Modeling Bi-modality Improves Characterization of Cell Cycle on Gene Expression in Single Cells
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Abstract
Advances in high-throughput, single cell gene expression are allowing interrogation of cell heterogeneity. However, there is concern that the cell cycle phase of a cell might bias characterizations of gene expression at the single-cell level. We assess the effect of cell cycle phase on gene expression in single cells by measuring 333 genes in 930 cells across three phases and three cell lines. We determine each cell's phase non-invasively without chemical arrest and use it as a covariate in tests of differential expression. We observe bi-modal gene expression, a previously-described phenomenon, wherein the expression of otherwise abundant genes is either strongly positive, or undetectable within individual cells. This bi-modality is likely both biologically and technically driven. Irrespective of its source, we show that it should be modeled to draw accurate inferences from single cell expression experiments. To this end, we propose a semi-continuous modeling framework based on the generalized linear model, and use it to characterize genes with consistent cell cycle effects across three cell lines. Our new computational framework improves the detection of previously characterized cell-cycle genes compared to approaches that do not account for the bi-modality of single-cell data. We use our semi-continuous modelling framework to estimate single cell gene co-expression networks. These networks suggest that in addition to having phase-dependent shifts in expression (when averaged over many cells), some, but not all, canonical cell cycle genes tend to be co-expressed in groups in single cells. We estimate the amount of single cell expression variability attributable to the cell cycle. We find that the cell cycle explains only 5%-17% of expression variability, suggesting that the cell cycle will not tend to be a large nuisance factor in analysis of the single cell transcriptome.
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Some methods adapt well-established tools previously used in bulk genomics to the particular challenges of noise and scale in single-cell data, whereas others are designed specifically for single-cell data.
“Some of these methods adapt well-established tools previously used in bulk genomics to the particular challenges of noise and scale in single-cell data1,44,89,90,105, whereas others are designed specifically for single-cell data71,93,135,137.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [89]
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Cites
Regularization and Variable Selection Via the Elastic Net
Zou, H., & Hastie, T. (2005) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2005.00503.x · Cited by 20,957 · see citing works
Regularization Paths for Generalized Linear Models via Coordinate Descent
Friedman, J., Hastie, T., & Tibshirani, R. (2010) · Journal of Statistical Software · doi:10.18637/jss.v033.i01 · Cited by 19,927 · see citing works
Adjusting batch effects in microarray expression data using empirical Bayes methods
Johnson, W. E., Li, C., & Rabinovic, A. (2007) · Biostatistics · doi:10.1093/biostatistics/kxj037 · Cited by 8,658 · see citing works
Stochastic Gene Expression in a Single Cell
Elowitz, M. B., Levine, A. J., Siggia, E. D., & Swain, P. S. (2002) · Science · doi:10.1126/science.1070919 · Cited by 5,441 · see citing works
Cragg, J. G. (1971) · Econometrica · doi:10.2307/1909582 · Cited by 3,123 · see citing works
Nature, Nurture, or Chance: Stochastic Gene Expression and Its Consequences
Raj, A., & van Oudenaarden, A. (2008) · Cell · doi:10.1016/j.cell.2008.09.050 · Cited by 2,593 · see citing works
High dimensional graphs and variable selection with the Lasso
Meinshausen, N., & Bühlmann, P. (2006) · Cited by 2,539 · see citing works
High-dimensional graphs and variable selection with the Lasso
Meinshausen, N., & Bühlmann, P. (2006) · The Annals of Statistics · doi:10.1214/009053606000000281 · Cited by 2,521 · see citing works
Direct multiplexed measurement of gene expression with color-coded probe pairs
Geiss, G. K., Bumgarner, R. E., Birditt, B., Dahl, T., Dowidar, N., Dunaway, D. L., Fell, H. P., Ferree, S., George, R. D., Grogan, T., et al. (2008) · Nature Biotechnology · doi:10.1038/nbt1385 · Cited by 2,066 · see citing works
Self-citationA Genome-Wide Transcriptional Analysis of the Mitotic Cell Cycle
Cho, R. J., Campbell, M. J., Winzeler, E. A., Steinmetz, L. M., Conway, A. R., Wodicka, L. M., Wolfsberg, T. G., Gabrielian, A. E., Landsman, D., Lockhart, D. J., et al. (1998) · Molecular Cell · doi:10.1016/s1097-2765(00)80114-8 · Cited by 2,024 · see citing works
Singular value decomposition for genome-wide expression data processing and modeling
Alter, O., Brown, P. O., & Botstein, D. (2000) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.97.18.10101 · Cited by 1,989 · see citing works
Stochastic mechanisms in gene expression.
