Discrete Distributional Differential Expression (D 3 E) - A Tool for Gene Expression Analysis of Single-cell RNA-seq Data
Open accessPreprint — published as 10.1186/s12859-016-0944-6 · Paper page
Abstract
The advent of high throughput RNA-seq at the single-cell level has opened up new opportunities to elucidate the heterogeneity of gene expression. One of the most widespread applications of RNA-seq is to identify genes which are differentially expressed between two experimental conditions. Here, we present a discrete, distributional method for differential gene expression (D 3 E), a novel algorithm specifically designed for single-cell RNA-seq data. We use synthetic data to evaluate D 3 E, demonstrating that it can detect changes in expression, even when the mean level remains unchanged. Since D 3 E is based on an analytically tractable stochastic model, it provides additional biological insights by quantifying biologically meaningful properties, such as the average burst size and frequency. We use D 3 E to investigate experimental data, and with the help of the underlying model, we directly test hypotheses about the driving mechanism behind changes in gene expression.
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.
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
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482 · see citing works
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
Love, M. I., Huber, W., & Anders, S. (2014) · Genome Biology · doi:10.1186/s13059-014-0550-8 · Cited by 95,106 · see citing works
Robinson, M. D., McCarthy, D. J., & Smyth, G. K. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btp616 · Cited by 41,364 · see citing works
limma powers differential expression analyses for RNA-sequencing and microarray studies
Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W., & Smyth, G. K. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv007 · Cited by 40,458 · see citing works
Global quantification of mammalian gene expression control
Schwanhäusser, B., Busse, D., Li, N., Dittmar, G., Schuchhardt, J., Wolf, J., Chen, W., & Selbach, M. (2011) · Nature · doi:10.1038/nature10098 · Cited by 6,566 · see citing works
Gillespie, D. T. (1976) · Journal of Computational Physics · doi:10.1016/0021-9991(76)90041-3 · Cited by 6,029 · see citing works
Controlling the false discovery rate in behavior genetics research
Benjamini, Y., Drai, D., Elmer, G., Kafkafi, N., & Golani, I. (2001) · Behavioural Brain Research · doi:10.1016/s0166-4328(01)00297-2 · Cited by 4,146 · see citing works
mRNA-Seq whole-transcriptome analysis of a single cell
Tang, F., Barbacioru, C., Wang, Y., Nordman, E., Lee, C., Xu, N., Wang, X., Bodeau, J., Tuch, B. B., Siddiqui, A., et al. (2009) · Nature Methods · doi:10.1038/nmeth.1315 · Cited by 3,969 · see citing works
Asymptotic Theory of Certain "Goodness of Fit" Criteria Based on Stochastic Processes
Anderson, T. W., & Darling, D. A. (1952) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177729437 · Cited by 3,542 · see citing works
Differential analysis of gene regulation at transcript resolution with RNA-seq
Trapnell, C., Hendrickson, D. G., Sauvageau, M., Goff, L., Rinn, J. L., & Pachter, L. (2013) · Nature Biotechnology · doi:10.1038/nbt.2450 · Cited by 3,492 · see citing works
Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq
Zeisel, A., Muñoz-Manchado, A. B., Codeluppi, S., Lönnerberg, P., La Manno, G., Juréus, A., Marques, S., Munguba, H., He, L., Betsholtz, C., et al. (2015) · Science · doi:10.1126/science.aaa1934 · Cited by 3,124 · 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
Nonparametric Statistical Inference
(2012) · Springer eBooks · doi:10.1007/springerreference_205502 · Cited by 2,398 · see citing works
Normalization of RNA-seq data using factor analysis of control genes or samples
Risso, D., Ngai, J., Speed, T. P., & Dudoit, S. (2014) · Nature Biotechnology · doi:10.1038/nbt.2931 · Cited by 2,169 · see citing works
RNA sequencing: advances, challenges and opportunities
Ozsolak, F., & Milos, P. M. (2011) · Nature Reviews Genetics · doi:10.1038/nrg2934 · Cited by 2,135 · 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
Neal, R. M. (2003) · The Annals of Statistics · doi:10.1214/aos/1056562461 · Cited by 1,380 · see citing works
Bayesian approach to single-cell differential expression analysis
Kharchenko, P. V., Silberstein, L., & Scadden, D. T. (2014) · Nature Methods · doi:10.1038/nmeth.2967 · Cited by 1,348 · see citing works
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 · 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
Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq
Islam, S., Kjällquist, U., Moliner, A., Zajac, P., Fan, J.-B., Lönnerberg, P., & Linnarsson, S. (2011) · Genome Research · doi:10.1101/gr.110882.110 · Cited by 949 · see citing works
ENZYME INDUCTION AS AN ALL-OR-NONE PHENOMENON
Novick, A., & Weiner, M. (1957) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.43.7.553 · Cited by 891 · 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
Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data
Rapaport, F., Khanin, R., Liang, Y., Pirun, M., Krek, A., Zumbo, P., Mason, C. E., Socci, N. D., & Betel, D. (2013) · Genome Biology · doi:10.1186/gb-2013-14-9-r95 · Cited by 733 · 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
Love, M. I., Huber, W. K., & Anders, S. (2014) · Cited by 718 · 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
Variability 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
Weinberger, L. S., Burnett, J., Toettcher, J. E., Arkin, A. P., & Schaffer, D. V. (2005) · Cell · doi:10.1016/j.cell.2005.06.006 · Cited by 616 · see citing works
On the Distribution of the Two-Sample Cramer-von Mises Criterion
Anderson, T. W. (1962) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177704477 · Cited by 606 · see citing works
Sharova, L. V., Sharov, A. A., Nedorezov, T., Piao, Y., Shaik, N., & Ko, M. S. H. (2009) · DNA Research · doi:10.1093/dnares/dsn030 · Cited by 591 · see citing works
ZIFA: Dimensionality reduction for zero-inflated single-cell gene expression analysis
Pierson, E., & Yau, C. (2015) · Genome Biology · doi:10.1186/s13059-015-0805-z · Cited by 577 · see citing works
Nonparametric Statistical Inference
Gibbons, J. D., & Chakraborti, S. (2010) · Chapman & Hall/CRC eBooks · doi:10.1201/9781439896129 · Cited by 574 · 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
Functional Roles of Pulsing in Genetic Circuits
Levine, J. H., Lin, Y., & Elowitz, M. B. (2013) · Science · doi:10.1126/science.1239999 · Cited by 369 · see citing works
Cell fate inclination within 2-cell and 4-cell mouse embryos revealed by single-cell RNA sequencingPreprint
Biase, F. H., Cao, X., & Zhong, S. (2014) · Genome Research · doi:10.1101/gr.177725.114 · Cited by 313 · see citing works
A model for the statistical fluctuations of protein numbers in a microbial population
Berg, O. G. (1978) · Journal of Theoretical Biology · doi:10.1016/0022-5193(78)90326-0 · Cited by 288 · see citing works
DNA Microarrays: a Powerful Genomic Tool for Biomedical and Clinical Research
Treviño, V., Falciani, F., & Barrera-Saldaña, H. A. (2007) · Molecular Medicine · doi:10.2119/2006-00107.trevino · Cited by 246 · see citing works
Single-cell gene expression analysis reveals genetic associations masked in whole-tissue experiments
Wills, Q. F., Livak, K. J., Tipping, A. J., Enver, T., Goldson, A. J., Sexton, D. W., & Holmes, C. C. (2013) · Nature Biotechnology · doi:10.1038/nbt.2642 · Cited by 244 · 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
Single-allele analysis of transcription kinetics in living mammalian cells
Yunger, S., Rosenfeld, L., Garini, Y., & Shav-Tal, Y. (2010) · Nature Methods · doi:10.1038/nmeth.1482 · Cited by 170 · see citing works
Quantification of mRNA in single cells and modelling of RT-qPCR induced noise
Bengtsson, M., Hemberg, M., Rorsman, P., & Ståhlberg, A. (2008) · BMC Molecular Biology · doi:10.1186/1471-2199-9-63 · Cited by 123 · see citing works
Self-citationGray, J. M., Harmin, D. A., Boswell, S. A., Cloonan, N., Mullen, T. E., Ling, J. J., Miller, N., Kuersten, S., Ma, Y. C., McCarroll, S. A., et al. (2014) · PLoS ONE · doi:10.1371/journal.pone.0089673 · Cited by 95 · see citing works
Coulon, A., Gandrillon, O., & Beslon, G. (2010) · BMC Systems Biology · doi:10.1186/1752-0509-4-2 · Cited by 78 · see citing works
Jääger, K., Islam, S., Zając, P., Linnarsson, S., & Neuman, T. (2012) · PLoS ONE · doi:10.1371/journal.pone.0038833 · Cited by 64 · see citing works
Digital nature of the immediate-early transcriptional response
Stevense, M., Muramoto, T., Müller, I. I., & Chubb, J. R. (2010) · Development · doi:10.1242/dev.043836 · Cited by 43 · see citing works
Modeling RNA degradation for RNA-Seq with applications
Wan, L., Yan, X., Chen, T., & Sun, F. (2012) · Biostatistics · doi:10.1093/biostatistics/kxs001 · Cited by 25 · see citing works
On a discrete compound distribution
Holla, M. S., & Bhattacharya, S. (1965) · Annals of the Institute of Statistical Mathematics · doi:10.1007/bf02868181 · Cited by 23 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Scenic: single-cell regulatory network inference and clustering.
