Academic literature on the topic 'Single cell sequencing data'
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Journal articles on the topic "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.
Full textZhao, 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.
Full textSatas, 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.
Full textVallejos, 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.
Full textSchnepp, 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.
Full textGisina, 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.
Full textDai, 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.
Full textZhang, 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.
Full textMyers, 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.
Full textZhao, 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.
Full textDissertations / Theses on the topic "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.
Full textSalehi, 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.
Full textScience, 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.
Full textBampalikis, 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.
Full textRonen, Jonathan. "Integrative analysis of data from multiple experiments." Doctoral thesis, Humboldt-Universität zu Berlin, 2020. http://dx.doi.org/10.18452/21612.
Full textThe 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.
Full textJohnson, Travis Steele. "Integrative approaches to single cell RNA sequencing analysis." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1586960661272666.
Full textBorgström, Erik. "Technologies for Single Cell Genome Analysis." Doctoral thesis, KTH, Genteknologi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-181059.
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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.
Full textKindblom, 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.
Full textBooks on the topic "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.
Full textYu, 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.
Full textWang, Xiangdong, ed. Single Cell Sequencing and Systems Immunology. Dordrecht: Springer Netherlands, 2015. http://dx.doi.org/10.1007/978-94-017-9753-5.
Full textLoos, Carolin. Analysis of Single-Cell Data. Wiesbaden: Springer Fachmedien Wiesbaden, 2016. http://dx.doi.org/10.1007/978-3-658-13234-7.
Full textYuan, 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.
Full textMallick, 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.
Full textChen, 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.
Full textYang, 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.
Full textSuzuki, Yutaka. Single Molecule and Single Cell Sequencing. Springer, 2019.
Find full textWang, Xiangdong. Single Cell Sequencing and Systems Immunology. Springer, 2016.
Find full textBook chapters on the topic "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.
Full textSagar, 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.
Full textVermeersch, 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.
Full textTran, 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.
Full textWang, 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.
Full textSagar 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.
Full textUlbrich, 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.
Full textLei, 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.
Full textLi, 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.
Full textBahonar, 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.
Full textConference papers on the topic "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.
Full textZare, 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.
Full textZhang, 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.
Full textTasoulis, 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.
Full textVrahatis, 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.
Full textWang, 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.
Full textBai, 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.
Full textLiu, 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.
Full textTran, 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.
Full textWang, 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.
Full textReports on the topic "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.
Full textHarouaka, 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.
Full textFung, 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.
Full textPalmer, 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.
Full textHawkins, 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.
Full textGur, 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.
Full textBacharach, 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.
Full textCytryn, 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.
Full textFahima, 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.
Full textMinz, 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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