McAdams, H., & Arkin, A. (1997) · Proceedings of the National Academy of Sciences of the United States of America · doi:10.1073/pnas.94.3.814 · Cited by 1,970 · see citing works
Gragg, J. (1971) · Econometrica · Cited by 1,645 · see citing works
Whitfield, M. L., Sherlock, G., Saldanha, A. J., Murray, J. I., Ball, C. A., Alexander, K. E., Matese, J. C., Perou, C. M., Hurt, M. M., Brown, P. O., et al. (2002) · Molecular Biology of the Cell · doi:10.1091/mbc.02-02-0030 · Cited by 1,527 · see citing works
A Comparison of Alternative Models for the Demand for Medical Care
Duan, N.-H., Manning, W., Morris, C., & Newhouse, J. (1983) · Journal of Business and Economic Statistics · doi:10.1080/07350015.1983.10509330 · Cited by 1,410 · see citing works
Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells
Shalek, A. K., Satija, R., Adiconis, X., Gertner, R. S., Gaublomme, J. T., Raychowdhury, R., Schwartz, S., Yosef, N., Malboeuf, C., Lu, D., et al. (2013) · Nature · doi:10.1038/nature12172 · Cited by 1,190 · see citing works
Buganim, Y., Faddah, D. A., Cheng, A. W., Itskovich, E., Markoulaki, S., Ganz, K., Klemm, S. L., van Oudenaarden, A., & Jaenisch, R. (2012) · Cell · doi:10.1016/j.cell.2012.08.023 · Cited by 800 · see citing works
High-dimensional covariance estimation by minimizing ℓ1-penalized log-determinant divergence
Ravikumar, P., Wainwright, M. J., Raskutti, G., & Yu, B. (2011) · Electronic Journal of Statistics · doi:10.1214/11-ejs631 · Cited by 746 · see citing works
Dynamic regulatory network controlling TH17 cell differentiation
Yosef, N., Shalek, A. K., Gaublomme, J. T., Jin, H., Lee, Y., Awasthi, A., Wu, C., Karwacz, K., Xiao, S., Jorgolli, M., et al. (2013) · Nature · doi:10.1038/nature11981 · Cited by 682 · see citing works
Single-Cell Gene Expression Profiling
Levsky, J. M., Shenoy, S. M., Pezo, R. C., & Singer, R. H. (2002) · Science · doi:10.1126/science.1072241 · Cited by 526 · see citing works
A double‐hurdle model of cigarette consumption
Jones, A. M. (1989) · Journal of Applied Econometrics · doi:10.1002/jae.3950040103 · Cited by 422 · see citing works
Data exploration, quality control and testing in single-cell qPCR-based gene expression experiments
McDavid, A., Finak, G., Chattopadyay, P. K., Dominguez, M., Lamoreaux, L., Ma, S. S., Roederer, M., & Gottardo, R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts714 · Cited by 395 · see citing works
Self-citationOn the Distribution of a Positive Random Variable Having a Discrete Probability Mass at the Origin
Aitchison, J. (1955) · Journal of the American Statistical Association · doi:10.1080/01621459.1955.10501976 · Cited by 386 · see citing works
Development and applications of single-cell transcriptome analysis
Tang, F., Lao, K., & Surani, M. A. (2011) · Nature Methods · doi:10.1038/nmeth.1557 · Cited by 337 · see citing works
Johnson, V., Scott, M. I. F., Holt, S. V., Hussein, D. M., & Taylor, S. S. (2004) · Journal of Cell Science · doi:10.1242/jcs.01006 · Cited by 336 · see citing works
Jensen, K. B., & Watt, F. M. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0601886103 · Cited by 321 · see citing works
Single cell transcriptional profiling reveals heterogeneity of human induced pluripotent stem cells
Narsinh, K. H., Sun, N., Sánchez-Freire, V., Lee, A. S., de Almeida, P. E., Hu, S., Jan, T. A., Wilson, K. D., Leong, D. E., Rosenberg, J., et al. (2011) · Journal of Clinical Investigation · doi:10.1172/jci44635 · Cited by 277 · see citing works