Aibar, S., González-Blas, C. B., Moerman, T., Huynh-Thu, V. A., Imrichova, H., Hulselmans, G., Rambow, F., Marine, J.-C., Geurts, P., Aerts, J., et al. (2017) · Nature Methods · doi:10.1038/nmeth.4463 · Cited by 6,520 · see citing works
Single-Cell RNA-Seq Technologies and Related Computational Data Analysis
Chen, G., Ning, B., & Shi, T. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00317 · Cited by 913 · see citing works
Bias, robustness and scalability in single-cell differential expression analysis
Soneson, C., & Robinson, M. D. (2018) · Nature Methods · doi:10.1038/nmeth.4612 · Cited by 770 · see citing works
Wang, T., Li, B., Nelson, C. E., & Nabavi, S. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-2599-6 · Cited by 286 · see citing works
A statistical approach for identifying differential distributions in single-cell RNA-seq experiments
Korthauer, K. D., Chu, L.-F., Newton, M. A., Li, Y., Thomson, J., Stewart, R., & Kendziorski, C. (2016) · Genome Biology · doi:10.1186/s13059-016-1077-y · Cited by 273 · see citing works
DEsingle for detecting three types of differential expression in single-cell RNA-seq data
Miao, Z., Deng, K., Wang, X., & Zhang, X. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty332 · Cited by 272 · see citing works
Chen, S., & Mar, J. C. (2018) · BMC Bioinformatics · doi:10.1186/s12859-018-2217-z · Cited by 235 · see citing works
Gorin, G., Fang, M., Chari, T., & Pachter, L. (2022) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1010492 · Cited by 186 · see citing works
Single-Cell Transcriptomics: Current Methods and Challenges in Data Acquisition and Analysis
Adil, A., Kumar, V., Jan, A. T., & Asger, M. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.591122 · Cited by 133 · see citing works
Gene expression distribution deconvolution in single-cell RNA sequencing
Wang, J., Huang, M. C., Torre, E. A., Dueck, H., Shaffer, S. M., Murray, J. I., Raj, A., Li, M., & Zhang, N. R. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1721085115 · Cited by 121 · see citing works
A DNA repair pathway can regulate transcriptional noise to promote cell fate transitions
Desai, R. V., Chen, X., Martin, B., Chaturvedi, S., Hwang, D.-W., Li, W., Chen, Y., Ding, S., Thomson, M. W., Singer, R. H., et al. (2021) · Science · doi:10.1126/science.abc6506 · Cited by 116 · see citing works
Single-Cell RNA-Sequencing: Assessment of Differential Expression Analysis Methods
Dal Molin, A., Baruzzo, G., & Di Camillo, B. (2017) · Frontiers in Genetics · doi:10.3389/fgene.2017.00062 · Cited by 107 · see citing works
scRNASeqDB: A Database for RNA-Seq Based Gene Expression Profiles in Human Single Cells
Cao, Y., Zhu, J., Han, G., Jia, P., & Zhao, Z. (2017) · Genes · doi:10.3390/genes8120368 · Cited by 104 · see citing works
Wang, M., Song, W., Chen, M., Wang, Q., Zhou, X., Xu, P., Krek, A., Yoon, Y., Ho, L., Orr, M. E., et al. (2022) · Molecular Neurodegeneration · doi:10.1186/s13024-022-00517-z · Cited by 100 · see citing works
High Throughput Single Cell RNA Sequencing, Bioinformatics Analysis and Applications
Huang, X., Liu, S., Wu, L. L., Jiang, M., & Hou, Y. (2018) · Advances in experimental medicine and biology · doi:10.1007/978-981-13-0502-3_4 · Cited by 85 · see citing works
What are the applications of single-cell RNA sequencing in cancer research: a systematic review
