Challenges in measuring and understanding biological noise
Open accessAbstract
Biochemical reactions are intrinsically stochastic, leading to variation in the production of mRNAs and proteins within cells. In the scientific literature, this source of variation is typically referred to as 'noise'. The observed variability in molecular phenotypes arises from a combination of processes that amplify and attenuate noise. Our ability to quantify cell-to-cell variability in numerous biological contexts has been revolutionized by recent advances in single-cell technology, from imaging approaches through to 'omics' strategies. However, defining, accurately measuring and disentangling the stochastic and deterministic components of cell-to-cell variability is challenging. In this Review, we discuss the sources, impact and function of molecular phenotypic variability and highlight future directions to understand its role.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Existing cell models are often rule-based and combine assumptions about the underlying biological mechanisms with parameters fit from observational data.
“Existing cell models are often rule-based and combine assumptions about the underlying biological mechanisms with parameters fit from observational data.”
From How to build the virtual cell with artificial intelligence: Priorities and opportunities (2024) · cites this paper as [11]
The Introduction section on measuring phenotypic variability describes a mechanistic model that assumes promoters switch between ON and OFF states and infers transcription parameters from single-cell RNA-sequencing data (page unavailable). This supports an example of combining a biological mechanism with data-derived parameters, but the paper does not establish that existing cell models are often rule-based. (LLM, conf=0.78)
Experimental — accuracy not yet measuredExisting cell models generally rely on explicitly defined mathematical or computational approaches, such as differential equations, stochastic simulations, or agent-based models.
“They generally rely on explicitly defined mathematical or computational approaches, such as differential equations,7–9 stochastic simulations,10,11 or agent-based models.12,13”
From How to build the virtual cell with artificial intelligence: Priorities and opportunities (2024) · cites this paper as [11]
The Introduction (The role of molecular phenotypic variability; page unavailable) describes stochastic and deterministic models of a cellular decision, supporting the general point that cell processes are modeled mathematically. It does not establish that existing cell models generally use the listed approaches, or document differential equations and agent-based models as examples. (LLM, conf=0.80)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
López-Otín, C., Blasco, M. A., Partridge, L., Serrano, M., & Kroemer, G. (2013) · Cell · doi:10.1016/j.cell.2013.05.039 · Cited by 14,982 · see citing works
Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets.
Macosko, E. Z., Basu, A., Satija, R., Nemesh, J., Shekhar, K., Goldman, M., Tirosh, I., Bialas, A. R., Kamitaki, N., Martersteck, E. M., et al. (2015) · Cell · doi:10.1016/j.cell.2015.05.002 · Cited by 7,110 · 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
Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells
Klein, A. M., Mazutis, L., Akartuna, I., Tallapragada, N., Veres, A., Li, V., Peshkin, L., Weitz, D. A., & Kirschner, M. W. (2015) · Cell · doi:10.1016/j.cell.2015.04.044 · Cited by 3,356 · see citing works
Simultaneous epitope and transcriptome measurement in single cells
Stoeckius, M., Hafemeister, C., Stephenson, W., Houck-Loomis, B., Chattopadhyay, P. K., Swerdlow, H., Satija, R., & Smibert, P. (2017) · Nature Methods · doi:10.1038/nmeth.4380 · Cited by 3,098 · see citing works
Spatially resolved, highly multiplexed RNA profiling in single cells
Chen, K. H., Boettiger, A. N., Moffitt, J. R., Wang, S., & Zhuang, X. (2015) · Science · doi:10.1126/science.aaa6090 · Cited by 2,969 · see citing works
Epigenetic modifications and human disease
Portela, A., & Esteller, M. (2010) · Nature Biotechnology · doi:10.1038/nbt.1685 · Cited by 2,843 · 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
Bendall, S. C., Simonds, E. F., Qiu, P., Amir, E.-A. D., Krutzik, P. O., Finck, R., Bruggner, R. V., Melamed, R., Trejo, A., Ornatsky, O. I., et al. (2011) · Science · doi:10.1126/science.1198704 · Cited by 2,279 · see citing works
Intra-tumour heterogeneity: a looking glass for cancer?
Marusyk, A., Almendro, V., & Polyák, K. (2012) · Nature reviews. Cancer · doi:10.1038/nrc3261 · Cited by 2,003 · see citing works
CRISPR-Cas9 Knockin Mice for Genome Editing and Cancer Modeling
Platt, R. J., Chen, S., Zhou, Y., Yim, M. J., Swiech, L., Kempton, H. R., Dahlman, J. E., Parnas, O., Eisenhaure, T. M., Jovanovic, M., et al. (2014) · Cell · doi:10.1016/j.cell.2014.09.014 · Cited by 2,001 · see citing works
Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry
Giesen, C., Wang, H. A. O., Schapiro, D., Zivanovic, N., Jacobs, A., Hattendorf, B., Schüffler, P. J., Grolimund, D., Buhmann, J. M., Brandt, S., et al. (2014) · Nature Methods · doi:10.1038/nmeth.2869 · Cited by 1,951 · see citing works
Stochastic mRNA Synthesis in Mammalian Cells
Raj, A., Peskin, C. S., Tranchina, D., Vargas, D. Y., & Tyagi, S. (2006) · PLoS Biology · doi:10.1371/journal.pbio.0040309 · Cited by 1,737 · see citing works
Noise in Gene Expression: Origins, Consequences, and Control