Tomer Kalisky, C., & Quake, S. R. (2011) · Nature Methods · doi:10.1038/nmeth0411-311 · Cited by 270 · see citing works
Automated gating of flow cytometry data via robust model‐based clustering
Lo, K., Brinkman, R. R., & Gottardo, R. (2008) · Cytometry Part A · doi:10.1002/cyto.a.20531 · Cited by 268 · see citing works
Self-citationOn the Distribution of a Positive Random Variable Having a Discrete Probability Mass at the Origin
Aitchison, J. (1955) · Journal of the American Statistical Association · doi:10.2307/2281175 · Cited by 174 · see citing works
Cell-Cycle Dependence of Transcription Dominates Noise in Gene Expression
Maheshri, N., Zopf, C. J., Quinn, K., & Zeidman, J. (2013) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 164 · see citing works
Methods for qPCR gene expression profiling applied to 1440 lymphoblastoid single cells
Livak, K. J., Wills, Q. F., Tipping, A. J., Datta, K., Mittal, R., Goldson, A. J., Sexton, D. W., & Holmes, C. C. (2012) · Methods · doi:10.1016/j.ymeth.2012.10.004 · Cited by 151 · see citing works
Microbial environments confound antibiotic efficacy
Lee, H. H., & Collins, J. J. (2011) · Nature Chemical Biology · doi:10.1038/nchembio.740 · Cited by 143 · see citing works
Graphical Models via Generalized Linear Models
Yang, E., Allen, G. I., Liu, Z., & Ravikumar, P. (2012) · Cited by 126 · see citing works
A Mechanistic Model of PCR for Accurate Quantification of Quantitative PCR Data
Boggy, G. J., & WOOLF, P. J. (2010) · PLoS ONE · doi:10.1371/journal.pone.0012355 · Cited by 99 · see citing works
Ma, L., Liu, J., Liu, L., Duan, G., Wang, Q., Xu, Y., Xia, F., Shan, J., Shen, J., Yang, Z., et al. (2014) · Cancer Research · doi:10.1158/0008-5472.can-13-2171 · Cited by 91 · see citing works
Liu, D., Ding, X., Du, J., Cai, X. M., Huang, Y., Ward, T. L., Shaw, A., Yang, Y., Ren-ming, H., Jin, C., et al. (2007) · Journal of Biological Chemistry · doi:10.1074/jbc.m609026200 · Cited by 90 · see citing works
Hepatitis C virus infects T cells and affects interferon-γ signaling in T cell lines
Kondo, Y., Sung, V. M.-H., Machida, K., Liu, M., & Lai, M. M. C. (2006) · Virology · doi:10.1016/j.virol.2006.11.009 · Cited by 71 · see citing works
Highly multiplexed quantitation of gene expression on single cells
Dominguez, M. H., Chattopadhyay, P. K., Ma, S., Lamoreaux, L., McDavid, A., Finak, G., Gottardo, R., Koup, R. A., & Roederer, M. (2013) · Journal of Immunological Methods · doi:10.1016/j.jim.2013.03.002 · Cited by 66 · see citing works
Self-citationGauthier, N. P., Jensen, L. J., Wernersson, R., Brunak, S., & Jensen, T. S. (2009) · Nucleic Acids Research · doi:10.1093/nar/gkp1044 · Cited by 58 · see citing works
Naka, K., Dansako, H., Kobayashi, N., Ikeda, M., & Kato, N. (2005) · Virology · doi:10.1016/j.virol.2005.10.023 · Cited by 53 · see citing works
Transcript counting in single cells reveals dynamics of rDNA transcription
Tan, R. Z., & van Oudenaarden, A. (2010) · Molecular Systems Biology · doi:10.1038/msb.2010.14 · Cited by 49 · see citing works
Böhmer, R. ‐., & Ellwart, J. W. (1981) · Cell Proliferation · doi:10.1111/j.1365-2184.1981.tb00850.x · Cited by 30 · see citing works
Functional interplay between the cell cycle and cell phenotypes