Li, L., Xiong, F., Wang, Y., Zhang, S., Gong, Z., Li, X., He, Y., Shi, L., Wang, F., Liao, Q., et al. (2021) · Journal of Experimental & Clinical Cancer Research · doi:10.1186/s13046-021-01955-1 · Cited by 86 · see citing works
Single-cell omics: experimental workflow, data analyses and applications
Sun, F., Li, H., Sun, D., Fu, S., Gu, L., Shao, X., Wang, Q., Dong, X., Duan, B., Xing, F., et al. (2024) · Science China Life Sciences · doi:10.1007/s11427-023-2561-0 · Cited by 81 · see citing works
Reproducibility of Methods to Detect Differentially Expressed Genes from Single-Cell RNA Sequencing
Mou, T., Deng, W., Gu, F., Yudi Pawitan, Y., & Vu, T. N. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2019.01331 · Cited by 73 · see citing works
Quantitative single-cell transcriptomics
Ziegenhain, C., Vieth, B., Parekh, S., Hellmann, I., & Enard, W. (2018) · Briefings in Functional Genomics · doi:10.1093/bfgp/ely009 · Cited by 58 · see citing works
Wang, T., & Nabavi, S. (2018) · Methods · doi:10.1016/j.ymeth.2018.04.017 · Cited by 56 · see citing works
Loss of Kat2a enhances transcriptional noise and depletes acute myeloid leukemia stem-like cells
Domingues, A. F., Kulkarni, R., Giotopoulos, G., Gupta, S., Vinnenberg, L., Arede, L., Foerner, E., Khalili, M., Adao, R. R., Johns, A., et al. (2020) · eLife · doi:10.7554/elife.51754 · Cited by 52 · see citing works
geneBasis: an iterative approach for unsupervised selection of targeted gene panels from scRNA-seq
Missarova, A. M., Jain, J., Butler, A., Ghazanfar, S., Stuart, T., Brusko, M., Wasserfall, C. H., Nick, H. S., Brusko, T. M., Atkinson, M. A., et al. (2021) · Genome biology · doi:10.1186/s13059-021-02548-z · Cited by 49 · see citing works
Das, S., Rai, A. K., & Rai, S. N. (2022) · Entropy · doi:10.3390/e24070995 · Cited by 45 · see citing works
Data-based RNA-seq simulations by binomial thinning
Gerard, D. (2020) · BMC Bioinformatics · doi:10.1186/s12859-020-3450-9 · Cited by 43 · see citing works
The Human Cell Atlas: Technical approaches and challenges
Hon, C.-C., Shin, J. W., Carninci, P., & Stubbington, M. J. T. (2017) · Briefings in Functional Genomics · doi:10.1093/bfgp/elx029 · Cited by 40 · see citing works
Ngara, M., Palmkvist, M., Sagasser, S., Hjelmqvist, D. T., Björklund, Å. K., Wahlgren, M., Ankarklev, J., & Sandberg, R. (2018) · Experimental Cell Research · doi:10.1016/j.yexcr.2018.08.003 · Cited by 40 · see citing works
Studying stochastic systems biology of the cell with single-cell genomics data
Gorin, G., Vastola, J. J., & Pachter, L. S. (2023) · Cell Systems · doi:10.1016/j.cels.2023.08.004 · Cited by 38 · see citing works
Two-phase differential expression analysis for single cell RNA-seq
Wu, Z. J., Zhang, Y., Stitzel, M. L., & Wu, H. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty329 · Cited by 36 · see citing works
Das, S., Rai, A., Merchant, M. L., Cave, M. C., & Rai, S. N. (2021) · Genes · doi:10.3390/genes12121947 · Cited by 35 · see citing works
Furchtgott, L., Melton, S., Menon, V., & Ramanathan, S. (2017) · eLife · doi:10.7554/elife.20488 · Cited by 29 · see citing works
A mechanistic model for the negative binomial distribution of single-cell mRNA countsPreprint
Amrhein, L., Harsha, K., & Fuchs, C. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/657619 · Cited by 29 · see citing works
Differential expression analyses for single‐cell RNA‐Seq: old questions on new data