Raser, J. M., & O’Shea, E. K. (2005) · Science · doi:10.1126/science.1105891 · Cited by 1,719 · see citing works
Intrinsic and extrinsic contributions to stochasticity in gene expression
Swain, P. S., Elowitz, M. B., & Siggia, E. D. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.162041399 · Cited by 1,661 · see citing works
Noise in eukaryotic gene expression
Blake, W. J., Kærn, M., Cantor, C. R., & Collins, J. J. (2003) · Nature · doi:10.1038/nature01546 · Cited by 1,636 · see citing works
Regulation of noise in the expression of a single gene
Özbudak, E. M., Thattai, M., Kurtser, I., Grossman, A. D., & van Oudenaarden, A. (2002) · Nature Genetics · doi:10.1038/ng869 · Cited by 1,559 · see citing works
Arkin, A. P., Ross, J., & McAdams, H. H. (1998) · Genetics · doi:10.1093/genetics/149.4.1633 · Cited by 1,492 · see citing works
The Technology and Biology of Single-Cell RNA Sequencing
Kolodziejczyk, A. A., Kim, J. K., Svensson, V., Marioni, J. C., & Teichmann, S. A. (2015) · Molecular Cell · doi:10.1016/j.molcel.2015.04.005 · Cited by 1,487 · see citing works
Self-citationSingle-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise
Newman, J. R. S., Ghaemmaghami, S., Ihmels, J., Breslow, D. K., Noble, M., DeRisi, J. L., & Weissman, J. S. (2006) · Nature · doi:10.1038/nature04785 · Cited by 1,483 · see citing works
Real-Time Kinetics of Gene Activity in Individual Bacteria
Golding, I., Paulsson, J., Zawilski, S. M., & Cox, E. C. (2005) · Cell · doi:10.1016/j.cell.2005.09.031 · Cited by 1,468 · see citing works
Functional roles for noise in genetic circuits
Eldar, A., & Elowitz, M. B. (2010) · Nature · doi:10.1038/nature09326 · Cited by 1,440 · see citing works
Control of Stochasticity in Eukaryotic Gene Expression
Raser, J. M., & O'Shea, E. K. (2004) · Science · doi:10.1126/science.1098641 · Cited by 1,425 · see citing works
Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding
Rosenberg, A. B., Roco, C. M., Muscat, R. A., Kuchina, A., Sample, P., Yao, Z., Graybuck, L. T., Peeler, D. J., Mukherjee, S., Chen, W., et al. (2018) · Science · doi:10.1126/science.aam8999 · Cited by 1,422 · see citing works
Cao, J., Packer, J. S., Ramani, V., Cusanovich, D. A., Huynh, C., Daza, R., Qiu, X., Lee, C., Furlan, S. N., Steemers, F. J., et al. (2017) · doi:10.1101/104844 · Cited by 1,402 · see citing works
Multiplex single-cell profiling of chromatin accessibility by combinatorial cellular indexing
Cusanovich, D. A., Daza, R., Adey, A., Pliner, H. A., Christiansen, L., Gunderson, K. L., Steemers, F. J., Trapnell, C., & Shendure, J. (2015) · Science · doi:10.1126/science.aab1601 · Cited by 1,248 · see citing works
Rare cell variability and drug-induced reprogramming as a mode of cancer drug resistance
Shaffer, S. M., Dunagin, M. C., Torborg, S. R., Torre, E. A., Emert, B., Krepler, C., Beqiri, M., Sproesser, K., Brafford, P. A., Xiao, M., et al. (2017) · Nature · doi:10.1038/nature22794 · Cited by 1,218 · see citing works
Quantitative single-cell RNA-seq with unique molecular identifiers
Islam, S., Zeisel, A., Joost, S., La Manno, G., Zajac, P., Kasper, M., Lönnerberg, P., & Linnarsson, S. (2014) · Nature Methods · doi:10.1038/nmeth.2772 · Cited by 1,183 · see citing works
Genome-Wide Detection of Single-Nucleotide and Copy-Number Variations of a Single Human Cell
Zong, C., Lu, S., Chapman, A. R., & Xie, X. S. (2012) · Science · doi:10.1126/science.1229164 · Cited by 1,154 · see citing works
Challenges in unsupervised clustering of single-cell RNA-seq data
Kiselev, V. Y., Andrews, T. S., & Hemberg, M. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-018-0088-9 · Cited by 1,149 · see citing works
Buettner, F., Natarajan, K. N., Casale, F. P., Proserpio, V., Scialdone, A., Theis, F. J., Teichmann, S. A., Marioni, J. C., & Stegle, O. (2015) · Nature Biotechnology · doi:10.1038/nbt.3102 · Cited by 1,136 · see citing works
Self-citationTranscriptome-wide noise controls lineage choice in mammalian progenitor cells
Chang, H. H., Hemberg, M., Barahona, M., Ingber, D. E., & Huang, S. (2008) · Nature · doi:10.1038/nature06965 · Cited by 1,067 · see citing works
Tuning gene expression to changing environments: from rapid responses to evolutionary adaptation
López-Maury, L., Marguerat, S., & Bähler, J. (2008) · Nature Reviews Genetics · doi:10.1038/nrg2398 · Cited by 1,061 · see citing works
Stoeckius, M., Zheng, S., Houck-Loomis, B., Hao, S., Yeung, B. Z., Mauck, W. M., Smibert, P., & Satija, R. (2018) · Genome Biology · doi:10.1186/s13059-018-1603-1 · Cited by 1,046 · see citing works
Non-genetic origins of cell-to-cell variability in TRAIL-induced apoptosis
Spencer, S. L., Gaudet, S., Albeck, J. G., Burke, J. M., & Sorger, P. K. (2009) · Nature · doi:10.1038/nature08012 · Cited by 1,023 · see citing works
Cellular Decision Making and Biological Noise: From Microbes to Mammals
Balázsi, G., van Oudenaarden, A., & Collins, J. J. (2011) · Cell · doi:10.1016/j.cell.2011.01.030 · Cited by 1,017 · see citing works
Single-cell RNA-seq reveals dynamic paracrine control of cellular variation
Shalek, A. K., Satija, R., Shuga, J., Trombetta, J. J., Gennert, D., Lu, D., Chen, P., Gertner, R. S., Gaublomme, J. T., Yosef, N., et al. (2014) · Nature · doi:10.1038/nature13437 · Cited by 1,000 · see citing works
Accounting for technical noise in single-cell RNA-seq experiments
Brennecke, P., Anders, S., Kim, J. K., Kołodziejczyk, A. A., Zhang, X., Proserpio, V., Baying, B., Benes, V., Teichmann, S. A., Marioni, J. C., et al. (2013) · Nature Methods · doi:10.1038/nmeth.2645 · Cited by 979 · see citing works
Self-citationTranscription factors: an overview.
