yanxiang, Amanda Finck, and Rong Fan. "Single-Cell Omics Analyses Enabled by Microchip Technologies." Annual Review of Biomedical Engineering 21, no. 1 (June 4, 2019): 365–93. http://dx.doi.org/10.1146/annurev-bioeng-060418-052538.

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Single-cell omics studies provide unique information regarding cellular heterogeneity at various levels of the molecular biology central dogma. This knowledge facilitates a deeper understanding of how underlying molecular and architectural changes alter cell behavior, development, and disease processes. The emerging microchip-based tools for single-cell omics analysis are enabling the evaluation of cellular omics with high throughput, improved sensitivity, and reduced cost. We review state-of-the-art microchip platforms for profiling genomics, epigenomics, transcriptomics, proteomics, metabolomics, and multi-omics at single-cell resolution. We also discuss the background of and challenges in the analysis of each molecular layer and integration of multiple levels of omics data, as well as how microchip-based methodologies benefit these fields. Additionally, we examine the advantages and limitations of these approaches. Looking forward, we describe additional challenges and future opportunities that will facilitate the improvement and broad adoption of single-cell omics in life science and medicine.
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Rai, Muhammad Farooq, Chia-Lung Wu, Terence D. Capellini, Farshid Guilak, Amanda R. Dicks, Pushpanathan Muthuirulan, Fiorella Grandi, Nidhi Bhutani, and Jennifer J. Westendorf. "Single Cell Omics for Musculoskeletal Research." Current Osteoporosis Reports 19, no. 2 (February 9, 2021): 131–40. http://dx.doi.org/10.1007/s11914-021-00662-2.

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Lv, Dekang, Xuehong Zhang, and Quentin Liu. "Single-cell omics decipher tumor evolution." Medicine in Omics 2 (September 2021): 100006. http://dx.doi.org/10.1016/j.meomic.2021.100006.

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Dissertations / Theses on the topic "Single-Cell omics"

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Kim, Jieun. "Computational tools for the integrative analysis of muti-omics data to decipher trans-omics networks." Thesis, The University of Sydney, 2022. https://hdl.handle.net/2123/28524.

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Regulatory networks define the phenotype, morphology, and function of cells. These networks are built from the basic building blocks of the cell—DNA, RNA, and proteins—and cut across the respective omics layers—genome, transcriptome, and proteome. The resulting omics networks depict a near infinite possibility of nodes and edges that intricately connect the ‘omes’. With the rapid advancement in the technologies that generate omics data in bulk samples and now at single-cell resolution, the field of life sciences is now met with the challenge to connect these omes to generate trans-omics networks. To this end, this thesis addressed some of the pressing challenges in trans-omics network reconstruction and the integrative analysis of omics data at both bulk and single-cell resolution: 1) the lack of an integrated pipeline for processing and downstream analysis of lesser studied omics layers; 2) the need for an integrative framework to reconstruct transcriptional networks and discover novel regulators of transcriptional regulation; and 3) development of tools for the reconstruction of single-cell multi-modal TRNs. I envision the work of my thesis to contribute towards the integrative study of bulk and single-cell trans-omics analysis, which I believe will become essential and standard-place in molecular biological studies as the comprehensiveness and accuracy of omics data measurements and databases for connecting different omics improves.
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Lin, Yingxin. "Statistical modelling and machine learning for single cell data harmonisation and analysis." Thesis, The University of Sydney, 2022. https://hdl.handle.net/2123/28034.

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Technological advances such as large-scale single-cell profiling have exploded in recent years and enabled unprecedented understanding of the behaviour of individual cells. Effectively harmonising multiple collections and different modalities of single-cell data and accurately annotating cell types using reference, which we consider as the step of “intermediate data analysis” in this thesis, serve as a foundation for the downstream analysis to uncover biological insights from single-cell data. This thesis proposed several statistical modelling and machine learning methods to address several challenges in intermediate data analysis in the single-cell omics era, including: (1) scMerge to effectively integrate multiple collections of single-cell RNA-sequencing (scRNA-seq) datasets from a single modality; (2) scClassify to annotate cell types for scRNA-seq data by capitalising on the large collection of well-annotated scRNA-seq datasets; and (3) scJoint to integrate unpaired atlas-scale single-cell multi-omics data and transfer labels from scRNA-seq datasets to scATAC-seq data. We illustrate that the proposed methods enable a novel and scalable workflow to integratively analyse large-cohort single-cell data, demonstrating using a collection of single-cell multi-omics COVID-19 datasets. In summary, this thesis contributes to single-cell research by developing effective, integrative and scalable methods towards a more comprehensive understanding of cellular phenotypes at single-cell resolution.
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Kim, Taiyun. "Development of statistical methods for integrative omics analysis in precision medicine." Thesis, The University of Sydney, 2022. https://hdl.handle.net/2123/28838.

