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Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex
Open accessAbstract
Large-scale surveys of single-cell gene expression have the potential to reveal rare cell populations and lineage relationships but require efficient methods for cell capture and mRNA sequencing. Although cellular barcoding strategies allow parallel sequencing of single cells at ultra-low depths, the limitations of shallow sequencing have not been investigated directly. By capturing 301 single cells from 11 populations using microfluidics and analyzing single-cell transcriptomes across downsampled sequencing depths, we demonstrate that shallow single-cell mRNA sequencing (~50,000 reads per cell) is sufficient for unbiased cell-type classification and biomarker identification. In the developing cortex, we identify diverse cell types, including multiple progenitor and neuronal subtypes, and we identify EGR1 and FOS as previously unreported candidate targets of Notch signaling in human but not mouse radial glia. Our strategy establishes an efficient method for unbiased analysis and comparison of cell populations from heterogeneous tissue by microfluidic single-cell capture and low-coverage sequencing of many cells.
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“When studying single cells, which have limited sample complexity, quantification is often carried out with just one million reads but may be done reliably for highly expressed genes with as few as 50,000 reads [8]; even 20,000 reads have been used to differentiate cell types in splenic tissue [9].”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [8]
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“Deeper sequencing for scRNA-seq will do little to improve quantification as the number of individual mRNA molecules in a cell is small (in the order of 100–300,000 transcripts) and only a fraction of them are successfully reverse-transcribed to cDNA [8, 176]”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [8]
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Cites
Ultrafast and memory-efficient alignment of short DNA sequences to the human genome.
Langmead, B., Trapnell, C., Pop, M., & Salzberg, S. L. (2009) · Genome Biology · doi:10.1186/gb-2009-10-3-r25 · Cited by 21,849
TopHat: discovering splice junctions with RNA-Seq
Trapnell, C., Pachter, L. S., & Salzberg, S. L. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp120 · Cited by 11,682
Pvclust: an R package for assessing the uncertainty in hierarchical clustering
Suzuki, R., & Shimodaira, H. (2006) · Bioinformatics · doi:10.1093/bioinformatics/btl117 · Cited by 2,456
The UCSC Genome Browser database: update 2011
Fujita, P. A., Rhead, B., Zweig, A. S., Hinrichs, A. S., Karolchik, D., Cline, M., Goldman, M. J., Barber, G. P., Clawson, H., Coelho, A., et al. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq963 · Cited by 1,843
Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types
Jaitin, D. A., Kenigsberg, E., Keren-Shaul, H., Elefant, N., Paul, F., Zaretsky, I., Mildner, A., Cohen, N., Jung, S., Tanay, A., et al. (2014) · Science · doi:10.1126/science.1247651 · Cited by 1,795
Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells
Ramsköld, D., Luo, S., Wang, Y.-C., Li, R., Deng, Q., Faridani, O. R., Daniels, G. A., Khrebtukova, I., Loring, J. F., Laurent, L. C., et al. (2012) · Nature Biotechnology · doi:10.1038/nbt.2282 · Cited by 1,609
Sequencing depth and coverage: key considerations in genomic analyses
Sims, D., Sudbery, I. M., Ilott, N. E., Heger, A., & Ponting, C. P. (2014) · Nature Reviews Genetics · doi:10.1038/nrg3642 · Cited by 1,488
Single-Cell RNA-Seq Reveals Dynamic, Random Monoallelic Gene Expression in Mammalian Cells
Deng, Q., Ramsköld, D., Reinius, B., & Sandberg, R. (2014) · Science · doi:10.1126/science.1245316 · Cited by 1,345
Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq
Treutlein, B., Brownfield, D. G., Wu, A. R., Neff, N. F., Mantalas, G. L., Espinoza, F. H., Desai, T. J., Krasnow, M. A., & Quake, S. R. (2014) · Nature · doi:10.1038/nature13173 · Cited by 1,342
Transcriptional landscape of the prenatal human brain
Miller, J. A., Ding, S.-L., Sunkin, S. M., Smith, K. A., Ng, L., Szafer, A., Ebbert, A., Riley, Z. L., Royall, J. J., Aiona, K., et al. (2014) · Nature · doi:10.1038/nature13185 · Cited by 1,325
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Mercatelli, D., Ray, F., & Giorgi, F. M. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00671 · Cited by 23
FEATS: feature selection-based clustering of single-cell RNA-seq data
Vans, E., Patil, A., & Sharma, A. K. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa306 · Cited by 23
Adameyko, I. I., Bakken, T. E., Bhaduri, A., Chhatbar, C., Filbin, M. G., Gate, D., Hochgerner, H., Kim, C. N., Krull, J. E., Manno, G. L., et al. (2024) · Nature Neuroscience · doi:10.1038/s41593-024-01827-9 · Cited by 26
Self-citationSingle-Cell RNA Sequencing of Human T Cells
Villani, A., & Shekhar, K. (2016) · Methods in molecular biology · doi:10.1007/978-1-4939-6548-9_16 · Cited by 25
Functional genomic approaches to elucidate the role of enhancers during development
Ryan, G. E., & Farley, E. K. (2019) · WIREs Systems Biology and Medicine · doi:10.1002/wsbm.1467 · Cited by 22
LAK: Lasso and K-Means Based Single-Cell RNA-Seq Data Clustering Analysis
Hua, J., Liu, H., Zhang, B., & Jin, S. (2020) · IEEE Access · doi:10.1109/access.2020.3008681 · Cited by 24
LSH-GAN enables in-silico generation of cells for small sample high dimensional scRNA-seq data
Lall, S., Ray, S., & Bandyopadhyay, S. (2022) · Communications Biology · doi:10.1038/s42003-022-03473-y · Cited by 29
Microfluidic single‐cell multiomics analysis
Xu, X., Zhang, Q., Li, M., Lin, S., Liang, S., Cai, L., Zhu, H., Su, R. J., & Yang, C. J. (2022) · Open Research (University of Surrey) · doi:10.1002/viw.20220034 · Cited by 24
SC3s: efficient scaling of single cell consensus clustering to millions of cells
Quah, F. X., & Hemberg, M. (2022) · BMC Bioinformatics · doi:10.1186/s12859-022-05085-z · Cited by 23
Cellular identity at the single-cell level
Coskun, A. F., Eser, U., & Islam, S. (2016) · Molecular BioSystems · doi:10.1039/c6mb00388e · Cited by 24
Berglund, E., Saarenpää, S., Jemt, A., Gruselius, J., Larsson, L., Bergenstråhle, L., Lundeberg, J., & Giacomello, S. (2020) · BMC Genomics · doi:10.1186/s12864-020-6631-z · Cited by 23
Illuminating Neural Circuits in Alzheimer’s Disease
Yang, Y., & Wang, J. (2021) · Neuroscience Bulletin · doi:10.1007/s12264-021-00716-6 · Cited by 24
Self-citationsc-REnF: An entropy guided robust feature selection for single-cell RNA-seq data
Lall, S., Ghosh, A., Ray, S., & Bandyopadhyay, S. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab517 · Cited by 21
Gene-expression memory-based prediction of cell lineages from scRNA-seq datasets
Eisele, A. S., Tarbier, M., Dormann, A. A., Pelechano, V., & Suter, D. M. (2024) · Nature Communications · doi:10.1038/s41467-024-47158-y · Cited by 29
Imputing dropouts for single-cell RNA sequencing based on multi-objective optimization
Jin, K., Li, B., Yan, H., & Zhang, X.-F. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac300 · Cited by 22
Advances in Single-Cell Toxicogenomics in Environmental Toxicology
Liu, Y., Chen, L., Yu, J., Ye, L., Hu, H., Wang, J., & Wu, B. (2022) · Environmental Science & Technology · doi:10.1021/acs.est.2c01098 · Cited by 23
Self-citationCRISPR screening in hematology research: from bulk to single-cell level
Meyers, S., Demeyer, S., & Cools, J. (2023) · Journal of Hematology & Oncology · doi:10.1186/s13045-023-01495-5 · Cited by 24
Current Progresses of Single Cell DNA Sequencing in Breast Cancer Research