Latchman, D. (1997) · International Journal of Biochemistry and Cell Biology · doi:10.1016/s1357-2725(97)00085-x · Cited by 938 · see citing works
Noise in protein expression scales with natural protein abundance
Bar-Even, A., Paulsson, J., Maheshri, N., Carmi, M., O'Shea, E., Pilpel, Y., & Barkai, N. (2006) · Nature Genetics · doi:10.1038/ng1807 · Cited by 807 · see citing works
G&T-seq: parallel sequencing of single-cell genomes and transcriptomes
Macaulay, I. C., Haerty, W., Kumar, P., Li, Y. I., Hu, T. X., Teng, M. J., Goolam, M., Saurat, N., Coupland, P., Shirley, L. M., et al. (2015) · Nature Methods · doi:10.1038/nmeth.3370 · Cited by 750 · see citing works
Markovian Modeling of Gene-Product Synthesis
Peccoud, J., & Ycart, B. (1995) · Theoretical Population Biology · doi:10.1006/tpbi.1995.1027 · Cited by 739 · see citing works
Validation of noise models for single-cell transcriptomics
Grün, D., Kester, L., & van Oudenaarden, A. (2014) · Nature Methods · doi:10.1038/nmeth.2930 · Cited by 721 · see citing works
Enhancer Control of Transcriptional Bursting
Fukaya, T., Lim, B., & Levine, M. (2016) · Cell · doi:10.1016/j.cell.2016.05.025 · Cited by 721 · see citing works
Parallel single-cell sequencing links transcriptional and epigenetic heterogeneity
Angermueller, C., Clark, S. J., Lee, H. J., Macaulay, I. C., Teng, M. J., Hu, T. X., Krueger, F., Smallwood, S. A., Ponting, C. P., Voet, T., et al. (2016) · Nature Methods · doi:10.1038/nmeth.3728 · Cited by 717 · see citing works
An excitable gene regulatory circuit induces transient cellular differentiation
Süel, G. M., García‐Ojalvo, J., Liberman, L. M., & Elowitz, M. B. (2006) · Nature · doi:10.1038/nature04588 · Cited by 714 · see citing works
Transcriptional Pulsing of a Developmental Gene
Chubb, J. R., Trcek, T., Shenoy, S. M., & Singer, R. H. (2006) · Current Biology · doi:10.1016/j.cub.2006.03.092 · Cited by 714 · see citing works
Single-RNA counting reveals alternative modes of gene expression in yeast
Zenklusen, D., Larson, D. R., & Singer, R. H. (2008) · Nature Structural & Molecular Biology · doi:10.1038/nsmb.1514 · Cited by 684 · see citing works
Shah, S., Lubeck, E., Zhou, W., & Cai, L. (2016) · Neuron · doi:10.1016/j.neuron.2016.10.001 · Cited by 671 · see citing works
Clark, S. J., Argelaguet, R., Kapourani, C.-A., Stubbs, T. M., Lee, H. J., Alda-Catalinas, C., Krueger, F., Sanguinetti, G., Kelsey, G., Marioni, J. C., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-03149-4 · Cited by 657 · see citing works
Self-citationVariability in gene expression underlies incomplete penetrance
Raj, A., Rifkin, S. A., Andersen, E. C., & van Oudenaarden, A. (2010) · Nature · doi:10.1038/nature08781 · Cited by 642 · see citing works
An atlas of the aging lung mapped by single cell transcriptomics and deep tissue proteomics
Angelidis, I., Simon, L. M., Fernandez, I. E., Strunz, M., Mayr, C. H., Greiffo, F. R., Tsitsiridis, G., Ansari, M., Graf, E., Strom, T.-M., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-08831-9 · Cited by 637 · see citing works
Single Cell RNA-Sequencing of Pluripotent States Unlocks Modular Transcriptional Variation
Kolodziejczyk, A. A., Kim, J. K., Tsang, J. C. H., Ilicic, T., Henriksson, J., Natarajan, K. N., Tuck, A. C., Gao, X., Bühler, M., Liu, P., et al. (2015) · Cell Stem Cell · doi:10.1016/j.stem.2015.09.011 · Cited by 570 · see citing works
Self-citationEnge, M., Arda, H. E., Mignardi, M., Beausang, J., Bottino, R., Kim, S. K., & Quake, S. R. (2017) · Cell · doi:10.1016/j.cell.2017.09.004 · Cited by 568 · see citing works
Huang, S., Ernberg, I. T., & Kauffman, S. A. (2009) · Seminars in Cell and Developmental Biology · doi:10.1016/j.semcdb.2009.07.003 · Cited by 556 · see citing works
Genomic encoding of transcriptional burst kinetics
Larsson, A. J. M., Johnsson, P., Hagemann-Jensen, M., Hartmanis, L., Faridani, O. R., Reinius, B., Segerstolpe, Å., Rivera, C. M., Ren, B., & Sandberg, R. (2019) · Nature · doi:10.1038/s41586-018-0836-1 · Cited by 534 · see citing works
Multiplexed protein maps link subcellular organization to cellular states
Gut, G., Herrmann, M. D., & Pelkmans, L. (2018) · Science · doi:10.1126/science.aar7042 · Cited by 510 · see citing works
Stochastic patterning in the mouse pre-implantation embryo
Dietrich, J. E., & Hiiragi, T. (2007) · Development · doi:10.1242/dev.003798 · Cited by 503 · see citing works
Integrated genome and transcriptome sequencing of the same cell
Dey, S. S., Kester, L., Spanjaard, B., Bienko, M., & van Oudenaarden, A. (2015) · Nature Biotechnology · doi:10.1038/nbt.3129 · Cited by 502 · see citing works
Signals and Combinatorial Functions of Histone Modifications
Suganuma, T., & Workman, J. L. (2011) · Annual Review of Biochemistry · doi:10.1146/annurev-biochem-061809-175347 · Cited by 498 · see citing works
Emergence of reproducible spatiotemporal activity during motor learning
Peters, A. J., Chen, S. X., & Komiyama, T. (2014) · Nature · doi:10.1038/nature13235 · Cited by 491 · see citing works