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Precision medicine is an integrative approach to the prevention and treatment of complex diseases such as cardiovascular disease that considers an individual’s lifestyle, clinical information, and omics profile. In the last decade, the advances in omics technologies have allowed researchers to gain insight into biological systems and progress to precision medicine. Many omics technology now enables us to rapidly generate, store and analyse data at a large scale. Many efforts have attempted to integrate large-scale multi-batch and multi-omics data. While many strategies have been developed, challenges remain in developing a robust method cap- able of pre-processing large-scale datasets, handling mislabelled information, and performing integrative analysis. Pre-processing any omics data is essential to remove technical factors whilst preserving biological variance. However, many methods still struggle to mitigate the batch effect, particularly for protracted acquisitions. Furthermore, robust visualisation tools for processing, quality control diagnostics, and integrative analysis of omics data are still lacking in effective data visualisation and integration. Lastly, cell type annotation remains a key challenge in single-cell transcriptomic data analysis due to the incompleteness of our current knowledge and the human subjectivity involved in manual curation. Together, these may result in cell type mislabelling and potentially lead to false discoveries in downstream analysis. This thesis first introduces each of the above challenges in detail (Chapter 1). We then introduce novel strategies and robust methods for the removal of unwanted variation in large-scale metabolomics data (Chapter 2), visualisation tools for omics data diagnostics and integrative analysis (Chapter 3), and cell-type identification methods in single cell transcriptomics data (Chapter 4). Chapter 5 summarises the contributions of each chapter to precision medicine and concludes the thesis.
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Blampey, Quentin. "Deep learning and computational methods on single-cell and spatial data for precision medicine in oncology." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASL116.

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La médecine de précision en oncologie a pour but de personnaliser les traitements en fonction des profils génétiques et moléculaires uniques des tumeurs des patients, et ce afin d'améliorer l'efficacité thérapeutique ou de minimiser les effets secondaires. À mesure que les avancées technologiques produisent des données de plus en plus précises sur le microenvironnement tumoral (TME), la complexité de ces données augmente également. Notamment, les données spatiales — un type récent et prometteur de données omiques — fournissent des informations moléculaires à la résolution de la cellule tout en conservant le contexte spatial des cellules au sein des tissus. Pour exploiter pleinement cette richesse et cette complexité, l'apprentissage profond émerge comme une approche capable de dépasser les limitations des approches traditionnelles. Ce manuscript détaille le développement de nouvelles méthodes de deep learning et computationnelles ayant pour but d'améliorer l'analyse des systèmes complexes des données single-cell et spatial. Trois outils sont décrits: (i) Scyan, pour l'annotation de types cellulaires en cytométrie, (ii) Sopa, une pipeline générale de preprocessing de données spatiales, et (iii) Novae, un modèle de fondation pour données spatiales. Ces méthodes sont appliqués à plusieurs projets de médecine de précision, approfondissant notre compréhension de la biologie du cancer et facilitant la découverte de nouveaux biomarqueurs et l'identification de cibles potentiellement actionnables pour la médecine de précision
Precision medicine in oncology customizes treatments based on the unique genetic and molecular profiles of patients' tumors, which is crucial for enhancing therapeutic efficacy and minimizing adverse effects. As technological advancements yield increasingly precise data about the tumor microenvironment (TME), the complexity of this data also grows. Notably, spatial data — a recent and promising type of omics data — provides molecular information at the single-cell level while maintaining the spatial context of cells within tissues. To fully exploit this rich and complex data, deep learning is emerging as a powerful approach that overcomes multiple limitations of traditional approaches. This manuscript details the development of new deep learning and computational methods to enhance our analysis of intricate systems like single-cell and spatial data. Three tools are introduced: (i) Scyan, for cell type annotation in cytometry, (ii) Sopa, a general pipeline for spatial omics, and (iii) Novae, a foundation model for spatial omics. These methods are applied to multiple precision medicine projects, exemplifying how they deepen our understanding of cancer biology, facilitating the discovery of new biomarkers and identifying potentially actionable targets for precision medicine
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Ronen, Jonathan. "Integrative analysis of data from multiple experiments." Doctoral thesis, Humboldt-Universität zu Berlin, 2020. http://dx.doi.org/10.18452/21612.