Liu, J., Adhav, R., & Xu, X. (2017) · International Journal of Biological Sciences · doi:10.7150/ijbs.19627 · Cited by 23
Sun, Y., Tian, H., Liu, C., Yang, D., & Li, Z. (2018) · ACS Sensors · doi:10.1021/acssensors.8b00524 · Cited by 22
A high-throughput RNA-seq approach to profile transcriptional responses
Moyerbrailean, G. A., Davis, G. O., Harvey, C. T., Watza, D., Wen, X., Piqué-Regi, R., & Luca, F. (2015) · Scientific Reports · doi:10.1038/srep14976 · Cited by 23
Zeng, P., Wangwu, J., & Lin, Z. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa347 · Cited by 23
Single Cell Gene Expression to Understand the Dynamic Architecture of the Heart
Massaia, A., Cháves, P., Samari, S., Miragaia, R. J., Meyer, K. B., Teichmann, S. A., & Noseda, M. (2018) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2018.00167 · Cited by 20
ScGSLC: An unsupervised graph similarity learning framework for single-cell RNA-seq data clustering
Li, J., Jiang, W., Han, H., Liu, J., Liu, B., & Wang, Y. (2020) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2020.107415 · Cited by 21
Baldwin, M. J., Mimpen, J. Y., Cribbs, A. P., Stace, E. T., Philpott, M., Dakin, S. G., Carr, A. J., & Snelling, S. J. B. (2022) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2021.795748 · Cited by 22
Single-cell RNA-sequencing of mammalian brain development: insights and future directions
Vinsland, E., & Linnarsson, S. (2022) · Development · doi:10.1242/dev.200180 · Cited by 21
Recovery and analysis of transcriptome subsets from pooled single-cell RNA-seq libraries
Riemondy, K. A., Ransom, M., Alderman, C., Gillen, A. E., Fu, R., Finlay-Schultz, J., Kirkpatrick, G. D., Paola, J. D., Kabos, P., Sartorius, C. A., et al. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky1204 · Cited by 20
Xu, D., Zhang, J., Xu, H., Zhang, Y., Chen, W., Gao, R., & Dehmer, M. (2020) · BMC Genomics · doi:10.1186/s12864-020-07038-3 · Cited by 23
Durruthy-Durruthy, R., Gottlieb, A., & Heller, S. (2015) · Nature Protocols · doi:10.1038/nprot.2015.022 · Cited by 20
Bioinformatics analysis on differentially expressed genes of alveolar macrophage in IPF
Wang, H., Wang, M., Xiao, K., Zhang, X., Wang, P., Xiao, S., Qi, H., Meng, L., Zhang, X., & Shen, F. (2019) · Experimental Lung Research · doi:10.1080/01902148.2019.1680765 · Cited by 21
Johnson, T. S., Xiang, S., Helm, B. R., Abrams, Z. B., Neidecker, P., Machiraju, R., Zhang, Y., Huang, K., & Zhang, J. (2020) · Scientific Reports · doi:10.1038/s41598-020-74917-w · Cited by 23
Lai, S.-C. A., Gundlapalli, H., Ekiz, H. A., Jiang, A., Fernandez, E., & Welm, A. L. (2021) · Cancer Discovery · doi:10.1158/2159-8290.cd-20-1172 · Cited by 20
Tradict enables accurate prediction of eukaryotic transcriptional states from 100 marker genes
Biswas, S., Kerner, K., Teixeira, P. J. P. L., Dangl, J. L., Jojic, V., & Wigge, P. A. (2017) · Nature Communications · doi:10.1038/ncomms15309 · Cited by 19
Cortical cellular diversity and development in schizophrenia
Price, A. J., Jaffe, A. E., & Weinberger, D. R. (2020) · Molecular Psychiatry · doi:10.1038/s41380-020-0775-8 · Cited by 23
Ou-Yang, L., Lü, F., Zhang, Z.-C., & Wu, M. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab479 · Cited by 20
Hao, J., Cao, W., Huang, J. J., Zou, X., & Han, Z. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty1016 · Cited by 19
Zhong, Q., Shang, J., Ren, Q., Li, F., Jiao, C.-N., & Liu, J. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3409628 · Cited by 19
Shi, R., Chen, H., Zhang, W., Leak, R. K., Lou, D., Chen, K., & Chen, J. (2024) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1177/0271678x241305914 · Cited by 19
Finding cell-specific expression patterns in the early Ciona embryo with single-cell RNA-seq
Ilsley, G. R., Suyama, R., Noda, T., Satoh, N., & Luscombe, N. M. (2020) · Scientific Reports · doi:10.1038/s41598-020-61591-1 · Cited by 22