Heterogeneity in Oct4 and Sox2 Targets Biases Cell Fate in 4-Cell Mouse Embryos
Goolam, M., Scialdone, A., Graham, S. J. L., Macaulay, I. C., Jedrusik, A., Hupalowska, A., Voet, T., Marioni, J. C., & Zernicka-Goetz, M. (2016) · Cell · doi:10.1016/j.cell.2016.01.047 · Cited by 484 · see citing works
Self-citationNon-genetic heterogeneity of cells in development: more than just noise
Huang, S. (2009) · Development · doi:10.1242/dev.035139 · Cited by 482 · see citing works
Genetic Determinants and Cellular Constraints in Noisy Gene Expression
Sánchez, Á., & Golding, I. (2013) · Science · doi:10.1126/science.1242975 · Cited by 460 · see citing works
Single-cell multiomics: multiple measurements from single cells
Macaulay, I. C., Ponting, C. P., & Voet, T. (2017) · Trends in Genetics · doi:10.1016/j.tig.2016.12.003 · Cited by 451 · see citing works
Padovan-Merhar, O. M., Nair, G. P., Biaesch, A. G., Mayer, A., Scarfone, S., Foley, S. W., Wu, A. R., Churchman, L. S., Singh, A., & Raj, A. (2015) · Molecular Cell · doi:10.1016/j.molcel.2015.03.005 · Cited by 435 · see citing works
Regulated cell-to-cell variation in a cell-fate decision system
Colman‐Lerner, A., Gordon, A. H., Serra, E., Chin, T., Resnekov, O., Endy, D., Pesce, C. G., & Brent, R. (2005) · Nature · doi:10.1038/nature03998 · Cited by 428 · see citing works
Variability in gene expression underlies incomplete penetrance
van Oudenaarden, A., Rifkin, S. A., Raj, A., & Andersen, E. C. (2010) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 424 · see citing works
Stochasticity of metabolism and growth at the single-cell level
Kiviet, D. J., Nghe, P., Walker, N., Boulineau, S., Šunderlíková, V., & Tans, S. J. (2014) · Nature · doi:10.1038/nature13582 · Cited by 419 · see citing works
MicroRNA control of protein expression noise
Schmiedel, J. M., Klemm, S. L., Zheng, Y., Sahay, A., Blüthgen, N., Marks, D. S., & van Oudenaarden, A. (2015) · Science · doi:10.1126/science.aaa1738 · Cited by 395 · see citing works
Landau, D. A., Clement, K., Ziller, M. J., Boyle, P., Fan, J., Gu, H., Stevenson, K. E., Sougnez, C. L., Wang, L., Li, S., et al. (2014) · Cancer Cell · doi:10.1016/j.ccell.2014.10.012 · Cited by 385 · see citing works
Two strategies for gene regulation by promoter nucleosomesPreprint
Tirosh, I., & Barkai, N. (2008) · Genome Research · doi:10.1101/gr.076059.108 · Cited by 383 · see citing works
Asymmetric division of contractile domains couples cell positioning and fate specification
Maître, J., Turlier, H., Illukkumbura, R., Eismann, B., Niwayama, R., Nédélec, F., & Hiiragi, T. (2016) · Nature · doi:10.1038/nature18958 · Cited by 381 · see citing works
Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis
Fan, J., Salathia, N. S., Liu, R., Kaeser, G. E., Yung, Y. C., Herman, J. L., Kaper, F., Fan, J., Zhang, K., Chun, J. J. M., et al. (2016) · Nature Methods · doi:10.1038/nmeth.3734 · Cited by 369 · see citing works
Cell Fate Decision as High-Dimensional Critical State Transition
Mojtahedi, M., Skupin, A., Zhou, J., Castaño, I. G., Leong-Quong, R. Y. Y., Chang, H. H., Trachana, K., Giuliani, A., & Huang, S. (2016) · PLoS Biology · doi:10.1371/journal.pbio.2000640 · Cited by 346 · see citing works
Aging increases cell-to-cell transcriptional variability upon immune stimulation
Martinez-Jimenez, C. P., Eling, N., Chen, H.-C., Vallejos, C. A., Kolodziejczyk, A. A., Connor, F., Stojic, L., Rayner, T. F., Stubbington, M. J. T., Teichmann, S. A., et al. (2017) · Science · doi:10.1126/science.aah4115 · Cited by 339 · see citing works
Self-citationScaling by shrinking: empowering single-cell 'omics' with microfluidic devices
Prakadan, S. M., Shalek, A. K., & Weitz, D. A. (2017) · Nature Reviews Genetics · doi:10.1038/nrg.2017.15 · Cited by 337 · see citing works
Highly multiplexed simultaneous detection of RNAs and proteins in single cells
Frei, A. P., Bava, F.-A., Zunder, E. R., Hsieh, E. W. Y., Chen, S.-Y., Nolan, G. P., & Gherardini, P. F. (2016) · Nature Methods · doi:10.1038/nmeth.3742 · Cited by 330 · see citing works
Control of Transcript Variability in Single Mammalian Cells
Battich, N., Stoeger, T., & Pelkmans, L. (2015) · Cell · doi:10.1016/j.cell.2015.11.018 · Cited by 324 · see citing works
Mohammed, H., Hernando-Herraez, I., Savino, A., Scialdone, A., Macaulay, I. C., Mulas, C., Chandra, T., Voet, T., Dean, W., Nichols, J., et al. (2017) · Cell Reports · doi:10.1016/j.celrep.2017.07.009 · Cited by 324 · see citing works
Self-citationSingle-cell chromatin modification profiling reveals increased epigenetic variations with aging
Cheung, P., Vallania, F., Warsinske, H. C., Donato, M., Schaffert, S., Chang, S. E., Dvorak, M., Dekker, C. L., Davis, M. M., Utz, P. J., et al. (2018) · Cell · doi:10.1016/j.cell.2018.03.079 · Cited by 323 · see citing works
Genetic Properties Influencing the Evolvability of Gene Expression
