Добірка наукової літератури з теми "Single cell sequencing data"
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Статті в журналах з теми "Single cell sequencing data"
Shi, Qianqian, Xinxing Li, Qirui Peng, Chuanchao Zhang, and Luonan Chen. "scDA: Single cell discriminant analysis for single-cell RNA sequencing data." Computational and Structural Biotechnology Journal 19 (2021): 3234–44. http://dx.doi.org/10.1016/j.csbj.2021.05.046.
Повний текст джерелаZhao, Xinlei, Shuang Wu, Nan Fang, Xiao Sun, and Jue Fan. "Evaluation of single-cell classifiers for single-cell RNA sequencing data sets." Briefings in Bioinformatics 21, no. 5 (October 23, 2019): 1581–95. http://dx.doi.org/10.1093/bib/bbz096.
Повний текст джерелаSatas, Gryte, and Benjamin J. Raphael. "Haplotype phasing in single-cell DNA-sequencing data." Bioinformatics 34, no. 13 (June 27, 2018): i211—i217. http://dx.doi.org/10.1093/bioinformatics/bty286.
Повний текст джерелаVallejos, Catalina A., John C. Marioni, and Sylvia Richardson. "BASiCS: Bayesian Analysis of Single-Cell Sequencing Data." PLOS Computational Biology 11, no. 6 (June 24, 2015): e1004333. http://dx.doi.org/10.1371/journal.pcbi.1004333.
Повний текст джерелаSchnepp, Patricia M., Mengjie Chen, Evan T. Keller, and Xiang Zhou. "SNV identification from single-cell RNA sequencing data." Human Molecular Genetics 28, no. 21 (August 27, 2019): 3569–83. http://dx.doi.org/10.1093/hmg/ddz207.
Повний текст джерелаGisina, Alisa, Irina Kholodenko, Yan Kim, Maxim Abakumov, Alexey Lupatov, and Konstantin Yarygin. "Glioma Stem Cells: Novel Data Obtained by Single-Cell Sequencing." International Journal of Molecular Sciences 23, no. 22 (November 17, 2022): 14224. http://dx.doi.org/10.3390/ijms232214224.
Повний текст джерелаDai, Hao, Lin Li, Tao Zeng, and Luonan Chen. "Cell-specific network constructed by single-cell RNA sequencing data." Nucleic Acids Research 47, no. 11 (March 13, 2019): e62-e62. http://dx.doi.org/10.1093/nar/gkz172.
Повний текст джерелаZhang, Yinan, Xiaowei Xie, Peng Wu, and Ping Zhu. "SIEVE: identifying robust single cell variable genes for single-cell RNA sequencing data." Blood Science 3, no. 2 (April 2021): 35–39. http://dx.doi.org/10.1097/bs9.0000000000000072.
Повний текст джерелаMyers, Matthew A., Simone Zaccaria, and Benjamin J. Raphael. "Identifying tumor clones in sparse single-cell mutation data." Bioinformatics 36, Supplement_1 (July 1, 2020): i186—i193. http://dx.doi.org/10.1093/bioinformatics/btaa449.
Повний текст джерелаZhao, Peng, Zenglin Xu, Junjie Chen, Yazhou Ren, and Irwin King. "Single Cell Self-Paced Clustering with Transcriptome Sequencing Data." International Journal of Molecular Sciences 23, no. 7 (March 31, 2022): 3900. http://dx.doi.org/10.3390/ijms23073900.
Повний текст джерелаДисертації з теми "Single cell sequencing data"
Ross, Edith. "Inferring tumour evolution from single-cell and multi-sample data." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/274604.
Повний текст джерелаSalehi, Sohrab. "dd-PyClone : improving clonal subpopulation inference from single cells and bulk sequencing data." Thesis, University of British Columbia, 2015. http://hdl.handle.net/2429/56179.
Повний текст джерелаScience, Faculty of
Graduate
Lavagi, Ilaria Verfasser], and Eckhard [Akademischer Betreuer] [Wolf. "Analysis of blastomere of bovine embryos during genome activation by evaluation of single-cell RNA sequencing data / Ilaria Lavagi ; Betreuer: Eckhard Wolf." München : Universitätsbibliothek der Ludwig-Maximilians-Universität, 2018. http://d-nb.info/1167160541/34.
