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A universal approach for integrating super large-scale single-cell transcriptomes by exploring gene rankings [article]

Hongru Shen, Xilin Shen, Mengyao Feng, Dan Wu, Chao Zhang, Yichen Yang, Meng Yang, Jiani Hu, Jilei Liu, Wei Wang, Yang Li, Qiang Zhang (+3 others)
2021 bioRxiv   pre-print
Here, we presented a universal approach iSEEEK for integrating super large-scale single-cell expression via exploring expression rankings of top-expressing genes.  ...  Our study presents a simple and yet effective method to integrate super large-scale single-cell transcriptomes and would facilitate translational single-cell research from bench to bedside.  ...  We are grateful for researchers for their generosity to made their data publicly available.  ... 
doi:10.1101/2021.08.23.457305 fatcat:gr5tpgfrxfhebdj5bzjaadrxdm

Whole blood co-expression modules associate with metabolic traits and type 2 diabetes: an IMI-DIRECT study

Valborg Gudmundsdottir, Helle Krogh Pedersen, Gianluca Mazzoni, Kristine H. Allin, Anna Artati, Joline W. Beulens, Karina Banasik, Caroline Brorsson, Henna Cederberg, Elizaveta Chabanova, Federico De Masi, Petra J. Elders (+44 others)
2020 Genome Medicine  
Conclusions Our results offer a large-scale map of whole blood transcriptomic modules in the context of metabolic disease and point to novel biological candidates for future studies related to T2D.  ...  Through the integration of genetic and multi-omics data, we provide a holistic view of the regulation and molecular context of whole blood transcriptomic modules.  ...  Acknowledgements We thank all the participants and study centre staff in IMI-DIRECT for their contribution to the study.  ... 
doi:10.1186/s13073-020-00806-6 pmid:33261667 fatcat:zrmp57bqezhldks52uwzsgo4ty

Super-resolved spatial transcriptomics by deep data fusion [article]

Ludvig Bergenstråhle, Bryan He, Joseph Bergenstråhle, Alma Andersson, Joakim Lundeberg, James Zou, Jonas Maaskola
2020 bioRxiv   pre-print
Here, we present XFuse, a scalable deep generative model for spatial data fusion.  ...  XFuse can infer high-resolution, full-transcriptome spatial gene expression from histological image data and be used to characterize transcriptional heterogeneity in detailed anatomical structures.  ...  Competing interests J.L. is a scientific advisor at 10x Genomics, which produces spatially barcoded microarrays for in situ RNA capturing.  ... 
doi:10.1101/2020.02.28.963413 fatcat:m4d5h2megzeftcjgvv7zpkxf4a

A machine learning approach for the identification of key markers involved in brain development from single-cell transcriptomic data

Yongli Hu, Takeshi Hase, Hui Peng Li, Shyam Prabhakar, Hiroaki Kitano, See Kiong Ng, Samik Ghosh, Lawrence Jin Kiat Wee
2016 BMC Genomics  
The ability to sequence the transcriptomes of single cells using single-cell RNA-seq sequencing technologies presents a shift in the scientific paradigm where scientists, now, are able to concurrently  ...  Also, these genes possessed a higher discriminative power (enhanced prediction accuracy) as compared commonly used statistical techniques or geneset-based approaches.  ...  For SiGN-BN [28] , we used software on the super-computing resource that was provided by Human Genome Center, the Institute of Medical Science, and the University of Tokyo.  ... 
doi:10.1186/s12864-016-3317-7 pmid:28155657 pmcid:PMC5260093 fatcat:z747ov5elrgdxhqyba6qwjfdau

Holo-Seq: single-cell sequencing of holo-transcriptome

Zhengyun Xiao, Guo Cheng, Yang Jiao, Chen Pan, Ran Li, Danmei Jia, Jing Zhu, Chao Wu, Min Zheng, Junling Jia
2018 Genome Biology  
Current single-cell RNA-seq approaches are hindered by preamplification bias, loss of strand of origin information, and the inability to observe small-RNA and mRNA dual transcriptomes.  ...  Here, we introduce a single-cell holo-transcriptome sequencing (Holo-Seq) that overcomes all three hurdles.  ...  Acknowledgements We thank Yali Wang and Yungui Yang for advice and help on the experiments and manuscript.  ... 
doi:10.1186/s13059-018-1553-7 pmid:30333049 pmcid:PMC6193298 fatcat:uq37enwr6nbb3g2y3uy2xxqp4i