Landry, C. R., Lemos, B., Rifkin, S. A., Dickinson, W. J., & Hartl, D. L. (2007) · Science · doi:10.1126/science.1140247 · Cited by 314 · see citing works
Single-cell epigenomics: powerful new methods for understanding gene regulation and cell identity
Clark, S. J., Lee, H. J., Smallwood, S. A., Kelsey, G., & Reik, W. (2016) · Genome Biology · doi:10.1186/s13059-016-0944-x · Cited by 310 · see citing works
BASiCS: Bayesian Analysis of Single-Cell Sequencing Data
Vallejos, C. A., Marioni, J. C., & Richardson, S. (2015) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1004333 · Cited by 302 · see citing works
Self-citationOhnishi, Y., Huber, W., Tsumura, A., Kang, M., Xenopoulos, P., Kurimoto, K., Oleś, A. K., Araúzo-Bravo, M. J., Saitou, M., Hadjantonakis, A.-K., et al. (2014) · Nature Cell Biology · doi:10.1038/ncb2881 · Cited by 300 · see citing works
Sequencing thousands of single-cell genomes with combinatorial indexing
Vitak, S. A., Torkenczy, K. A., Rosenkrantz, J. L., Fields, A. J., Christiansen, L., Wong, M. H., Carbone, L., Steemers, F. J., & Adey, A. (2017) · Nature Methods · doi:10.1038/nmeth.4154 · Cited by 294 · see citing works
Cell-to-cell variation in p53 dynamics leads to fractional killing.
Paek, A. L., Liu, J. C., Loewer, A., Forrester, W. C., & Lahav, G. (2016) · Cell · doi:10.1016/j.cell.2016.03.025 · Cited by 292 · see citing works
Highly scalable generation of DNA methylation profiles in single cells
Mulqueen, R. M., Pokholok, D., Norberg, S. J., Torkenczy, K. A., Fields, A. J., Sun, D., Sinnamon, J. R., Shendure, J., Trapnell, C., O'Roak, B. J., et al. (2018) · Nature Biotechnology · doi:10.1038/nbt.4112 · Cited by 289 · see citing works
A genetic signature of interspecies variations in gene expression
Tirosh, I., Weinberger, A., Carmi, M., & Barkai, N. (2006) · Nature Genetics · doi:10.1038/ng1819 · Cited by 276 · see citing works
Decision Making at a Subcellular Level Determines the Outcome of Bacteriophage Infection
Zeng, L., Skinner, S. O., Zong, C. C., Sippy, J., Feiss, M. G., & Golding, I. (2010) · Cell · doi:10.1016/j.cell.2010.03.034 · Cited by 272 · see citing works
Origins of extrinsic variability in eukaryotic gene expression
Volfson, D., Marciniak, J. Y., Blake, W. J., Ostroff, N. A., Tsimring, L. S., & Hasty, J. (2005) · Nature · doi:10.1038/nature04281 · Cited by 269 · see citing works
Intrinsic Dynamics of a Human Gene Reveal the Basis of Expression Heterogeneity
Rodriguez, J., Ren, G., Day, C. R., Zhao, K., Chow, C. C., & Larson, D. R. (2018) · Cell · doi:10.1016/j.cell.2018.11.026 · Cited by 267 · see citing works
Nuclear Retention of mRNA in Mammalian Tissues
Bahar Halpern, K., Caspi, I., Lemze, D., Levy, M., Landen, S., Elinav, E., Ulitsky, I., & Itzkovitz, S. (2015) · Cell Reports · doi:10.1016/j.celrep.2015.11.036 · Cited by 258 · see citing works
Abseq: Ultrahigh-throughput single cell protein profiling with droplet microfluidic barcoding
Shahi, P., Kim, S., Haliburton, J. R., Gartner, Z. J., & Abate, A. R. (2017) · Scientific Reports · doi:10.1038/srep44447 · Cited by 258 · see citing works
Synchronous and Stochastic Patterns of Gene Activation in the Drosophila Embryo
Boettiger, A. N., & Levine, M. (2009) · Science · doi:10.1126/science.1173976 · Cited by 256 · see citing works
Filtering transcriptional noise during development: concepts and mechanisms
Arias, A. M., & Hayward, P. C. (2005) · Nature Reviews Genetics · doi:10.1038/nrg1750 · Cited by 256 · see citing works
Richard, A., Loïs, B., Herbach, U., Bonnafoux, A., Morin, V., Vallin, E., Guillemin, A., Gao, N. P., Gunawan, R., Cosette, J., et al. (2016) · PLoS Biology · doi:10.1371/journal.pbio.1002585 · Cited by 252 · see citing works
Dynamics extracted from fixed cells reveal feedback linking cell growth to cell cycle
Kafri, R., Levy, J., Ginzberg, M. B., Oh, S., Lahav, G., & Kirschner, M. W. (2013) · Nature · doi:10.1038/nature11897 · Cited by 232 · see citing works
Promoter architecture dictates cell-to-cell variability in gene expression
Jones, D., Brewster, R. C., & Phillips, R. (2014) · Science · doi:10.1126/science.1255301 · Cited by 223 · see citing works
Gene expression variability across cells and species shapes innate immunity
Hagai, T., Chen, X., Miragaia, R. J., Rostom, R., Gomes, T., Kunowska, N., Henriksson, J., Park, J.-E., Proserpio, V., Donati, G., et al. (2018) · Nature · doi:10.1038/s41586-018-0657-2 · Cited by 220 · see citing works
Lehner, B. (2008) · Molecular Systems Biology · doi:10.1038/msb.2008.11 · Cited by 213 · see citing works
Heterogeneity in immune responses: from populations to single cells
Satija, R., & Shalek, A. K. (2014) · Trends in Immunology · doi:10.1016/j.it.2014.03.004 · Cited by 210 · see citing works
Determination of cell fate selection during phage lambda infection
St-Pierre, F., & Endy, D. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0808831105 · Cited by 208 · see citing works
Inferring the kinetics of stochastic gene expression from single-cell RNA-sequencing data