Повний текст джерелаBampalikis, Dimitrios. "Recognizing biological and technical differences in scRNAseq : A comparison of two protocols." Thesis, Uppsala universitet, Institutionen för biologisk grundutbildning, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-366169.
Повний текст джерелаRonen, Jonathan. "Integrative analysis of data from multiple experiments." Doctoral thesis, Humboldt-Universität zu Berlin, 2020. http://dx.doi.org/10.18452/21612.
Повний текст джерелаThe 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.
Büttner, Maren [Verfasser], Fabian J. [Akademischer Betreuer] Theis, Julien [Gutachter] Gagneur, Fabian J. [Gutachter] Theis, and Peter V. [Gutachter] Kharchenko. "Statistical data integration for single-cell RNA-sequencing - batch effect correction and lineage inference / Maren Büttner ; Gutachter: Julien Gagneur, Fabian J. Theis, Peter V. Kharchenko ; Betreuer: Fabian J. Theis." München : Universitätsbibliothek der TU München, 2019. http://d-nb.info/119244194X/34.
Повний текст джерелаJohnson, Travis Steele. "Integrative approaches to single cell RNA sequencing analysis." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1586960661272666.
Повний текст джерелаBorgström, Erik. "Technologies for Single Cell Genome Analysis." Doctoral thesis, KTH, Genteknologi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-181059.
Повний текст джерелаQC 20160127
Raoux, Corentin. "Review and Analysis of single-cell RNA sequencing cell-type identification and annotation tools." Thesis, KTH, Skolan för kemi, bioteknologi och hälsa (CBH), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-297852.
Повний текст джерелаKindblom, Marie, and Hakim Ezeddin Al. "Phylogenetic fatemapping: estimating allelic dropout probability in single cell genomic sequencing." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-186453.
Повний текст джерелаКниги з теми "Single cell sequencing data"
Suzuki, Yutaka, ed. Single Molecule and Single Cell Sequencing. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6037-4.
Повний текст джерелаYu, Buwei, Jiaqiang Zhang, Yiming Zeng, Li Li, and Xiangdong Wang, eds. Single-cell Sequencing and Methylation. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4494-1.
Повний текст джерелаWang, Xiangdong, ed. Single Cell Sequencing and Systems Immunology. Dordrecht: Springer Netherlands, 2015. http://dx.doi.org/10.1007/978-94-017-9753-5.
Повний текст джерелаLoos, Carolin. Analysis of Single-Cell Data. Wiesbaden: Springer Fachmedien Wiesbaden, 2016. http://dx.doi.org/10.1007/978-3-658-13234-7.
Повний текст джерелаYuan, Guo-Cheng, ed. Computational Methods for Single-Cell Data Analysis. New York, NY: Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4939-9057-3.
Повний текст джерелаMallick, Himel, Lingling An, Mengjie Chen, Pei Wang, and Ni Zhao, eds. Methods for Single-Cell and Microbiome Sequencing Data. Frontiers Media SA, 2022. http://dx.doi.org/10.3389/978-2-88976-280-4.
Повний текст джерелаChen, Geng, Zhichao Liu, and Cheng Peng, eds. Multimodal and Integrative Analysis of Single-Cell or Bulk Sequencing Data. Frontiers Media SA, 2021. http://dx.doi.org/10.3389/978-2-88966-668-3.
Повний текст джерелаYang, Jialiang, Liao Bo, Tuo Zhang, and Yifei Xu, eds. Bioinformatics Analysis of Single Cell Sequencing Data and Applications in Precision Medicine. Frontiers Media SA, 2020. http://dx.doi.org/10.3389/978-2-88963-528-3.
Повний текст джерелаSuzuki, Yutaka. Single Molecule and Single Cell Sequencing. Springer, 2019.
Знайти повний текст джерелаWang, Xiangdong. Single Cell Sequencing and Systems Immunology. Springer, 2016.
Знайти повний текст джерелаЧастини книг з теми "Single cell sequencing data"
Gao, Shan. "Data Analysis in Single-Cell Transcriptome Sequencing." In Methods in Molecular Biology, 311–26. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-7717-8_18.
Повний текст джерелаSagar, Josip Stefan Herman, John Andrew Pospisilik, and Dominic Grün. "High-Throughput Single-Cell RNA Sequencing and Data Analysis." In Methods in Molecular Biology, 257–83. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-7768-0_15.