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Auf die Entwicklung der Hochdurchsatz-Sequenzierung (HTS) folgte eine Reihe von speziellen Erweiterungen, die erlauben verschiedene zellbiologischer Aspekte wie Genexpression, DNA-Methylierung, etc. zu messen. Die Analyse dieser Daten erfordert die Entwicklung von Algorithmen, die einzelne Experimenteberücksichtigen oder mehrere Datenquellen gleichzeitig in betracht nehmen. Der letztere Ansatz bietet besondere Vorteile bei Analyse von einzelligen RNA-Sequenzierung (scRNA-seq) Experimenten welche von besonders hohem technischen Rauschen, etwa durch den Verlust an Molekülen durch die Behandlung geringer Ausgangsmengen, gekennzeichnet sind. Um diese experimentellen Defizite auszugleichen, habe ich eine Methode namens netSmooth entwickelt, welche die scRNA-seq-Daten entrascht und fehlende Werte mittels Netzwerkdiffusion über ein Gennetzwerk imputiert. Das Gennetzwerk reflektiert dabei erwartete Koexpressionsmuster von Genen. Unter Verwendung eines Gennetzwerks, das aus Protein-Protein-Interaktionen aufgebaut ist, zeige ich, dass netSmooth anderen hochmodernen scRNA-Seq-Imputationsmethoden bei der Identifizierung von Blutzelltypen in der Hämatopoese, zur Aufklärung von Zeitreihendaten unter Verwendung eines embryonalen Entwicklungsdatensatzes und für die Identifizierung von Tumoren der Herkunft für scRNA-Seq von Glioblastomen überlegen ist. netSmooth hat einen freien Parameter, die Diffusionsdistanz, welche durch datengesteuerte Metriken optimiert werden kann. So kann netSmooth auch dann eingesetzt werden, wenn der optimale Diffusionsabstand nicht explizit mit Hilfe von externen Referenzdaten optimiert werden kann. Eine integrierte Analyse ist auch relevant wenn multi-omics Daten von mehrerer Omics-Protokolle auf den gleichen biologischen Proben erhoben wurden. Hierbei erklärt jeder einzelne dieser Datensätze nur einen Teil des zellulären Systems, während die gemeinsame Analyse ein vollständigeres Bild ergibt. Ich entwickelte eine Methode namens maui, um eine latente Faktordarstellungen von multiomics Daten zu finden.
The development of high throughput sequencing (HTS) was followed by a swarm of protocols utilizing HTS to measure different molecular aspects such as gene expression (transcriptome), DNA methylation (methylome) and more. This opened opportunities for developments of data analysis algorithms and procedures that consider data produced by different experiments. Considering data from seemingly unrelated experiments is particularly beneficial for Single cell RNA sequencing (scRNA-seq). scRNA-seq produces particularly noisy data, due to loss of nucleic acids when handling the small amounts in single cells, and various technical biases. To address these challenges, I developed a method called netSmooth, which de-noises and imputes scRNA-seq data by applying network diffusion over a gene network which encodes expectations of co-expression patterns. The gene network is constructed from other experimental data. Using a gene network constructed from protein-protein interactions, I show that netSmooth outperforms other state-of-the-art scRNA-seq imputation methods at the identification of blood cell types in hematopoiesis, as well as elucidation of time series data in an embryonic development dataset, and identification of tumor of origin for scRNA-seq of glioblastomas. netSmooth has a free parameter, the diffusion distance, which I show can be selected using data-driven metrics. Thus, netSmooth may be used even in cases when the diffusion distance cannot be optimized explicitly using ground-truth labels. Another task which requires in-tandem analysis of data from different experiments arises when different omics protocols are applied to the same biological samples. Analyzing such multiomics data in an integrated fashion, rather than each data type (RNA-seq, DNA-seq, etc.) on its own, is benefitial, as each omics experiment only elucidates part of an integrated cellular system. The simultaneous analysis may reveal a comprehensive view.
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Czerwińska, Urszula. "Unsupervised deconvolution of bulk omics profiles : methodology and application to characterize the immune landscape in tumors Determining the optimal number of independent components for reproducible transcriptomic data analysis Application of independent component analysis to tumor transcriptomes reveals specific and reproducible immune-related signals A multiscale signalling network map of innate immune response in cancer reveals signatures of cell heterogeneity and functional polarization." Thesis, Sorbonne Paris Cité, 2018. http://www.theses.fr/2018USPCB075.