Kim, J. K., & Marioni, J. C. (2013) · Genome biology · doi:10.1186/gb-2013-14-1-r7 · Cited by 207 · see citing works
Self-citationSingle-molecule mRNA detection and counting in mammalian tissue
Lyubimova, A., Itzkovitz, S., Junker, J. P., Fan, Z. P., Wu, X., & van Oudenaarden, A. (2013) · Nature Protocols · doi:10.1038/nprot.2013.109 · Cited by 206 · see citing works
Early myeloid lineage choice is not initiated by random PU.1 to GATA1 protein ratios
Hoppe, P. S., Schwarzfischer, M., Loeffler, D., Kokkaliaris, K. D., Hilsenbeck, O., Moritz, N., Endele, M., Filipczyk, A. A., Gambardella, A. R., Ahmed, N., et al. (2016) · Nature · doi:10.1038/nature18320 · Cited by 202 · see citing works
Deciding fate in adverse times: Sporulation and competence in <i>Bacillus subtilis</i>
Schultz, D., Wolynes, P. G., Jacob, E. B., & Onuchic, J. N. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0912185106 · Cited by 201 · see citing works
Regulation of Noise in Gene Expression
Sánchez, Á., Choubey, S., & Kondev, J. (2013) · Annual Review of Biophysics · doi:10.1146/annurev-biophys-083012-130401 · Cited by 193 · see citing works
A stochastic model for gene induction
Ko, M. S. H. (1991) · Journal of Theoretical Biology · doi:10.1016/s0022-5193(05)80421-7 · Cited by 192 · see citing works
A single molecule view of gene expression
Larson, D. R., Singer, R. H., & Zenklusen, D. (2009) · Trends in Cell Biology · doi:10.1016/j.tcb.2009.08.008 · Cited by 177 · see citing works
Cellular Noise Regulons Underlie Fluctuations in Saccharomyces cerevisiae
Stewart-Ornstein, J., Weissman, J. S., & El-Samad, H. (2012) · Molecular Cell · doi:10.1016/j.molcel.2011.11.035 · Cited by 177 · 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
Onset of differentiation is post-transcriptionally controlled in adult neural stem cells
Baser, A., Skabkin, M. A., Kleber, S., Dang, Y., Balta, G. S. G., Kalamakis, G., Göpferich, M., Ibañez, D. C., Schefzik, R., Lopez, A. S., et al. (2019) · Nature · doi:10.1038/s41586-019-0888-x · Cited by 161 · see citing works
Noise–mean relationship in mutated promotersPreprint
Hornung, G., Bar‐Ziv, R., Rosin, D., Tokuriki, N., Tawfik, D. S., Oren, M., & Barkai, N. (2012) · Genome Research · doi:10.1101/gr.139378.112 · Cited by 156 · see citing works
Surviving apoptosis: life–death signaling in single cells
Flusberg, D. A., & Sorger, P. K. (2015) · Trends in Cell Biology · doi:10.1016/j.tcb.2015.03.003 · Cited by 149 · see citing works
A coordinated global control over cellular transcription
Zhurinsky, J., Leonhard, K., Watt, S., Marguerat, S., Bähler, J., & Nurse, P. (2010) · Current Biology · doi:10.1016/j.cub.2010.10.002 · Cited by 148 · see citing works
Multi-omics of single cells: strategies and applications
Bock, C., Farlik, M., & Sheffield, N. C. (2016) · Trends in Biotechnology · doi:10.1016/j.tibtech.2016.04.004 · Cited by 146 · see citing works
Selection on noise constrains variation in a eukaryotic promoter
Metzger, B. P. H., Yuan, D. C., Gruber, J. D., Duveau, F., & Wittkopp, P. J. (2015) · Nature · doi:10.1038/nature14244 · Cited by 138 · see citing works
Fractional killing arises from cell‐to‐cell variability in overcoming a caspase activity threshold
Roux, J., Hafner, M. A., Bandara, S., Sims, J. J., Hudson, H., Chai, D. H., & Sorger, P. K. (2015) · Molecular Systems Biology · doi:10.15252/msb.20145584 · Cited by 136 · see citing works
Bartman, C., Hamagami, N., Keller, C. A., Giardine, B. M., Hardison, R. C., Blobel, G. A., & Raj, A. (2018) · Molecular Cell · doi:10.1016/j.molcel.2018.11.004 · Cited by 134 · see citing works
Kempe, H., Schwabe, A., Crémazy, F. G. E., Verschure, P. J., & Bruggeman, F. J. (2014) · Molecular Biology of the Cell · doi:10.1091/mbc.e14-08-1296 · Cited by 127 · see citing works
THE ESTABLISHMENT OF LYSOGENICITY IN ESCHERICHIA COLI
Lieb, M. (1953) · Journal of Bacteriology · doi:10.1128/jb.65.6.642-651.1953 · Cited by 123 · see citing works
Orthogonal control of expression mean and variance by epigenetic features at different genomic loci
Dey, S. S., Foley, J. E., Limsirichai, P., Schaffer, D. V., & Arkin, A. P. (2015) · Molecular Systems Biology · doi:10.15252/msb.20145704 · Cited by 119 · see citing works
Epigenetic regulation and the variability of gene expression
Choi, J. K., & Kim, Y.-J. (2008) · Nature Genetics · doi:10.1038/ng.2007.58 · Cited by 108 · see citing works
Probing the effect of promoters on noise in gene expression using thousands of designed sequencesPreprint
Sharon, E., van Dijk, D., Kalma, Y., Keren, L., Manor, O., Yakhini, Z., & Segal, E. (2014) · Genome Research · doi:10.1101/gr.168773.113 · Cited by 107 · see citing works
Comprehensive analysis of promoter-proximal RNA polymerase II pausing across mammalian cell types
Day, D. S., Zhang, B., Stevens, S. M., Ferrari, F., Larschan, E., Park, P. J., & Pu, W. T. (2016) · Genome biology · doi:10.1186/s13059-016-0984-2 · Cited by 107 · see citing works
Single-cell nucleosome mapping reveals the molecular basis of gene expression heterogeneity