Wang, H., Zhao, J., & Zheng, C.-H. (2021) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-020-00411-6 · Cited by 20
Wang, Z., Zhong, Y., Ye, Z., Zeng, L., Chen, Y., Shi, M., Yuan, Z., Zhou, Q., Qian, M., & Zhang, M. Q. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab1132 · Cited by 21
Zou, J., Deng, F., Wang, M., Zhang, Z., Liu, Z., Zhang, X., Hua, R., Chen, K., Zou, X., & Hao, J. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac180 · Cited by 20
Ahn, R. S., Taravati, K., Lai, K., Lee, K. M., Nititham, J., Gupta, R., Chang, D. S., Arron, S. T., Rosenblum, M. D., & Liao, W. J. (2017) · Scientific Reports · doi:10.1038/s41598-017-01468-y · Cited by 18
PanoView: An iterative clustering method for single-cell RNA sequencing data
Hu, M.-W., Kim, D. W., Liu, S., Zack, D. J., Blackshaw, S., & Qian, J. X. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007040 · Cited by 19
Dissecting the human immune system with single cell RNA sequencing technology
Xu, G., Liu, Y., Li, H., Liu, L., Zhang, S., & Zhang, Z. (2019) · Journal of Leukocyte Biology · doi:10.1002/jlb.5mr1019-179r · Cited by 19
RgCop-A regularized copula based method for gene selection in single-cell RNA-seq data
Lall, S., Ray, S., & Bandyopadhyay, S. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009464 · Cited by 17
Retina Development in Vertebrates: Systems Biology Approaches to Understanding Genetic Programs
Buono, L., & Martínez‐Morales, J. R. (2020) · BioEssays · doi:10.1002/bies.201900187 · Cited by 18
Scedar: A scalable Python package for single-cell RNA-seq exploratory data analysis
Zhang, Y., Kim, M. S., Reichenberger, E. R., Stear, B. J., & Taylor, D. M. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007794 · Cited by 17
Dissecting stem cell differentiation using single cell expression profiling
Moignard, V., & Göttgens, B. (2016) · Current Opinion in Cell Biology · doi:10.1016/j.ceb.2016.08.005 · Cited by 18
Recursive Consensus Clustering for novel subtype discovery from transcriptome data
Sonpatki, P., & Shah, N. (2020) · Scientific Reports · doi:10.1038/s41598-020-67016-3 · Cited by 17
Kulkarni, S., Saha, M., Slosberg, J., Singh, A., Nagaraj, S., Becker, L. S., Zhang, C., Bukowski, A., Wang, Z., Liu, G., et al. (2023) · eLife · doi:10.7554/elife.88051 · Cited by 32
Vicus: Exploiting local structures to improve network-based analysis of biological data
Wang, B., Huang, L., Zhu, Y., Kundaje, A. B., Batzoglou, S., & Goldenberg, A. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005621 · Cited by 17
Quality Control of Single-Cell RNA-seq
Jiang, P. (2019) · Methods in molecular biology · doi:10.1007/978-1-4939-9057-3_1 · Cited by 18
Single-Cell RNA Sequencing of Plant-Associated Bacterial Communities
Ma, Q., Bücking, H., González-Hernández, J. L., & Subramanian, S. (2019) · Frontiers in Microbiology · doi:10.3389/fmicb.2019.02452 · Cited by 16
Lin, Z., Zamanighomi, M., Daley, T., Ma, S., & Wong, W. H. (2020) · Statistical Science · doi:10.1214/19-sts714 · Cited by 17
Wu, Z., Shou, L., Wang, J., & Xu, X. (2020) · Medicine · doi:10.1097/md.0000000000021707 · Cited by 17
Self-citationShi, Y. Y., Huang, X., Du, Z., & Tan, J. (2022) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2022271 · Cited by 18
Quantitative single cell gene expression profiling in the avian embryo
Morrison, J. A., Box, A., McKinney, M. C., McLennan, R., & Kulesa, P. M. (2015) · Developmental Dynamics · doi:10.1002/dvdy.24274 · Cited by 17
Okada, J., Landgraf, A., Xiaoli, A. M., Liu, L. P., Horton, M., Schuster, V. L., Yang, F., Sidoli, S., Qiu, Y., Kurland, I. J., et al. (2025) · Nature Metabolism · doi:10.1038/s42255-025-01269-y · Cited by 19
Engineering next-generation microfluidic technologies for single-cell phenomics
Lambert, C. L. G., van Mierlo, G., Bues, J., Guillaume-Gentil, O. J., & Deplancke, B. (2025) · Nature Genetics · doi:10.1038/s41588-025-02198-y · Cited by 17