Chen, W.-C., Wu, P.-H., Phillip, J. M., Khatau, S. B., Choi, J. M., Dallas, M. R., Κωνσταντόπουλος, Κ., Sun, S. X., Lee, J. S. H., Hodzic, D. M., et al. (2013) · Integrative Biology · doi:10.1039/c2ib20246h · Cited by 30 · see citing works
Chen, Y., & Zhong, J. F. (2008) · Methods in molecular biology · doi:10.1007/978-1-59745-133-8_22 · Cited by 20 · see citing works
Cell-cycle dependence of transcription dominates noise in gene expression
Zopf, C. J., Quinn, K., Zeidman, J., & Maheshri, N. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003161 · Cited by 4 · see citing works
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Mitroulis, I., Ruppova, K., Wang, B., Chen, L.-S., Grzybek, M., Grinenko, T., Eugster, A., Troullinaki, M., Palladini, A., Kourtzelis, I., et al. (2018) · Cell · doi:10.1016/j.cell.2017.11.034 · Cited by 1,100 · see citing works
Revealing the vectors of cellular identity with single-cell genomics
Wagner, A., Regev, A., & Yosef, N. (2016) · Nature Biotechnology · doi:10.1038/nbt.3711 · Cited by 632 · see citing works
Gene Regulatory Network Inference from Single-Cell Data Using Multivariate Information Measures
Chan, T. E., Stumpf, M. P. H., & Babtie, A. C. (2017) · Cell Systems · doi:10.1016/j.cels.2017.08.014 · Cited by 578 · see citing works
CIDR: Ultrafast and accurate clustering through imputation for single-cell RNA-seq data
Lin, P., Troup, M., & Ho, J. W. K. (2017) · Genome biology · doi:10.1186/s13059-017-1188-0 · Cited by 529 · see citing works
Single-Cell Analysis in Cancer Genomics
Saadatpour, A., Lai, S., Guo, G., & Yuan, G. (2015) · Trends in Genetics · doi:10.1016/j.tig.2015.07.003 · Cited by 222 · see citing works
Single cell molecular alterations reveal target cells and pathways of concussive brain injury
Arneson, D., Zhang, G., Ying, Z., Zhuang, Y., Byun, H. R., Ahn, I. S., Gomez-Pinilla, F., & Yang, X. (2018) · Nature Communications · doi:10.1038/s41467-018-06222-0 · Cited by 193 · see citing works
Reconstructing cell cycle pseudo time-series via single-cell transcriptome data
Liu, Z., Lou, H., Xie, K., Wang, H. E., Chen, N., Aparicio, O. M., Zhang, M. Q., Jiang, R., & Chen, T. (2017) · Nature Communications · doi:10.1038/s41467-017-00039-z · Cited by 156 · see citing works
Distinct activation thresholds of human conventional and innate-like memory T cells
Slichter, C. K., McDavid, A., Miller, H. W., Finak, G., Seymour, B. J., McNevin, J. P., Diaz, G. M., Czartoski, J., McElrath, M. J., Gottardo, R., et al. (2016) · JCI Insight · doi:10.1172/jci.insight.86292 · Cited by 132 · see citing works
Self-citationTranscriptional and Translational Heterogeneity among Neonatal Mouse Spermatogonia1
Hermann, B. P., Mutoji, K. N., Velte, E. K., Ko, D., Oatley, J. M., Geyer, C. B., & McCarrey, J. R. (2015) · Biology of Reproduction · doi:10.1095/biolreprod.114.125757 · Cited by 104 · see citing works
Grinenko, T., Eugster, A., Thielecke, L., Ramasz, B., Krüger, A., Dietz, S., Glauche, I., Gerbaulet, A., Bonin, M. V., Başak, O., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-04188-7 · Cited by 87 · see citing works
Geometry of the Gene Expression Space of Individual Cells
Korem, Y., Szekely, P., Hart, Y., Sheftel, H., Hausser, J., Mayo, A., Rothenberg, M. E., Kalisky, T., & Alon, U. (2015) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1004224 · Cited by 85 · see citing works