Small, E. C., Xi, L., Wang, J.-P., Widom, J., & Licht, J. D. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1400517111 · Cited by 105 · see citing works
Mapping Differentiation under Mixed Culture Conditions Reveals a Tunable Continuum of T Cell Fates
Antebi, Y. E., Reich-Zeliger, S., Hart, Y., Mayo, A., Eizenberg, I., Rimer, J., Putheti, P., Pe'er, D., & Friedman, N. (2013) · PLoS Biology · doi:10.1371/journal.pbio.1001616 · Cited by 100 · see citing works
Eling, N., Richard, A. C., Richardson, S., Marioni, J. C., & Vallejos, C. A. (2018) · Cell Systems · doi:10.1016/j.cels.2018.06.011 · Cited by 99 · see citing works
Self-citationPhenotypic Plasticity and Cell Fate Decisions in Cancer: Insights from Dynamical Systems Theory
Jia, D., Jolly, M. K., Kulkarni, P., & Levine, H. (2017) · Cancers · doi:10.3390/cancers9070070 · Cited by 94 · see citing works
Systematic Analysis of the Determinants of Gene Expression Noise in Embryonic Stem Cells
Faure, A. J., Schmiedel, J. M., & Lehner, B. (2017) · Cell Systems · doi:10.1016/j.cels.2017.10.003 · Cited by 91 · see citing works
Epigenetic and Transcriptional Variability Shape Phenotypic Plasticity
Ecker, S., Pancaldi, V., Valencia, A., Beck, S., & Paul, D. S. (2017) · BioEssays · doi:10.1002/bies.201700148 · Cited by 90 · see citing works
Feedback Control of Gene Expression Variability in the Caenorhabditis elegans Wnt Pathway
Ji, N., Middelkoop, T. C., Mentink, R. A., Betist, M. C., Tonegawa, S., Mooijman, D., Korswagen, H. C., & van Oudenaarden, A. (2013) · Cell · doi:10.1016/j.cell.2013.09.060 · Cited by 89 · see citing works
Cytoplasmic Amplification of Transcriptional Noise Generates Substantial Cell-to-Cell Variability
Hansen, M. M. K., Desai, R. V., Simpson, M. L., & Weinberger, L. S. (2018) · Cell Systems · doi:10.1016/j.cels.2018.08.002 · Cited by 87 · see citing works
A Post-Transcriptional Feedback Mechanism for Noise Suppression and Fate Stabilization
Hansen, M. M. K., Wen, W. Y., Ingerman, E., Razooky, B. S., Thompson, C. E., Dar, R. D., Chin, C. W. J., Simpson, M. L., & Weinberger, L. S. (2018) · Cell · doi:10.1016/j.cell.2018.04.005 · Cited by 86 · see citing works
Origins of extrinsic variability in eukaryotic gene expression
Ostroff, N. A., Volfson, D., Marciniak, J. Y., Blake, W. J., Tsimring, L. S., & Hasty, J. (2006) · Bulletin of the American Physical Society · Cited by 80 · see citing works
Kar, G., Kim, J. K., Kolodziejczyk, A. A., Natarajan, K. N., Triglia, E. T., Mifsud, B., Elderkin, S., Marioni, J. C., Pombo, A., & Teichmann, S. A. (2017) · Nature Communications · doi:10.1038/s41467-017-00052-2 · Cited by 75 · see citing works
Self-citationAkopyan, K., Cascales, H. S., Hukasova, E., Saurin, A. T., Müllers, E., Jaiswal, H., Hollman, D. A. A., Kops, G. J. P. L., Medema, R. H., & Lindqvist, A. (2014) · Molecular Cell · doi:10.1016/j.molcel.2014.01.031 · Cited by 74 · see citing works
An atlas of the aging lung mapped by single cell transcriptomics and deep tissue proteomicsPreprint
Angelidis, I., Simon, L. M., Fernandez, I. E., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/351353 · Cited by 71 · see citing works
Digital signaling decouples activation probability and population heterogeneity
Kellogg, R., Tian, C., Tomasz Lipniacki, B., Quake, S. R., & Tay, S. (2015) · eLife · doi:10.7554/elife.08931 · Cited by 67 · see citing works
Fitness effects of altering gene expression noise in Saccharomyces cerevisiae
Duveau, F., Hodgins-Davis, A., Metzger, B. P. H., Yang, B., Tryban, S., Walker, E. A., Lybrook, T., & Wittkopp, P. J. (2018) · eLife · doi:10.7554/elife.37272 · Cited by 60 · see citing works
Symmetry Breaking in the Mammalian Embryo
Zhang, H. T., & Hiiragi, T. (2018) · Annual Review of Cell and Developmental Biology · doi:10.1146/annurev-cellbio-100617-062616 · Cited by 59 · see citing works
South Africa: from species cradle to genomic applications
Hardy, B.-J., Séguin, B., Ramesar, R., SINGER, P. A., & Daar, A. S. (2008) · Nature Reviews Genetics · doi:10.1038/nrg2441 · Cited by 56 · see citing works
Stochastic Cytokine Expression Induces Mixed T Helper Cell States
Fang, M., Xie, H., Dougan, S. K., Ploegh, H. L., & van Oudenaarden, A. (2013) · PLoS Biology · doi:10.1371/journal.pbio.1001618 · Cited by 55 · see citing works
Noise in a phosphorelay drives stochastic entry into sporulation in Bacillus subtilis
Russell, J. R., Cabeen, M. T., Wiggins, P. A., et al. (2017) · The EMBO Journal · doi:10.15252/embj.201796988 · Cited by 52 · see citing works
Lu, Y. C., Biancotto, A., Cheung, F., Remmers, E. F., Shah, N., McCoy, J. P., & Tsang, J. S. (2016) · Immunity · doi:10.1016/j.immuni.2016.10.025 · Cited by 50 · see citing works
Promoter-mediated diversification of transcriptional bursting dynamics following gene duplication
Tunnacliffe, E. A. J., Corrigan, A. M., & Chubb, J. R. (2018) · Proceedings of the National Academy of Sciences of the United States of America · doi:10.1073/pnas.1800943115 · Cited by 49 · see citing works
