Relevant bibliographies by topics / Single cell omic

Academic literature on the topic 'Single cell omic'

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Published: 9 March 2023
Last updated: 10 March 2023

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Journal articles on the topic "Single cell omic"

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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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Gao, Chao, Jialin Liu, April R. Kriebel, Sebastian Preissl, Chongyuan Luo, Rosa Castanon, Justin Sandoval, et al. "Iterative single-cell multi-omic integration using online learning." Nature Biotechnology 39, no. 8 (April 19, 2021): 1000–1007. http://dx.doi.org/10.1038/s41587-021-00867-x.

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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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Glass, David R., Albert G. Tsai, John Paul Oliveria, Felix J. Hartmann, Samuel C. Kimmey, Ariel A. Calderon, Luciene Borges, et al. "An Integrated Multi-omic Single-Cell Atlas of Human B Cell Identity." Immunity 53, no. 1 (July 2020): 217–32. http://dx.doi.org/10.1016/j.immuni.2020.06.013.

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Regner, Matthew J., Kamila Wisniewska, Susana Garcia-Recio, Aatish Thennavan, Raul Mendez-Giraldez, Venkat S. Malladi, Gabrielle Hawkins, et al. "A multi-omic single-cell landscape of human gynecologic malignancies." Molecular Cell 81, no. 23 (December 2021): 4924–41. http://dx.doi.org/10.1016/j.molcel.2021.10.013.

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Mannello, Ferdinando, Daniela Ligi, and Mauro Magnani. "Deciphering the single-cell omic: innovative application for translational medicine." Expert Review of Proteomics 9, no. 6 (December 2012): 635–48. http://dx.doi.org/10.1586/epr.12.61.

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Yang, Ming-Chao, Zi-Chen Wu, Liang-Liang Huang, Farhat Abbas, and Hui-Cong Wang. "Systematic Methods for Isolating High Purity Nuclei from Ten Important Plants for Omics Interrogation." Cells 11, no. 23 (December 3, 2022): 3919. http://dx.doi.org/10.3390/cells11233919.

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Recent advances in developmental biology have been made possible by using multi-omic studies at single cell resolution. However, progress in plants has been slowed, owing to the tremendous difficulty in protoplast isolation from most plant tissues and/or oversize protoplasts during flow cytometry purification. Surprisingly, rapid innovations in nucleus research have shed light on plant studies in single cell resolution, which necessitates high quality and efficient nucleus isolation. Herein, we present efficient nuclei isolation protocols from the leaves of ten important plants including Arabidopsis, rice, maize, tomato, soybean, banana, grape, citrus, apple, and litchi. We provide a detailed procedure for nucleus isolation, flow cytometry purification, and absolute nucleus number quantification. The nucleus isolation buffer formula of the ten plants tested was optimized, and the results indicated a high nuclei yield. Microscope observations revealed high purity after flow cytometry sorting, and the DNA and RNA quality extract from isolated nuclei were monitored by using the nuclei in cell division cycle and single nucleus RNA sequencing (snRNA-seq) studies, with detailed procedures provided. The findings indicated that nucleus yield and quality meet the requirements of snRNA-seq, cell division cycle, and likely other omic studies. The protocol outlined here makes it feasible to perform plant omic studies at single cell resolution.
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Welch, Joshua D., Velina Kozareva, Ashley Ferreira, Charles Vanderburg, Carly Martin, and Evan Z. Macosko. "Single-Cell Multi-omic Integration Compares and Contrasts Features of Brain Cell Identity." Cell 177, no. 7 (June 2019): 1873–87. http://dx.doi.org/10.1016/j.cell.2019.05.006.

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Gayoso, Adam, Zoë Steier, Romain Lopez, Jeffrey Regier, Kristopher L. Nazor, Aaron Streets, and Nir Yosef. "Joint probabilistic modeling of single-cell multi-omic data with totalVI." Nature Methods 18, no. 3 (February 15, 2021): 272–82. http://dx.doi.org/10.1038/s41592-020-01050-x.

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Sukovich, David J., Sarah E. B. Taylor, Katherine A. Pfeiffer, Michael J. T. Stubbington, Josephine Y. Lee, Jerald Sapida, Daniel P. Roidan, et al. "An advancement in single cell genomics allows for T cell population analysis at high resolution." Journal of Immunology 202, no. 1_Supplement (May 1, 2019): 131.13. http://dx.doi.org/10.4049/jimmunol.202.supp.131.13.