Miao, Z., & Zhang, X. (2016) · Quantitative Biology · doi:10.1007/s40484-016-0089-7 · Cited by 28 · see citing works
Single cell transcriptome sequencing: A new approach for the study of mammalian sex determination
Stévant, I., & Nef, S. (2018) · Molecular and Cellular Endocrinology · doi:10.1016/j.mce.2018.01.013 · Cited by 28 · see citing works
Single-cell transcriptomics: background, technologies, applications, and challenges
Duhan, L., Kumari, D., Naime, M., Parmar, V. S., Chhillar, A. K., Dangi, M., & Pasrija, R. (2024) · Molecular Biology Reports · doi:10.1007/s11033-024-09553-y · Cited by 28 · see citing works
Length biases in single-cell RNA sequencing of pre-mRNA
Gorin, G., & Pachter, L. S. (2022) · Biophysical Reports · doi:10.1016/j.bpr.2022.100097 · Cited by 28 · see citing works
Thurman, A. L., Ratcliff, J. A., Chimenti, M. S., & Pezzulo, A. A. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab337 · Cited by 27 · see citing works
An efficient ensemble method for missing value imputation in microarray gene expression data
Zhu, X., Wang, J., Sun, B., Ren, C., Yang, T., & Ding, J. (2021) · BMC Bioinformatics · doi:10.1186/s12859-021-04109-4 · Cited by 24 · see citing works
Missarova, A., Dann, E., Dann, E., Satija, R., & Marioni, J. C. (2024) · Genome biology · doi:10.1186/s13059-024-03334-3 · Cited by 32 · see citing works
Applications of Single-Cell Sequencing for Multiomics
Xu, Y., & Zhou, X. (2018) · Methods in molecular biology · doi:10.1007/978-1-4939-7717-8_19 · Cited by 22 · see citing works
scRecover: Discriminating true and false zeros in single-cell RNA-seq data for imputationPreprint
Miao, Z., Li, J., & Zhang, X. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/665323 · Cited by 21 · see citing works
Venice: A New Algorithm for Finding Marker Genes in Single-Cell Transcriptomic DataPreprint
Vuong, H., Truong, T., Phan, T. Đ., & Pham, S. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.11.16.384479 · Cited by 20 · see citing works
Navigating single-cell RNA-sequencing: protocols, tools, databases, and applications
Arya, A., Tripathi, P., Dubey, N., Aier, I., & Varadwaj, P. K. (2025) · Genomics & Informatics · doi:10.1186/s44342-025-00044-5 · Cited by 19 · see citing works
Decoding brain memory formation by single-cell RNA sequencing
Sardoo, A. M., Zhang, S., Ferraro, T. N., Keck, T. M., & Chen, Y. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac412 · Cited by 18 · see citing works
Guo, X., Ning, J., Chen, Y., Liu, G., Zhao, L., Fan, Y., & Sun, S. (2023) · Briefings in Functional Genomics · doi:10.1093/bfgp/elad011 · Cited by 18 · see citing works
SCeQTL: an R package for identifying eQTL from single-cell parallel sequencing data
Hu, Y., Xi, X., Yang, Q., & Zhang, X. (2020) · BMC Bioinformatics · doi:10.1186/s12859-020-3534-6 · Cited by 18 · see citing works
Gorin, G., Chari, T., Carilli, M., Vastola, J. J., & Pachter, L. S. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.06.11.495771 · Cited by 16 · see citing works
Das, S., & Rai, S. N. (2021) · Genomics · doi:10.1016/j.ygeno.2021.02.014 · Cited by 15 · see citing works
Contrastive latent variable modeling with application to case-control sequencing experiments
Jones, A., Townes, F. W., Li, D., & Engelhardt, B. E. (2022) · The Annals of Applied Statistics · doi:10.1214/21-aoas1534 · Cited by 14 · see citing works
Sensitive cluster-free differential expression testing.Preprint