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Les tumeurs sont entourées d'un microenvironnement complexe comprenant des cellules tumorales, des fibroblastes et une diversité de cellules immunitaires. Avec le développement actuel des immunothérapies, la compréhension de la composition du microenvironnement tumoral est d'une importance critique pour effectuer un pronostic sur la progression tumorale et sa réponse au traitement. Cependant, nous manquons d'approches quantitatives fiables et validées pour caractériser le microenvironnement tumoral, facilitant ainsi le choix de la meilleure thérapie. Une partie de ce défi consiste à quantifier la composition cellulaire d'un échantillon tumoral (appelé problème de déconvolution dans ce contexte), en utilisant son profil omique de masse (le profil quantitatif global de certains types de molécules, tels que l'ARNm ou les marqueurs épigénétiques). La plupart des méthodes existantes utilisent des signatures prédéfinies de types cellulaires et ensuite extrapolent cette information à des nouveaux contextes. Cela peut introduire un biais dans la quantification de microenvironnement tumoral dans les situations où le contexte étudié est significativement différent de la référence. Sous certaines conditions, il est possible de séparer des mélanges de signaux complexes, en utilisant des méthodes de séparation de sources et de réduction des dimensions, sans définitions de sources préexistantes. Si une telle approche (déconvolution non supervisée) peut être appliquée à des profils omiques de masse de tumeurs, cela permettrait d'éviter les biais contextuels mentionnés précédemment et fournirait un aperçu des signatures cellulaires spécifiques au contexte. Dans ce travail, j'ai développé une nouvelle méthode appelée DeconICA (Déconvolution de données omiques de masse par l'analyse en composantes immunitaires), basée sur la méthodologie de séparation aveugle de source. DeconICA a pour but l'interprétation et la quantification des signaux biologiques, façonnant les profils omiques d'échantillons tumoraux ou de tissus normaux, en mettant l'accent sur les signaux liés au système immunitaire et la découverte de nouvelles signatures. Afin de rendre mon travail plus accessible, j'ai implémenté la méthode DeconICA en tant que librairie R. En appliquant ce logiciel aux jeux de données de référence, j'ai démontré qu'il est possible de quantifier les cellules immunitaires avec une précision comparable aux méthodes de pointe publiées, sans définir a priori des gènes spécifiques au type cellulaire. DeconICA peut fonctionner avec des techniques de factorisation matricielle telles que l'analyse indépendante des composants (ICA) ou la factorisation matricielle non négative (NMF). Enfin, j'ai appliqué DeconICA à un grand volume de données : plus de 100 jeux de données, contenant au total plus de 28 000 échantillons de 40 types de tumeurs, générés par différentes technologies et traités indépendamment. Cette analyse a démontré que les signaux immunitaires basés sur l'ICA sont reproductibles entre les différents jeux de données. D'autre part, nous avons montré que les trois principaux types de cellules immunitaires, à savoir les lymphocytes T, les lymphocytes B et les cellules myéloïdes, peuvent y être identifiés et quantifiés. Enfin, les métagènes dérivés de l'ICA, c'est-à-dire les valeurs de projection associées à une source, ont été utilisés comme des signatures spécifiques permettant d'étudier les caractéristiques des cellules immunitaires dans différents types de tumeurs. L'analyse a révélé une grande diversité de phénotypes cellulaires identifiés ainsi que la plasticité des cellules immunitaires, qu'elle soit dépendante ou indépendante du type de tumeur. Ces résultats pourraient être utilisés pour identifier des cibles médicamenteuses ou des biomarqueurs pour l'immunothérapie du cancer
Tumors are engulfed in a complex microenvironment (TME) including tumor cells, fibroblasts, and a diversity of immune cells. Currently, a new generation of cancer therapies based on modulation of the immune system response is in active clinical development with first promising results. Therefore, understanding the composition of TME in each tumor case is critically important to make a prognosis on the tumor progression and its response to treatment. However, we lack reliable and validated quantitative approaches to characterize the TME in order to facilitate the choice of the best existing therapy. One part of this challenge is to be able to quantify the cellular composition of a tumor sample (called deconvolution problem in this context), using its bulk omics profile (global quantitative profiling of certain types of molecules, such as mRNA or epigenetic markers). In recent years, there was a remarkable explosion in the number of methods approaching this problem in several different ways. Most of them use pre-defined molecular signatures of specific cell types and extrapolate this information to previously unseen contexts. This can bias the TME quantification in those situations where the context under study is significantly different from the reference. In theory, under certain assumptions, it is possible to separate complex signal mixtures, using classical and advanced methods of source separation and dimension reduction, without pre-existing source definitions. If such an approach (unsupervised deconvolution) is feasible to apply for bulk omic profiles of tumor samples, then this would make it possible to avoid the above mentioned contextual biases and provide insights into the context-specific signatures of cell types. In this work, I developed a new method called DeconICA (Deconvolution of bulk omics datasets through Immune Component Analysis), based on the blind source separation methodology. DeconICA has an aim to decipher and quantify the biological signals shaping omics profiles of tumor samples or normal tissues. A particular focus of my study was on the immune system-related signals and discovering new signatures of immune cell types. In order to make my work more accessible, I implemented the DeconICA method as an R package named "DeconICA". By applying this software to the standard benchmark datasets, I demonstrated that DeconICA is able to quantify immune cells with accuracy comparable to published state-of-the-art methods but without a priori defining a cell type-specific signature genes. The implementation can work with existing deconvolution methods based on matrix factorization techniques such as Independent Component Analysis (ICA) or Non-Negative Matrix Factorization (NMF). Finally, I applied DeconICA to a big corpus of data containing more than 100 transcriptomic datasets composed of, in total, over 28000 samples of 40 tumor types generated by different technologies and processed independently. This analysis demonstrated that ICA-based immune signals are reproducible between datasets and three major immune cell types: T-cells, B-cells and Myeloid cells can be reliably identified and quantified. Additionally, I used the ICA-derived metagenes as context-specific signatures in order to study the characteristics of immune cells in different tumor types. The analysis revealed a large diversity and plasticity of immune cells dependent and independent on tumor type. Some conclusions of the study can be helpful in identification of new drug targets or biomarkers for immunotherapy of cancer
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CAPORALE, NICOLO'. "A UNIFYING FRAMEWORK TO STUDY THE GENETIC AND ENVIRONMENTAL FACTORS SHAPING HUMAN BRAIN DEVELOPMENT." Doctoral thesis, Università degli Studi di Milano, 2020. http://hdl.handle.net/2434/697871.