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Abstract Progress in our understanding of immunology and cancer immunotherapies requires a comprehensive view of immune cell behavior and the interactions of these cells with their environment. Recent technological innovations have facilitated the combination of cell-surface protein, transcriptome, immune repertoire, and antigen specificity measurements from the same single cells, providing thorough and high-throughput lymphocyte characterization. Using the 10x Genomics Single Cell Immune Profiling Solution with Feature Barcoding technology along with oligo-conjugated antibodies and peptide-MHC (pMHC) Dextramers®, we performed multi-omic characterization of PBMCs from cytomegalovirus (CMV) seronegative and seropositive patients. Next generation sequencing libraries were made following the 10x Genomics workflows, where transcriptome and immune repertoire libraries are generated alongside libraries from DNA barcodes conjugated to antibodies or pMHC. Full length, paired TCRα/β sequences with specificity to known CMV antigens were identified in the seropositive donor, but not in the seronegative donor. Interestingly, a large Epstein Barr Virus (EBV) pMHC specific T cell expansion was identified in the CMV seronegative donor, suggesting an active EBV response. Moreover, the combination of transcriptomic and cell surface protein information resulted in an increase in resolution of cell type identification. This multi-omic workflow allowed the identification of enriched amino acid motifs within the TCR sequences that contained novel and known CDR3 amino acid sequences specific to CMV. These technological advancements provide new biological insights that are critical for progress in the field.
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Dissertations / Theses on the topic "Single cell omic"

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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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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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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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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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Books on the topic "Single cell omic"

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

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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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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 1: Technological Advances and Applications. Elsevier Science & Technology, 2019.

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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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Single-Cell Omics : Volume 2: Applications in Biomedicine and Agriculture. Elsevier Science & Technology, 2019.

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Book chapters on the topic "Single cell omic"

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Li, Chen, Maria Virgilio, Kathleen L. Collins, and Joshua D. Welch. "Single-Cell Multi-omic Velocity Infers Dynamic and Decoupled Gene Regulation." In Lecture Notes in Computer Science, 297–99. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04749-7_18.

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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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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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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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Dilshad, Erum, Amna Naheed Khan, Iqra Bashir, Muhammad Maaz, Maria Shabbir, and Marriam Bakhtiar. "Single Cell Omics." In Omics Technologies for Clinical Diagnosis and Gene Therapy: Medical Applications in Human Genetics, 156–73. BENTHAM SCIENCE PUBLISHERS, 2022. http://dx.doi.org/10.2174/9789815079517122010013.

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Recent advances are nowadays providing opportunities to examine the complexities of organs and organisms at the single-cell level. The conventional cell.based analysis mainly examines the cellular processes from the bulk of cells but single.cell omics provides a more detailed insight into individual cell phenotypes, thus giving a link between the phenotype and genotype of cells. Single-cell analysis can be performed at genome, epigenome, transcriptome, proteome and metabolome levels and thus makes it possible to come across mechanisms not seen during the sequencing of bulk tissues. Researchers need to isolate single cells before the initiation of single-cell analysis. For this, various strategies like FACS, MACS, LCM, micro-manipulation and micro-fluids are used for cell isolation depending upon their physical properties and cellular biological characteristics. The analysis of single-cell data at multiple levels gives us an unusual view of multilevel transformation at the single-cell level and thus providing a better chance to discover novel biological processes. High throughput analysis of single cells at genome, transcriptome and proteome levels provides unique and important insights into cell variability and diverse processes like development, genetic expressions and severity of different symptoms in disease pathogenesis.
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Winograd, Paul, Benjamin DiPardo, Colin M. Court, Shonan Sho, and James S. Tomlinson. "Single-Cell Omics: Circulating Tumor Cells." In Single-Cell Omics, 37–54. Elsevier, 2019. http://dx.doi.org/10.1016/b978-0-12-817532-3.00003-7.

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Conference papers on the topic "Single cell omic"

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Zappasodi, Roberta, Lydia Mok student, Andrea Orlando, Julian Lehrer, Joshua Stuart, Nils-Petter Rudqvist, Benjamin Vincent, et al. "9 A pan-cancer multi-omic immune single-cell atlas for cancer immunotherapy: focus on CD4+ T cells." In SITC 37th Annual Meeting (SITC 2022) Abstracts. BMJ Publishing Group Ltd, 2022. http://dx.doi.org/10.1136/jitc-2022-sitc2022.0009.