Повний текст джерелаVermeersch, Lieselotte, Abbas Jariani, Jana Helsen, Benjamin M. Heineike, and Kevin J. Verstrepen. "Single-Cell RNA Sequencing in Yeast Using the 10× Genomics Chromium Device." In Methods in Molecular Biology, 3–20. New York, NY: Springer US, 2022. http://dx.doi.org/10.1007/978-1-0716-2257-5_1.
Повний текст джерелаTran, Duc, Frederick C. Harris, Bang Tran, Nam Sy Vo, Hung Nguyen, and Tin Nguyen. "Single-Cell RNA Sequencing Data Imputation Using Deep Neural Network." In Advances in Intelligent Systems and Computing, 403–10. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70416-2_52.
Повний текст джерелаWang, Zuoheng, and Xiting Yan. "Computational and Statistical Methods for Single-Cell RNA Sequencing Data." In Springer Handbooks of Computational Statistics, 3–35. Berlin, Heidelberg: Springer Berlin Heidelberg, 2022. http://dx.doi.org/10.1007/978-3-662-65902-1_1.
Повний текст джерелаSagar and Dominic Grün. "Lineage Inference and Stem Cell Identity Prediction Using Single-Cell RNA-Sequencing Data." In Computational Stem Cell Biology, 277–301. New York, NY: Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4939-9224-9_13.
Повний текст джерелаUlbrich, Jannes, Vadir Lopez-Salmeron, and Ian Gerrard. "BD Rhapsody™ Single-Cell Analysis System Workflow: From Sample to Multimodal Single-Cell Sequencing Data." In Methods in Molecular Biology, 29–56. New York, NY: Springer US, 2022. http://dx.doi.org/10.1007/978-1-0716-2756-3_2.
Повний текст джерелаLei, Haoyun, Bochuan Lyu, E. Michael Gertz, Alejandro A. Schäffer, Xulian Shi, Kui Wu, Guibo Li, et al. "Tumor Copy Number Deconvolution Integrating Bulk and Single-Cell Sequencing Data." In Lecture Notes in Computer Science, 174–89. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-17083-7_11.
Повний текст джерелаLi, Ronnie Y., Wenjing Ma, and Zhaohui S. Qin. "Approaches to Marker Gene Identification from Single-Cell RNA-Sequencing Data." In Springer Handbooks of Computational Statistics, 71–84. Berlin, Heidelberg: Springer Berlin Heidelberg, 2022. http://dx.doi.org/10.1007/978-3-662-65902-1_4.
Повний текст джерелаBahonar, Sajedeh, and Hesam Montazeri. "Somatic Single-Nucleotide Variant Calling from Single-Cell DNA Sequencing Data Using SCAN-SNV." In Variant Calling, 267–77. New York, NY: Springer US, 2022. http://dx.doi.org/10.1007/978-1-0716-2293-3_17.
Повний текст джерелаТези доповідей конференцій з теми "Single cell sequencing data"
Ciccolella, Simone, Murray D. Patterson, Paola Bonizzoni, and Gianluca Della Vedova. "Effective Clustering for Single Cell Sequencing Cancer Data." In BCB '19: 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3307339.3342149.
Повний текст джерелаZare, Fatima, Jacob Stark, and Sheida Nabavi. "Copy number variation detection using single cell sequencing data." In BCB '21: 12th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3459930.3469556.
Повний текст джерелаZhang, Wenjuan, William Yang, John Talburt, Sherman Weissman, and Mary Qu Yang. "Missing Value Recovery for Single Cell RNA Sequencing Data." In 2021 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 2021. http://dx.doi.org/10.1109/csci54926.2021.00129.
Повний текст джерелаTasoulis, Sotiris K., Aristidis G. Vrahatis, Spiros V. Georgakopoulos, and Vassilis P. Plagianakos. "Visualizing High-dimensional single-cell RNA-sequencing data through multiple Random Projections." In 2018 IEEE International Conference on Big Data (Big Data). IEEE, 2018. http://dx.doi.org/10.1109/bigdata.2018.8622170.
Повний текст джерелаVrahatis, Aristidis G., Georgios N. Dimitrakopoulos, Sotiris K. Tasoulis, Spiros V. Georgakopoulos, and Vassilis P. Plagianakos. "Single-cell regulatory network inference and clustering from high-dimensional sequencing data." In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019. http://dx.doi.org/10.1109/bigdata47090.2019.9006016.