Cerosaletti, K. M., Barahmand-pour-Whitman, F., Yang, J., DeBerg, H. A., Dufort, M. J., Murray, S. A., Israelsson, E., Speake, C., Gersuk, V. H., Eddy, J. A., et al. (2017) · The Journal of Immunology · doi:10.4049/jimmunol.1700172 · Cited by 81 · see citing works
Self-citationAntigen discovery and specification of immunodominance hierarchies for MHCII-restricted epitopes
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Dueck, H., Eberwine, J. H., & Kim, J. (2015) · BioEssays · doi:10.1002/bies.201500124 · Cited by 76 · see citing works
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Heninger, A., Eugster, A., Kuehn, D., Buettner, F., Kühn, M., Lindner, A., Dietz, S., Jergens, S., Wilhelm, C., Beyerlein, A., et al. (2017) · Science Translational Medicine · doi:10.1126/scitranslmed.aaf8848 · Cited by 72 · see citing works
Carmona, S. J., Siddiqui, I., Bilous, M., Held, W., & Gfeller, D. (2020) · OncoImmunology · doi:10.1080/2162402x.2020.1737369 · Cited by 65 · see citing works
Wu, L. L., Zhang, X., Zhao, Z., Wang, L., Li, B., Li, G., Dean, M. C., Yu, Q., Wang, Y., Lin, X., et al. (2015) · GigaScience · doi:10.1186/s13742-015-0091-4 · Cited by 59 · see citing works
The contribution of cell cycle to heterogeneity in single-cell RNA-seq data
McDavid, A., Finak, G., & Gottardo, R. (2016) · Nature Biotechnology · doi:10.1038/nbt.3498 · Cited by 57 · see citing works
Self-citationGrandPrix: scaling up the Bayesian GPLVM for single-cell data
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Juliá, M., Telenti, A., & Rausell, A. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv368 · Cited by 55 · see citing works
Latent periodic process inference from single-cell RNA-seq data
Liang, S., Wang, F., Han, J., & Chen, K. (2020) · Nature Communications · doi:10.1038/s41467-020-15295-9 · Cited by 52 · see citing works
Single-Cell Expression Variability Implies Cell Function
Osorio, D., Yu, X., Zhong, Y., Li, G., Yu, P., Serpedin, E., Huang, J., & Cai, J. J. (2019) · Cells · doi:10.3390/cells9010014 · Cited by 43 · see citing works
TSPAN8 Expression Distinguishes Spermatogonial Stem Cells in the Prepubertal Mouse Testis
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Gene Expression-Based Identification of Antigen-Responsive CD8+ T Cells on a Single-Cell Level
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Tagliafierro, L., Bonawitz, K., Glenn, O., & Chiba‐Falek, O. (2016) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2016.00072 · Cited by 25 · see citing works
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Bolton, D. L., McGinnis, K., Finak, G., Chattopadhyay, P. K., Gottardo, R., & Roederer, M. (2017) · PLoS Pathogens · doi:10.1371/journal.ppat.1006445 · Cited by 22 · see citing works
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A novel method for quantitative measurements of gene expression in single living cells
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Schultze, A. E., Bennet, B. M., Rae, J. M. C., Chiang, A. Y., Frazier, K. S., Katavolos, P., McKinney, L., Patrick, D. J., & Tripathi, N. (2020) · Toxicologic Pathology · doi:10.1177/0192623320906385 · Cited by 11 · see citing works
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Haider, M., Haselgrübler, T., Sonnleitner, A., Aberger, F., & Hesse, J. (2016) · Microarrays · doi:10.3390/microarrays5010005 · Cited by 7 · see citing works