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The development of human brain is a fascinating and complex process that still needs to be uncovered at the molecular resolution. Even though animal studies have revealed a lot of its unfolding, the fine regulation of cellular differentiation trajectories that characterizes humans has become only recently open to experimental tractability, thanks to the development of organoids, human cellular models that are able to recapitulate the spatiotemporal architecture of the brain in a 3D fashion. Here we first benchmarked human brain organoids at the level of transcriptomic and structural architecture of cell composition along several stages of differentiation. Then we harnessed their properties to probe the longitudinal impact of GSK3 on human corticogenesis, a pivotal regulator of both proliferation and polarity, that we revealed having a direct impact on early neurogenesis with a selective role in the regulation of glutamatergic lineages and outer radial glia output. Moreover, we spearheaded the use of organoids for regulatory toxicology through the study of Endocrine disrupting chemicals (EDC), pervasive compounds that can interfere with human hormonal systems. Early life exposure to EDC is associated with human disorders, but the molecular events triggered remain unknown. We developed a novel approach, integrating epidemiological with experimental biology to study the mixtures of EDC that were associated with neurodevelopmental and metabolic adverse effects in the biggest pregnancy cohort profiled so far. Our experiments were carried out on two complementary models i) human fetal primary neural stem cells, and ii) 3-dimensional cortical brain organoids and we identified the genes specifically dysregulated by EDC mixture exposure, unravelling a significant enrichment for autism spectrum disorders causative genes, thereby proposing a convergent paradigm of neurodevelopmental disorders pathophysiology between genetic and environmental factors. Finally, while EDCs are everywhere, their impact on adverse health outcomes can vary substantially among individuals, suggesting that other genetic factors may play a pivotal role for the onset of the disorders. We took advantage of organoids multiplexing to recapitulate, at the same time, neurodevelopmental trajectories on multiple genetic backgrounds, and showed that chimeric organoids preserved the overall morphological organization and transcriptomic signatures of the ones generated from single lines. In conclusion our work shows the possibility to perform population level studies in vitro and use the deep resolution of molecular biology to dissect key aspects of human neurodevelopment.
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Books on the topic "Single-Cell omics"