Integrated Omics: Tools, Advances, and Future Approaches

Biswapriya B Misra, Carl D Langefeld, Michael Olivier, Laura A Cox
2018 Journal of Molecular Endocrinology  
Commonly used approaches in these efforts are currently limited by the 3 i's - integration, interpretation, and insights.  ...  We discuss recent approaches, existing tools, and potential caveats in the integration of omics datasets for development of standardized analytical pipelines that could be adopted by the global omics research  ...  However, large-scale analyses with pretty graphics should not be a permit for poor-quality analyses.  ... 
doi:10.1530/jme-18-0055 pmid:30006342 fatcat:62c6xglxcbhhnkxgqo5gt7garq

Transcriptional programs define intratumoral heterogeneity of Ewing sarcoma at single cell resolution [article]

Marie-Ming Aynaud, Olivier Mirabeau, Nadege Gruel, Sandrine Grossetete-Lalami, Valentina Boeva, Simon Durand, Didier Surdez, Olivier Saulnier, Sakina Zaidi, Svetlana Gribkova, Ulykbek Kairov, Virginie Raynal (+9 others)
2019 biorxiv/medrxiv   pre-print
EWSR1-FLI1, the chimeric oncogene specific for Ewing sarcoma (EwS), induces a cascade of signaling events leading to cell transformation.  ...  In contrast, a subpopulation of cells from below and above optimal EWSR1-FLI1 activity was characterized by increased hypoxia.  ...  SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15.  ... 
doi:10.1101/623710 fatcat:3zxb3tc3dzgd7fcl5pvnczscma

Transcriptional Programs Define Intratumoral Heterogeneity of Ewing Sarcoma at Single-Cell Resolution

Marie-Ming Aynaud, Olivier Mirabeau, Nadege Gruel, Sandrine Grossetête, Valentina Boeva, Simon Durand, Didier Surdez, Olivier Saulnier, Sakina Zaïdi, Svetlana Gribkova, Aziz Fouché, Ulykbek Kairov (+10 others)
2020 Cell Reports  
EWSR1-FLI1, the chimeric oncogene specific for Ewing sarcoma (EwS), induces a cascade of signaling events leading to cell transformation.  ...  In contrast, a subpopulation of cells from below and above the intermediary EWSR1-FLI1 activity is characterized by increased hypoxia.  ...  SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15.  ... 
doi:10.1016/j.celrep.2020.01.049 pmid:32049009 fatcat:lx47ifrcinhe3evezn4ikaqr54

Comprehensive Integration of Single-Cell Data

Tim Stuart, Andrew Butler, Paul Hoffman, Christoph Hafemeister, Efthymia Papalexi, William M. Mauck, Yuhan Hao, Marlon Stoeckius, Peter Smibert, Rahul Satija
2019 Cell  
Single-cell transcriptomics has transformed our ability to characterize cell states, but deep biological understanding requires more than a taxonomic listing of clusters.  ...  Here, we develop a strategy to "anchor" diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities.  ...  We clustered the scRNA-seq cells by first identifying the top 3,000 highly variable genes, scaling and centering the expression of these genes, computing PCA on the scaled expression values, and performing  ... 
doi:10.1016/j.cell.2019.05.031 pmid:31178118 pmcid:PMC6687398 fatcat:hofjwozaffdynho7kmcljxez44

Resolving cellular systems by ultra-sensitive and economical single-cell transcriptome filtering

Andres F. Vallejo, James Davies, Amit Grover, Ching-Hsuan Tsai, Robert Jepras, Marta E. Polak, Jonathan West
2021 iScience  
The technique reliably measures changes in gene expression and was demonstrated by resolving rare dendritic cell populations from a peripheral blood mononuclear cell sample sample and exploring their biology  ...  Single-cell transcriptomics suffer from sensitivity limits that restrict low abundance transcript identification, affects clustering and can hamper downstream analyses.  ...  In addition, it opens the Constellation-Seq builds on standard scRNA-Seq pipelines, to provide a cost-effective single cell transcriptomics approach for large-scale experiments, while addressing the issues  ... 
doi:10.1016/j.isci.2021.102147 pmid:33665566 pmcid:PMC7900351 fatcat:puvmymad7bbixpnxbyszlxkxay

Single-Cell RNA Sequencing of Childhood Ependymoma Reveals Neoplastic Cell Subpopulations That Impact Molecular Classification and Etiology