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Shi, Wenge, Christian Laing, Jane Gao, Kerri Burns, Shyam Sarikonda, Reinhold Pollner, and Hua Gong. "Abstract 4290: Multi-omic single cell sequencing for deep cell immune profiling and identification of potential biomarkers for cell therapy and immunotherapy." In Proceedings: AACR Annual Meeting 2020; April 27-28, 2020 and June 22-24, 2020; Philadelphia, PA. American Association for Cancer Research, 2020. http://dx.doi.org/10.1158/1538-7445.am2020-4290.

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Beaumont, Kristin G., Austin Hake, Ying-Chih Wang, Hardik Shah, Kimaada Allette, Wissam Hamou, Arpit Dave, et al. "Abstract PR10: High-throughput functional and multi-omic single-cell characterization to elucidate ovarian intratumor and microenvironmental heterogeneity." In Abstracts: AACR Special Conference on Advances in Ovarian Cancer Research; September 13-16, 2019; Atlanta, GA. American Association for Cancer Research, 2020. http://dx.doi.org/10.1158/1557-3265.ovca19-pr10.

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Khan, Yasef, Francisco Ramirez, Shobha Gokul, Lawrence Manzano, Louis Leong, Gary J. Latham, and Chris Heger. "Abstract 6296: Multiplexed protein and RNA quantification on a single instrument harmonizes multi-omic analyses of biomarkers for immunotherapies and targeted therapies in non-small cell lung cancer." In Proceedings: AACR Annual Meeting 2020; April 27-28, 2020 and June 22-24, 2020; Philadelphia, PA. American Association for Cancer Research, 2020. http://dx.doi.org/10.1158/1538-7445.am2020-6296.

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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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Occhipinti, Annalisa, and Claudio Angione. "A Computational Model of Cancer Metabolism for Personalised Medicine." In Building Bridges in Medical Science 2021. Cambridge Medicine Journal, 2021. http://dx.doi.org/10.7244/cmj.2021.03.001.3.

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Cancer cells must rewrite their ‘‘internal code’’ to satisfy the demand for growth and proliferation. Such changes are driven by a combination of genetic (e.g., genes’ mutations) and non-genetic factors (e.g., tumour microenvironment) that result in an alteration of cellular metabolism. For this reason, understanding the metabolic and genomic changes of a cancer cell can provide useful insight on cancer progression and survival outcomes. In our work, we present a computational framework that uses patient-specific data to investigate cancer metabolism and provide personalised survival predictions and cancer development outcomes. The proposed model integrates patient-specific multi-omics data (i.e., genomic, metabolomic and clinical data) into a metabolic model of cancer to produce a list of metabolic reactions affecting cancer progression. Quantitative and predictive analysis, through survival analysis and machine learning techniques, is then performed on the list of selected reactions. Since our model performs an analysis of patient-specific data, the outcome of our pipeline provides a personalised prediction of survival outcome and cancer development based on a subset of identified multi-omics features (genomic, metabolomic and clinical data). In particular, our work aims to develop a computational pipeline for clinicians that relates the omic profile of each patient to their survival probability, based on a combination of machine learning and metabolic modelling techniques. The model provides patient-specific predictions on cancer development and survival outcomes towards the development of personalised medicine.
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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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Landau, Dan A. "Abstract IA12: Single cell multi-omics to define normal and malignant differentiation topologies." In Abstracts: AACR Virtual Special Conference on Tumor Heterogeneity: From Single Cells to Clinical Impact; September 17-18, 2020. American Association for Cancer Research, 2020. http://dx.doi.org/10.1158/1538-7445.tumhet2020-ia12.

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Peng, Tao, Kamyar Esmaeili Pourfarhangi, and Kai Tan. "Abstract PO-026: GLUER: integrative analysis of multi-omics data at single-cell resolution." In Abstracts: AACR Virtual Special Conference on Tumor Heterogeneity: From Single Cells to Clinical Impact; September 17-18, 2020. American Association for Cancer Research, 2020. http://dx.doi.org/10.1158/1538-7445.tumhet2020-po-026.

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Gaiti, Federico, Ronan Chaligne, Dana Silverbush, Joshua S. Schiffman, Hannah R. Weisman, Lloyd Kluegel, Simon Gritsch, et al. "Abstract PO-019: Deciphering differentiation hierarchies, heritability and plasticity in human gliomas via single-cell multi-omics." In Abstracts: AACR Virtual Special Conference on Tumor Heterogeneity: From Single Cells to Clinical Impact; September 17-18, 2020. American Association for Cancer Research, 2020. http://dx.doi.org/10.1158/1538-7445.tumhet2020-po-019.

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Reports on the topic "Single cell omic"

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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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