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Sweedler, Jonathan V., James Eberwine, and Scott E. Fraser, eds. Single Cell ‘Omics of Neuronal Cells. New York, NY: Springer US, 2022. http://dx.doi.org/10.1007/978-1-0716-2525-5.

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Single-Cell Omics. Elsevier, 2019. http://dx.doi.org/10.1016/c2017-0-02420-5.

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Single-Cell Omics. Elsevier, 2019. http://dx.doi.org/10.1016/c2018-0-02201-x.

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Pan, Xinghua, Shixiu Wu, and Sherman M. Weissman, eds. Introduction to Single Cell Omics. Frontiers Media SA, 2019. http://dx.doi.org/10.3389/978-2-88945-920-9.

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Eberwine, James, Jonathan V. Sweedler, and Scott E. Fraser. Single Cell 'Omics of Neuronal Cells. Springer, 2022.

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Single Cell 'Omics of Neuronal Cells. Springer, 2023.

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Barh, Debmalya, and Vasco Azevedo. Single-Cell Omics: Technological Advances and Applications. Elsevier Science & Technology, 2019.

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Menon, Swapna. Single Cell Sequencing Essentials in Brief: Single Cell RNA Sequencing and Orthogonal Omics Technologies. Independently Published, 2021.

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Barh, Debmalya, and Vasco Azevedo. Single-Cell Omics : Volume 2: Technological Advances and Applications. Elsevier Science & Technology Books, 2019.

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Barh, Debmalya, and Vasco Azevedo. Single-Cell Omics : Volume 1: Technological Advances and Applications. Elsevier Science & Technology, 2019.

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Book chapters on the topic "Single-Cell omics"

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Lynch, Mark, and Naveen Ramalingam. "Integrated Fluidic Circuits for Single-Cell Omics and Multi-omics Applications." In Single Molecule and Single Cell Sequencing, 19–26. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6037-4_2.

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Wang, Jingshu, and Tianyu Chen. "Deep Learning Methods for Single-Cell Omics Data." In Springer Handbooks of Computational Statistics, 109–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 2022. http://dx.doi.org/10.1007/978-3-662-65902-1_6.

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Wang, Xinjun, Haoran Hu, and Wei Chen. "Model-Based Clustering of Single-Cell Omics Data." In Springer Handbooks of Computational Statistics, 85–108. Berlin, Heidelberg: Springer Berlin Heidelberg, 2022. http://dx.doi.org/10.1007/978-3-662-65902-1_5.

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Demetçi, Pınar, Rebecca Santorella, Björn Sandstede, and Ritambhara Singh. "Unsupervised Integration of Single-Cell Multi-omics Datasets with Disproportionate Cell-Type Representation." In Lecture Notes in Computer Science, 3–19. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04749-7_1.

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Han, Maozhen, Pengshuo Yang, Hao Zhou, Hongjun Li, and Kang Ning. "Metagenomics and Single-Cell Omics Data Analysis for Human Microbiome Research." In Advances in Experimental Medicine and Biology, 117–37. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-1503-8_6.

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Chau, Tran, Prakash Timilsena, and Song Li. "Gene Regulatory Network Modeling Using Single-Cell Multi-Omics in Plants." In Methods in Molecular Biology, 259–75. New York, NY: Springer US, 2023. http://dx.doi.org/10.1007/978-1-0716-3354-0_16.

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Li, Yue, Gregory Fonseca, and Jun Ding. "Multimodal Methods for Knowledge Discovery from Bulk and Single-Cell Multi-Omics Data." In Machine Learning Methods for Multi-Omics Data Integration, 39–74. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-36502-7_4.