Missarova, A. M., Rosen, L. U., Dann, E., Satija, R., & Marioni, J. C. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.03.08.531744 · Cited by 15 · see citing works
ZIAQ: a quantile regression method for differential expression analysis of single-cell RNA-seq data
Zhang, W., Wei, Y., Zhang, D., & Xu, E. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa098 · Cited by 13 · see citing works
Li, H., Zhu, B., Xu, Z., Adams, T. S., Kaminski, N., & Zhao, H. (2021) · BMC Bioinformatics · doi:10.1186/s12859-021-04412-0 · Cited by 14 · see citing works
BISC: accurate inference of transcriptional bursting kinetics from single-cell transcriptomic data
Luo, X., Qin, F., Xiao, F., & Cai, G. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac464 · Cited by 12 · see citing works
Expression quantitative trait locus studies in the era of single-cell omics
Luo, J., Wu, X., Cheng, Y., Chen, G., Wang, J., & Song, X. (2023) · Frontiers in Genetics · doi:10.3389/fgene.2023.1182579 · Cited by 11 · see citing works
iDESC: identifying differential expression in single-cell RNA sequencing data with multiple subjects
Liu, Y., Zhao, J., Adams, T. S., Wang, N., Schupp, J. C., Wu, W., McDonough, J. E., Chupp, G. L., Kaminski, N., Wang, Z., et al. (2023) · BMC Bioinformatics · doi:10.1186/s12859-023-05432-8 · Cited by 11 · see citing works
BSDE: barycenter single-cell differential expression for case–control studies
Zhang, M., & Guo, F. R. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac171 · Cited by 10 · see citing works
Statistical Methods for RNA Sequencing Data Analysis
Li, D. (2019) · Codon Publications eBooks · doi:10.15586/computationalbiology.2019.ch6 · Cited by 10 · see citing works
Zhang, S., Xie, L., Cui, Y., Carone, B. R., & Chen, Y. (2022) · Biomolecules · doi:10.3390/biom12081130 · Cited by 9 · see citing works
Defining cell identity beyond the premise of differential gene expression
Kim, H. J., Tam, P., & Yang, P. (2021) · Cell Regeneration · doi:10.1186/s13619-021-00083-7 · Cited by 8 · see citing works
scRecover : Discriminating True and False Zeros in Single‐Cell RNA ‐Seq Data for Imputation
Miao, Z., Lin, X., Li, J., Ho, J., Meng, Q., & Zhang, X. (2025) · Statistics in Medicine · doi:10.1002/sim.10334 · Cited by 7 · see citing works
Kervadec, A., Kezos, J. N., Ni, H., Yu, M. S., Marchant, J., Spiering, S., Kannan, S., Kwon, C., Andersen, P., Bodmer, R., et al. (2023) · Disease Models & Mechanisms · doi:10.1242/dmm.049962 · Cited by 6 · see citing works
scHiCDiff: detecting differential chromatin interactions in single-cell Hi-C data
Liu, H., & Ma, W. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad625 · Cited by 7 · see citing works
Zhu, B., Li, H., Zhang, L., Chandra, S. S., & Zhao, H. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac166 · Cited by 6 · see citing works
Differential gene expression analysis in single-cell RNA sequencing data
Wang, T., & Nabavi, S. (2017) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2017.8217650 · Cited by 5 · see citing works
A compositional model to assess expression changes from single-cell RNA-seq data
Ma, X., Korthauer, K. D., Kendziorski, C. M., & Newton, M. A. (2021) · The Annals of Applied Statistics · doi:10.1214/20-aoas1423 · Cited by 4 · see citing works
Mathematical modeling and analysis of mitochondrial retrograde signaling dynamics
Chiu, S.-T., Tseng, W.-W., & Wei, A. (2022) · iScience · doi:10.1016/j.isci.2022.105502 · Cited by 5 · see citing works