Generation of Single-Cell Transcript Variability by Repression
Antolović, V., Miermont, A., Corrigan, A. M., & Chubb, J. R. (2017) · Current Biology · doi:10.1016/j.cub.2017.05.028 · Cited by 45 · see citing works
Morgan, M. D., & Marioni, J. C. (2018) · Genome biology · doi:10.1186/s13059-018-1461-x · Cited by 41 · see citing works
Self-citationEmpirical mean-noise fitness landscapes reveal the fitness impact of gene expression noise
Schmiedel, J. M., Carey, L. B., & Lehner, B. (2019) · Nature Communications · doi:10.1038/s41467-019-11116-w · Cited by 38 · see citing works
Uniform gene expression in embryos is achieved by temporal averaging of transcription noisePreprint
Stapel, L. C., Zechner, C., & Vastenhouw, N. L. (2017) · Genes & Development · doi:10.1101/gad.302935.117 · Cited by 32 · see citing works
Iwamoto, K., Shindo, Y., & Takahashi, K. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005222 · Cited by 28 · see citing works
Fuhrmann, F., Lischke, T., Groß, F., et al. (2016) · eLife · doi:10.7554/elife.20616 · Cited by 25 · see citing works
Single-cell systems biology: Probing the basic unit of information flow
Patange, S., Girvan, M., & Larson, D. R. (2017) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2017.11.011 · Cited by 21 · see citing works
López‐Maury, L., Marguerat, S., & Bähler, J. (2008) · Nature Reviews Genetics · doi:10.1038/nrg2500 · Cited by 13 · see citing works
Schrom, E. C., & Graham, A. L. (2017) · Current Opinion in Genetics & Development · doi:10.1016/j.gde.2017.08.008 · Cited by 7 · 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
Empirical noise-mean fitness landscapes and the evolution of gene expressionPreprint
Schmiedel, J. M., Carey, L. B., & Lehner, B. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/436170 · Cited by 1 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- [76]Mulqueen RM Scalable and efficient single-cell DNA methylation sequencing by combinatorial indexing bioRxiv 2017
Cited by
Sorted by citations
López-Otín, C., & Kroemer, G. (2021) · Cell · doi:10.1016/j.cell.2020.11.034 · Cited by 499 · see citing works
How to build the virtual cell with artificial intelligence: Priorities and opportunities
Bunne, C., Roohani, Y., Rosen, Y., Gupta, A., Zhang, X., Roed, M., Alexandrov, T., AlQuraishi, M., Brennan, P., Burkhardt, D. B., et al. (2024) · Cell · doi:10.1016/j.cell.2024.11.015 · Cited by 393 · see citing works
“They generally rely on explicitly defined mathematical or computational approaches, such as differential equations, 7–9 stochastic simulations, 10,11 or agent-based models.”
Fast and precise single-cell data analysis using a hierarchical autoencoder
Tran, D., Nguyen, H., Tran, B., La Vecchia, C., Luu, H. N., & Nguyen, T. (2021) · Nature Communications · doi:10.1038/s41467-021-21312-2 · Cited by 145 · see citing works
Single-nucleus RNA-seq2 reveals functional crosstalk between liver zonation and ploidy
Richter, M. L., Deligiannis, I. K., Yin, K., Danese, A., Lleshi, E., Coupland, P., Vallejos, C. A., Matchett, K. P., Henderson, N. C., Colome-Tatche, M., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-24543-5 · Cited by 75 · see citing works
Highly accurate isoform identification for the human transcriptome
Sommer, M. J., Cha, S., Varabyou, A., Rincon, N., Park, S., Minkin, I., Pertea, M., Steinegger, M., & Salzberg, S. L. (2022) · doi:10.1101/2022.06.08.495354 · Cited by 40 · see citing works
PSAURON: a tool for assessing protein annotation across a broad range of species
Sommer, M. J., Zimin, A. V., & Salzberg, S. L. (2025) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqae189 · Cited by 29 · see citing works
El Meouche, I., Jain, P., Jolly, M. K., & Capp, J.-P. (2024) · Translational Oncology · doi:10.1016/j.tranon.2024.102069 · Cited by 22 · see citing works
- Authors
- Eling, N., Morgan, M. D., & Marioni, J. C.
- Affiliations
- European Bioinformatics Institute (GB)
- Wellcome Sanger Institute (GB)
- Corresponding author
- Eling, Nils
- Morgan, Michael D.
- Marioni, John C.
- Venue
- Nature Reviews Genetics
- Published
- 2019-05-21
- Volume
- 20
- Issue
- 9
- Pages
- 536-548
- ISSN
- 1471-0056 (ISSN-L)
- 1471-0064
- PMC
- PMC7611518
- OpenAlex
- W2945570991
- Cited by
- 247
- References
- 158
- Citations by year
- 2026: 22 · 2025: 33 · 2024: 51 · 2023: 39 · 2022: 37 · 2021: 37 · 2020: 24 · 2019: 4
- FWCI
- 2.24
- Type
- journal article
- Language
- en
- Update notices
- correction (2019-06-03)
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Nature Reviews Genetics (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Wellcome Trust (105045, 105045/Z/14/Z)
- Cancer Research UK (22231, 17197)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › stochastic dynamics and bifurcation
- Keywords
- biological noise, cell-to-cell variability, stochastic gene expression, single-cell technologies
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