Exploring the Complexity of Cortical Development Using Single-Cell Transcriptomics
Jeong, H., & Tiwari, V. K. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00031 · Cited by 16
TsImpute: an accurate two-step imputation method for single-cell RNA-seq data
Zheng, W., Min, W., & Wang, S. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad731 · Cited by 16
A Hybrid Clustering Algorithm for Identifying Cell Types from Single-Cell RNA-Seq Data
Zhu, X., Li, H., Xu, Y., Guo, L., Wu, F., Duan, G., & Wang, J. (2019) · Genes · doi:10.3390/genes10020098 · Cited by 17
Self-citationDe novo prediction of cell-type complexity in single-cell RNA-seq and tumor microenvironments
Woo, J., Winterhoff, B. J. N., Starr, T. K., Aliferis, C., & Wang, J. (2019) · Life Science Alliance · doi:10.26508/lsa.201900443 · Cited by 16
Self-citationSingle-Cell RNA Sequencing Data Clustering by Low-Rank Subspace Ensemble Framework
Wang, C., Gao, Y.-L., Liu, J., Kong, X.-Z., & Zheng, C.-H. (2020) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2020.3029187 · Cited by 16
Sc-GPE: A Graph Partitioning-Based Cluster Ensemble Method for Single-Cell
Zhu, X., Li, J., Li, H., Xie, M., & Wang, J. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.604790 · Cited by 16
Self-citationImproving Single-Cell RNA-seq Clustering by Integrating Pathways
Zhang, C., Gao, L., Wang, B., & Gao, Y. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab147 · Cited by 15
Secuer: Ultrafast, scalable and accurate clustering of single-cell RNA-seq data
Wei, N., Nie, Y., Liu, L. L., Zheng, X., & Wu, H. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010753 · Cited by 17
Highly multiplexed single-cell quantitative PCR
VanInsberghe, M. A., Zahn, H., White, A. K., Petriv, O. I., & Hansen, C. L. (2018) · PLoS ONE · doi:10.1371/journal.pone.0191601 · Cited by 16
Efficient profiling of total RNA in single cells with STORM-seqPreprint
Johnson, B. K., Majewski, M. F., Jang, H. J., Siwicki, R. A., Wegener, M., Semwal, A., Morrison, J., Gallik, K., Hagemann-Jensen, M., Foy, K. K., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.03.14.484332 · Cited by 18
Shiga, M., Seno, S., Onizuka, M., & Matsuda, H. (2021) · PeerJ · doi:10.7717/peerj.12087 · Cited by 15
ScLRTC: imputation for single-cell RNA-seq data via low-rank tensor completion
Pan, X., Li, Z., Qin, S., Yu, M., & Hu, H. (2021) · BMC Genomics · doi:10.1186/s12864-021-08101-3 · Cited by 17
SLRRSC: Single-Cell Type Recognition Method Based on Similarity and Graph Regularization Constraints
Zhang, N.-N., Liu, J., Zheng, C.-H., & Wang, J. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3148286 · Cited by 16
Self-citationRecent advances in deciphering hippocampus complexity using single-cell transcriptomics
Chang, C., Zuo, H., & Li, Y. (2023) · Neurobiology of Disease · doi:10.1016/j.nbd.2023.106062 · Cited by 16
Electrochemiluminescence Detection and Imaging of Biomolecules at the Single-Cell Level
He, X., Deng, Y., Jiang, D., & Fang, D. (2023) · Chemosensors · doi:10.3390/chemosensors11100538 · Cited by 17
Kulkarni, S., Saha, M., Slosberg, J., Singh, A., Nagaraj, S., Becker, L. S., Zhang, C., Bukowski, A., Wang, Z., Liu, G., et al. (2023) · eLife · doi:10.7554/elife.88051.2 · Cited by 15
Single neuron transcriptome analysis can reveal more than cell type classification
Harbom, L. J., Chronister, W. D., & McConnell, M. J. (2016) · BioEssays · doi:10.1002/bies.201500097 · Cited by 16
Yifan, C., Fan, Y., & Pu, J. (2020) · Journal of Molecular and Cellular Cardiology · doi:10.1016/j.yjmcc.2020.03.005 · Cited by 15
Network-Based Single-Cell RNA-Seq Data Imputation Enhances Cell Type Identification
Zand, M., & Ruan, J. (2020) · Genes · doi:10.3390/genes11040377 · Cited by 14
Min, W., Wan, X., Chang, T., & Zhang, S. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3054635 · Cited by 15