Gauthier, M., Agniel, D., Thiébaut, R., & Hejblum, B. P. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.05.21.445165 · Cited by 5 · see citing works
Two‐sample test withg‐modeling and its applications
Zhai, J., & Jiang, H. (2022) · Statistics in Medicine · doi:10.1002/sim.9603 · Cited by 3 · see citing works
Bulk and Single-Cell RNA-Seq Analyses for Studies of Spermatogonia
Singh, A., & Hermann, B. P. (2023) · Methods in molecular biology · doi:10.1007/978-1-0716-3139-3_4 · Cited by 4 · see citing works
Cui, L., Wang, B., Ren, C., Wang, A., An, H., & Liang, W. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.648898 · Cited by 3 · see citing works
An evaluation of statistical differential analysis methods in single-cell RNA-seq dataPreprint
Li, D., Zand, M. S., Dye, T., Goniewicz, M. L., Rahman, I., & Xie, Z. (2023) · Research Square · doi:10.21203/rs.3.rs-2670717/v1 · Cited by 3 · see citing works
Xue, J., Zhou, X., Yang, J., & Niu, A. (2024) · PLoS ONE · doi:10.1371/journal.pone.0299358 · Cited by 2 · see citing works
Alaqeeli, O., Xing, L., & Zhang, X. (2021) · Microbiology Research · doi:10.3390/microbiolres12020022 · Cited by 2 · see citing works
Comparison of marker selection methods for high throughput scRNA-seq dataPreprint
Gilbert, A. C., & Vargo, A. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/679761 · Cited by 2 · see citing works
Statistical Methods for Single‐Cell RNA‐Sequencing
Andrews, T. S., Kiselev, V. Y., & Hemberg, M. (2019) · Wiley eBooks · doi:10.1002/9781119487845.ch26 · Cited by 2 · see citing works
Self-citationM-Band Wavelet-Based Imputation of scRNA-seq Matrix and Multi-view Clustering of CellPreprint
Liu, Z., Liu, T., Sun, W., Zhao, Y., & Wang, X. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.12.05.519090 · Cited by 2 · see citing works
Li, D., Liu, P., Rahman, I., Zand, M. S., Pryhuber, G., Dye, T., Goniewicz, M. L., Gurkar, A. U., Königshoff, M., Eickelberg, O., et al. (2026) · Cell Reports Methods · doi:10.1016/j.crmeth.2025.101264 · Cited by 1 · see citing works
Approaches to Marker Gene Identification from Single-Cell RNA-Sequencing Data
Li, Y., Ma, W., & Qin, Z. S. (2022) · Springer handbooks of computational statistics · doi:10.1007/978-3-662-65902-1_4 · Cited by 1 · see citing works
RNA-Seq Analysis: Methods, Applications and Challenges
vari, A., Geraci, F., Saha, I., & Bianchini, M. (2020) · Frontiers research topics · doi:10.3389/978-2-88963-705-8 · Cited by 1 · see citing works
Li, H., Xu, Z., Adams, T. S., Kaminski, N., & Zhao, H. (2020) · Research Square · doi:10.21203/rs.3.rs-116107/v1 · Cited by 1 · see citing works
Zhang, B., & Zhang, H. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.12.21.521359 · Cited by 1 · see citing works
Petrany, A., Chen, R., Zhang, S., & Chen, Y. (2024) · Genome Research · doi:10.1101/gr.278843.123 · Cited by 1 · see citing works
Improving leukemic CD34 + /CD38 − blasts characterization with single-cell transcriptome sequencingPreprint
Sartori, A., Cheng, P. F., Falconnet, E., Ribaux, P., Aubry-Lachainaye, J.-P., Levesque, M. P., Antonarakis, S. E., Matthes, T., & Borel, C. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/141754
Single-Cell Dissection of Human Pancreatic Islet Dysfunction in Diabetes
Stitzel, M. L. (2017)
Decoding the heterogeneity of skin in homeostasis and regeneration at single-cell resolution