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Böttcher, M. E., Kouno, T., Madissoon, E., Motakis, E., Abugessaisa, I., Kato, S., Suzuki, H., Hayashizaki, Y., Kasukawa, T., Carninci, P., et al. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/088500 · Cited by 3 · see citing works
Osorio, D., Yu, X. Q., Zhong, Y., Li, G., Yu, P., Serpedin, E., Huang, J., & Cai, J. J. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3377618 · Cited by 2 · see citing works
Automated cell cycle and cell size measurements for single-cell gene expression studies
Guillemin, A., Richard, A., Gonin-Giraud, S., & Gandrillon, O. (2018) · BMC Research Notes · doi:10.1186/s13104-018-3195-y · Cited by 1 · see citing works
Single-Cell Analysis in Cancer
Saadatpour, G., Lai, S., Guo, G., & Yuan, G. (2015)
Multiplexed Transcriptomic Profiling Using Color-Coded Probe Pairs
Wyllie, A. K., & Herazo‐Maya, J. D. (2016) · WORLD SCIENTIFIC eBooks · doi:10.1142/9789814678735_0009
Sincell: R package for statistical assessment of cell state hierarchies from single-cell RNA-seq
Juli, M., Telenti, A., & Rausell, A. (2016)
Mitochondrial control of gene expression and extrinsic apoptosis
Colunga, J. D. (2019) · LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)
Yurovsky, A., Futcher, B., & Skiena, S. (2019) · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) · doi:10.1145/3307339.3342174
Computational approaches towards reducing contamination in single-cell RNA-seq dataPreprint
Yousefi, S., Chen, H., Ingels, J. F., McCarty, M. S., Centeno, A. G., Chintalapudi, S. R., Mulligan, M. K., Williams, P. A., John, S. W. M., Jones, B. W., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.07.15.205062
CycleMix: Gaussian mixture modeling of the cell cycle
Peplinski, J. E., & Andrews, T. S. (2026) · Bioinformatics Advances · doi:10.1093/bioadv/vbag179
Scalable Optimization Algorithms for High-throughput Genomic Data
Lindsay, J. R. (2015) · OpenCommons - UConn (University of Connecticut)
- Authors
- McDavid, A., Dennis, L., Danaher, P., Finak, G., Krouse, M., Wang, A., Webster, P., Beechem, J., & Gottardo, R.
- Affiliations
- University of Washington (US)
- Nanostring Technologies (United States) (US)
- Fred Hutch Cancer Center (US)
- BD Biosciences (United States) (US)
- Corresponding author
- McDavid, Andrew
- Dennis, Lucas
- Danaher, Patrick
- Finak, Greg
- Krouse, Michael
- Wang, Alice
- Webster, Philippa
- Beechem, Joseph
- Venue
- PLoS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2014-07-17
- Volume
- 10
- Issue
- 7
- Pages
- e1003696
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC4102402
- OpenAlex
- W2952877818
- Cited by
- 58
- References
- 45
- Citations by year
- 2026: 1 · 2025: 1 · 2023: 1 · 2022: 4 · 2021: 1 · 2020: 5 · 2019: 5 · 2018: 7 · 2017: 8 · 2016: 14 · 2015: 11
- FWCI
- 4.51
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Funders
- National Institute of Allergy and Infectious Diseases (U19AI089986)
- National Institute of Biomedical Imaging and Bioengineering (R01EB008400)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- cell cycle phases, single-cell gene expression, cell cycle genes, differential gene expression, generalized linear models, gene co-expression network, cellular heterogeneity, expression variability
- MeSH terms
- Cell Cycle, Cell Line, Humans, Models, Genetic, Gene Expression, Genes, cdc, Computational Biology, Gene Expression Profiling, Gene Regulatory Networks
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