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Wu, M., Xia, M., Li, W., & Li, H. (2021) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2021.637779 · Cited by 14
Armfield, B. A., & Cohn, M. J. (2021) · Developmental Biology · doi:10.1016/j.ydbio.2021.05.014 · Cited by 14
Single-cell genomic profiling of acute myeloid leukemia for clinical use: A pilot study
Yan, B., Hu, Y., Ban, K. H.-K., Tiang, Z., Ng, C., Lee, J., Tan, W. L. W., Chiu, L., Tan, T. W., Seah, E., et al. (2017) · Oncology Letters · doi:10.3892/ol.2017.5669 · Cited by 16
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Min, W., Xu, T., Wan, X., & Chang, T. (2022) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2022.3206881 · Cited by 15
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Baran, Y., & Doğan, B. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.106634 · Cited by 14
Chu, H.-M., Kong, X.-Z., Liu, J., Zheng, C.-H., & Zhang, H. (2023) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2023.3283801 · Cited by 14
A novel method for single-cell data imputation using subspace regression
Tran, D., Tran, B., Nguyen, H., & Nguyen, T. (2022) · Scientific Reports · doi:10.1038/s41598-022-06500-4 · Cited by 14
Li, Z., Tang, C., Zheng, X., Li, Z., Zhang, W., & Cao, L. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.06.046 · Cited by 14
Athreya, A. P., Kalari, K. R., Cairns, J., Gaglio, A. J., Wills, Q. F., Niu, N., Weinshilboum, R. M., Iyer, R. K., & Wang, L. (2017) · Oncotarget · doi:10.18632/oncotarget.16109 · Cited by 14
High-Order Correlation Integration for Single-Cell or Bulk RNA-seq Data Analysis
Tang, H., Zeng, T., & Chen, L. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00371 · Cited by 14
A Gene Rank Based Approach for Single Cell Similarity Assessment and Clustering
Xu, Y., Li, H., Pan, Y. F., Luo, F., Wu, F., & Wang, J. (2019) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2019.2931582 · Cited by 14
Self-citationClustering Single-Cell RNA-Seq Data with Regularized Gaussian Graphical Model
Liu, Z. (2021) · Genes · doi:10.3390/genes12020311 · Cited by 14
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Metadata
- Authors
- Pollen, A. A., Nowakowski, T. J., Shuga, J., Wang, X., Leyrat, A. A., Lui, J. H., Li, N., Szpankowski, L., Fowler, B., Chen, P., Ramalingam, N., Sun, G., Thu, M., Norris, M., Lebofsky, R., Toppani, D., Kemp, D. W., Wong, M., Clerkson, B., Jones, B. N., Wu, S., Knutsson, L., Alvarado, B., Wang, J., Weaver, L. S., May, A. P., Jones, R. C., Unger, M. A., Kriegstein, A. R., & West, J. A. A.
- Affiliations
- University of California, San Francisco (US)
- Standard BioTools (United States) (US)
- Corresponding author
- Pollen, Alex A
- Kriegstein, Arnold R
- ORCID
- Venue
- Nature Biotechnology
- Published
- 2014-08-03
- Volume
- 32
- Issue
- 10
- Pages
- 1053-1058
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- PMC
- PMC4191988
- OpenAlex
- W2051658465
- Cited by
- 920
- References
- 38
- Citations by year
- 2026: 33 · 2025: 51 · 2024: 54 · 2023: 62 · 2022: 78 · 2021: 110 · 2020: 110 · 2019: 105 · 2018: 93 · 2017: 91 · 2016: 74 · 2015: 52 · 2014: 5
- FWCI
- 30.36
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Biotechnology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- National Institute of Neurological Disorders and Stroke (R01NS072630, R01NS075998)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- single-cell transcriptomics, low-pass sequencing, cellular heterogeneity, cortical development, microfluidic cell capture, cell type classification, biomarker discovery, Notch signaling, radial glia, EGR1, FOS, human brain
- MeSH terms
- Animals, Cerebral Cortex, Equipment Design, Humans, RNA, Messenger, Signal Transduction, Sequence Analysis, RNA, Computational Biology, Gene Expression Profiling, Microfluidic Analytical Techniques, Mice
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