Austin E. Gillen, Kent A. Riemondy, Vladimir Amani, Andrea M. Griesinger, Ahmed Gilani, Sujatha Venkataraman, Krishna Madhavan, Eric Prince, Bridget Sanford, Todd C. Hankinson, Michael H. Handler, Rajeev Vibhakar (+5 others)
2020 Cell Reports  
We provide an interactive online resource that facilitates exploration of the EPN single-cell dataset. This atlas of EPN cellular heterogeneity increases understanding of EPN biology.  ...  We, therefore, use single-cell RNA sequencing, histology, and deconvolution to catalog cellular heterogeneity of the major childhood EPN subgroups.  ...  Bioinformatic support for this work was supported by the RNA Bioscience Initiative, University of Colorado School of Medicine.  ... 
doi:10.1016/j.celrep.2020.108023 pmid:32783945 pmcid:PMC7452755 fatcat:fugiuggytzckpfiscf32gzrnue

Single-cell RNA-sequencing of peripheral neuroblastic tumors reveals an aggressive transitional cell state at the junction of an adrenergic-mesenchymal transdifferentiation trajectory [article]

Xiaojun Yuan, Janith A Seneviratne, Shibei Du, Ying Xu, Yijun Chen, Qianya Jin, Xuanxuan Jin, Anushree Balachandran, Shihao Huang, Yanli Xu, Yue Zhai, Liumei Lu (+7 others)
2020 bioRxiv   pre-print
Here, single-cell RNA-sequencing analysis of 4267 cells from 7 PNTs demonstrated extensive transcriptomic heterogeneity.  ...  Transitional cells are characterized by gene expression programs linked to a sympathoadrenal development, and aggressive tumor phenotypes such as rapid proliferation and tumor dissemination.  ...  Our trajectory modelling using single-cell RNA-seq data, and our reclassification approach for H3K27ac landscapes in neuroblastoma cell lines is in support of this theory, albeit by a slightly more complex  ... 
doi:10.1101/2020.05.15.097469 fatcat:ma6fkwmsivea5m3xk55myeaovm

Synergistic action of master transcription factors controls epithelial-to-mesenchymal transition

Hongyuan Chang, Yuwei Liu, Mengzhu Xue, Haiyue Liu, Shaowei Du, Liwen Zhang, Peng Wang
2016 Nucleic Acids Research  
Here, we performed a computational analysis that integrated time-course EMT transcriptomic data with public cistromic data and identified three synergistic master TFs (ETS2, HNF4A and JUNB) that regulate  ...  Systematic characterization of all dynamic TFs controlling EMT state transitions, especially for the intermediate partial-EMT state, represents a highly relevant yet largely unexplored task.  ...  ACKNOWLEDGEMENTS The authors would like to thank members of the Laboratory of Systems Biology for participating in helpful discussions and National Center for Protein Science Shanghai for assistance with  ... 
doi:10.1093/nar/gkw126 pmid:26926107 pmcid:PMC4824118 fatcat:nvraigzu3vay5fcxebpxvk7d2y

Quantum Computing at the Frontiers of Biological Sciences [article]

Prashant S. Emani, Jonathan Warrell, Alan Anticevic, Stefan Bekiranov, Michael Gandal, Michael J. McConnell, Guillermo Sapiro, Alán Aspuru-Guzik, Justin Baker, Matteo Bastiani, Patrick McClure, John Murray, Stamatios N Sotiropoulos, Jacob Taylor (+3 others)
2019 arXiv   pre-print
By examining joint opportunities for computational innovation across fields, we highlight the need for a common language between biological data analysis and quantum computing.  ...  We use this coincidence to explore the potential for quantum computing to aid in one such endeavor: the merging of insights from genetics, genomics, neuroimaging and behavioral phenotyping.  ...  Single cell transcriptomic approaches further refine these subcategories and identify unique subtypes of human neurons.  ... 
arXiv:1911.07127v1 fatcat:k2agx5yysjgi3m3ryhicptzauq

Single-cell transcriptomic profiling of the aging mouse brain

Methodios Ximerakis, Scott L. Lipnick, Brendan T. Innes, Sean K. Simmons, Xian Adiconis, Danielle Dionne, Brittany A. Mayweather, Lan Nguyen, Zachary Niziolek, Ceren Ozek, Vincent L. Butty, Ruth Isserlin (+6 others)
2019 Nature Neuroscience  
Here we performed a single-cell transcriptomic analysis of young and old mouse brains.  ...  Overall, these large-scale datasets (accessible online at ) provide a resource for the neuroscience community that will facilitate  ...  The raw single-cell RNA sequencing data are available through NCBI's Gene Expression Omnibus (GEO) under the accession number GSE129788.  ... 
doi:10.1038/s41593-019-0491-3 pmid:31551601 fatcat:zhrzwupi3rhllctrcb47y5ub6e
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