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Misra, Biswapriya B. "A Workflow in Single Cell-Type Metabolomics: From Data Pre-Processing and Statistical Analysis to Biological Insights." In OMICS-Based Approaches in Plant Biotechnology, 105–27. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2019. http://dx.doi.org/10.1002/9781119509967.ch6.

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Lan, Wei, Shengzu Huang, Xun Sun, Haibo Liao, Qingfeng Chen, and Junyue Cao. "Single-Cell Multi-omics Clustering Algorithm Based on Adaptive Weighted Hyper-laplacian Regularization." In Bioinformatics Research and Applications, 373–82. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-5131-0_32.

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Lin, Zhixiang. "Integrative Analyses of Single-Cell Multi-Omics Data: A Review from a Statistical Perspective." In Springer Handbooks of Computational Statistics, 53–69. Berlin, Heidelberg: Springer Berlin Heidelberg, 2022. http://dx.doi.org/10.1007/978-3-662-65902-1_3.

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Conference papers on the topic "Single-Cell omics"

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Li, Xiaoli, Rui Zhang, Saba Aslam, Huijun Li, Yuxi Chen, Zequn Zhang, Ruey-Song Huang, and Hongyan Wu. "scMonica: Single-cell Mosaic Omics Nonlinear Integration and Clustering Analysis." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 1579–83. IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10822866.

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Taha, Manar H., Mohamed El-Hadidi, and Sahar Ali Fawzi. "Deep Learning Applications in Single-Cell Multi-Omics Analysis: A Review." In 2024 6th Novel Intelligent and Leading Emerging Sciences Conference (NILES), 85–88. IEEE, 2024. http://dx.doi.org/10.1109/niles63360.2024.10753202.

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Pang, Shanchen, Jiarui Wu, Wenhao Wu, Hengxiao Li, Ruiqian Wang, Yulin Zhang, and Shudong Wang. "scKADE: Single-Cell Multi-Omics Integration with Kolmogorov-Arnold Deep Embedding." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 633–38. IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10822086.

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Li, Jiawei, Shizhan Chen, Zongbo Han, Wei Li, Jijun Tang, and Fei Guo. "Multi-Task Driven Multi-Level Dynamical Fusion for Single-Cell Multi-Omics Cell Type Annotation." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 1009–14. IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10822524.

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Li, Enling, Lin Gao, and Yusen Ye. "CellFeature: Cell and Feature Co-Embedding from Single-Cell Multi-Omics with Heterogeneous Graph Model." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 976–81. IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10821837.

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Wolfgang, Seth, Skyler Ruiter, Marc Tunnell, Timothy Triche, Erin Carrier, and Zachary DeBruine. "Value-Compressed Sparse Column (VCSC): Sparse Matrix Storage for Single-cell Omics Data." In 2024 IEEE International Conference on Big Data (BigData), 4952–58. IEEE, 2024. https://doi.org/10.1109/bigdata62323.2024.10825091.

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Zhao, Zhongming. "Session details: Single cell omics." In BCB '21: 12th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3478669.

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Singh, Ritambhara, Pinar Demetci, Giancarlo Bonora, Vijay Ramani, Choli Lee, He Fang, Zhijun Duan, et al. "Unsupervised manifold alignment for single-cell multi-omics data." In BCB '20: 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3388440.3412410.

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Prabhala, P., S. Lang, S. Wijk, R. Cattani, K. Kanzenbach, K. Malmros, S. Soneji, et al. "Characterizing Molecular Targets in Lung Squamous Cell Carcinoma Using Single Cell Omics." In American Thoracic Society 2024 International Conference, May 17-22, 2024 - San Diego, CA. American Thoracic Society, 2024. http://dx.doi.org/10.1164/ajrccm-conference.2024.209.1_meetingabstracts.a4910.

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Do, Van Hoan, and Stefan Canzar. "Identifying Cell Types in Single-Cell Multimodal Omics Data via Joint Embedding Learning." In 2023 15th International Conference on Knowledge and Systems Engineering (KSE). IEEE, 2023. http://dx.doi.org/10.1109/kse59128.2023.10299517.

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Reports on the topic "Single-Cell omics"

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Wang, Daojing, and Steven Bodovitz. Single cell analysis: the new frontier in 'Omics'. Office of Scientific and Technical Information (OSTI), January 2010. http://dx.doi.org/10.2172/983315.

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