Повний текст джерелаWang, Tianyu, and Sheida Nabavi. "Differential gene expression analysis in single-cell RNA sequencing data." In 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2017. http://dx.doi.org/10.1109/bibm.2017.8217650.
Повний текст джерелаBai, Litai, Yuan Zhu, and Ming Yi. "Clustering Single-Cell RNA Sequencing Data by Deep Learning Algorithm." In 2021 IEEE 9th International Conference on Bioinformatics and Computational Biology (ICBCB). IEEE, 2021. http://dx.doi.org/10.1109/icbcb52223.2021.9459219.
Повний текст джерелаLiu, Chenliang, Yuan Zhu, and Houwang Zhang. "Cellular Similarity based Imputation for Single cell RNA Sequencing Data." In ICBBT '21: 2021 13th International Conference on Bioinformatics and Biomedical Technology. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3473258.3473269.
Повний текст джерелаTran, Duc, Hung Nguyen, Frederick C. Harris, and Tin Nguyen. "Single-cell RNA sequencing data imputation using similarity preserving network." In 2021 13th International Conference on Knowledge and Systems Engineering (KSE). IEEE, 2021. http://dx.doi.org/10.1109/kse53942.2021.9648794.
Повний текст джерелаWang, Tianyu, Bingjun Li, and Sheida Nabavi. "Single-cell RNA sequencing data clustering using graph convolutional networks." In 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2021. http://dx.doi.org/10.1109/bibm52615.2021.9669529.
Повний текст джерелаЗвіти організацій з теми "Single cell sequencing data"
Savaldi-Goldstein, Sigal, and Todd C. Mockler. Precise Mapping of Growth Hormone Effects by Cell-Specific Gene Activation Response. United States Department of Agriculture, December 2012. http://dx.doi.org/10.32747/2012.7699849.bard.
Повний текст джерелаHarouaka, Ramdane. Platform for Single-Cell Dual RNA Sequencing of Host-Pathogen Interactions. Office of Scientific and Technical Information (OSTI), October 2021. http://dx.doi.org/10.2172/1832283.
Повний текст джерелаFung, N. DNA sequencing with capillary electrophoresis and single cell analysis with mass spectrometry. Office of Scientific and Technical Information (OSTI), March 1998. http://dx.doi.org/10.2172/348902.
Повний текст джерелаPalmer, Guy, Varda Shkap, Wendy Brown, and Thea Molad. Control of bovine anaplasmosis: cytokine enhancement of vaccine efficacy. United States Department of Agriculture, March 2007. http://dx.doi.org/10.32747/2007.7695879.bard.
Повний текст джерелаHawkins, Brian T., and Sonia Grego. A Better, Faster Road From Biological Data to Human Health: A Systems Biology Approach for Engineered Cell Cultures. RTI Press, June 2017. http://dx.doi.org/10.3768/rtipress.2017.rb.0015.1706.
Повний текст джерелаGur, Amit, Edward Buckler, Joseph Burger, Yaakov Tadmor, and Iftach Klapp. Characterization of genetic variation and yield heterosis in Cucumis melo. United States Department of Agriculture, January 2016. http://dx.doi.org/10.32747/2016.7600047.bard.
Повний текст джерелаBacharach, Eran, and Sagar Goyal. Generation of Avian Pneumovirus Modified Clones for the Development of Attenuated Vaccines. United States Department of Agriculture, November 2008. http://dx.doi.org/10.32747/2008.7696541.bard.
Повний текст джерелаCytryn, Eddie, Mark R. Liles, and Omer Frenkel. Mining multidrug-resistant desert soil bacteria for biocontrol activity and biologically-active compounds. United States Department of Agriculture, January 2014. http://dx.doi.org/10.32747/2014.7598174.bard.
Повний текст джерелаFahima, Tzion, and Jorge Dubcovsky. Map-based cloning of the novel stripe rust resistance gene YrG303 and its use to engineer 1B chromosome with multiple beneficial traits. United States Department of Agriculture, January 2013. http://dx.doi.org/10.32747/2013.7598147.bard.
Повний текст джерелаMinz, Dror, Stefan J. Green, Noa Sela, Yitzhak Hadar, Janet Jansson, and Steven Lindow. Soil and rhizosphere microbiome response to treated waste water irrigation. United States Department of Agriculture, January 2013. http://dx.doi.org/10.32747/2013.7598153.bard.
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