Joost, S. (2018) · Figshare
Mathematical Modeling and Analysis of Mitochondrial Retrograde Signaling DynamicsPreprint
Chiu, S.-T., Tseng, W.-W., & Wei, A. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.03.27.437239
Schefzik, R. (2021) · Az Eszterházy Károly Tanárképző Főiskola tudományos közleményei. Tanulmányok a matematikai tudományok köréből/Az Eszterházy Károly Főiskola tudományos közleményei. Tanulmányok a matematikai tudományok köréből/Annales mathematicae et informaticae · doi:10.33039/ami.2021.03.003
Mathematical Modeling and Analysis of Mitochondrial Retrograde Signaling DynamicsPreprint
Chiu, S.-T., Tseng, W.-W., & Wei, A. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3962445
Zhao, H. C., Guo, Y., Ma, Y., Chen, Y., Sun, H., Sun, D., Wu, N., & Jin, Y. (2021) · Annals of Translational Medicine · doi:10.21037/atm-21-6401
Handling the Cellular Complex Systems in Alzheimer’s Disease Through a Graph Mining Approach
Vrahatis, A. G., Vlamos, P., Gonidi, M., & Avramouli, A. (2021) · Advances in experimental medicine and biology · doi:10.1007/978-3-030-78775-2_16
Computational and Statistical Methods for Single-Cell RNA Sequencing Data
Wang, Z., & Yan, X. (2022) · Springer handbooks of computational statistics · doi:10.1007/978-3-662-65902-1_1
Posterior Inference of Differential Expression (DE) State and Evolutionary DistancePreprint
Gu, X. (2023) · Research Square · doi:10.21203/rs.3.rs-2694316/v1
A Model for Detecting Type 2 Diabetes Using Mixed Single-Cell RNA Sequencing with Optimized Data
Padmaja, K., & Mukhopadhyay, D. (2023) · SN Computer Science · doi:10.1007/s42979-023-02215-z
Li, H., Zhu, B., Xu, Z., Adams, T. S., Kaminski, N., & Zhao, H. (2021) · Figshare · doi:10.6084/m9.figshare.16880171
Li, H., Zhu, B., Xu, Z., Adams, T. S., Kaminski, N., & Zhao, H. (2021) · Figshare · doi:10.6084/m9.figshare.16880165
Zhang, B., Wang, J., Wang, W., & Zhang, H. (2025) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-025-00778-4 · Cited by 1 · see citing works
Missarova, A. M., Jain, J., Butler, A., Ghazanfar, S., Stuart, T., Brusko, M., Wasserfall, C. H., Nick, H. S., Brusko, T. M., Atkinson, M., et al. (2021) · Figshare · doi:10.6084/m9.figshare.17133256
Li, H., Zhu, B., Xu, Z., Adams, T. S., Kaminski, N., & Zhao, H. (2021) · Figshare · doi:10.6084/m9.figshare.16880162
Missarova, A., Dann, E., Rosen, L. U., Satija, R., & Marioni, J. (2024) · Figshare · doi:10.6084/m9.figshare.26750681
- Authors
- Delmans, M., & Hemberg, M.
- Affiliations
- University of Cambridge (GB)
- Wellcome Sanger Institute (GB)
- Corresponding author
- Delmans, Mihails
- Hemberg, Martin
- Venue
- BMC Bioinformatics
- Publisher
- openRxiv
- Published
- 2015-06-10
- Volume
- 17
- Issue
- 1
- Pages
- 110-110
- ISSN
- 1471-2105 (ISSN-L)
- PMC
- PMC4772470
- OpenAlex
- W2950583356
- Cited by
- 111
- References
- 48
- Citations by year
- 2026: 3 · 2025: 3 · 2024: 9 · 2023: 15 · 2022: 17 · 2021: 25 · 2020: 10 · 2019: 7 · 2018: 12 · 2017: 8 · 2016: 2
- FWCI
- 5.03
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- BMC Bioinformatics (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Wellcome Trust
- University of Cambridge
- Biotechnology and Biological Sciences Research Council (1497818)
- 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
- differential gene expression, single-cell RNA sequencing, stochastic gene expression models, gene expression heterogeneity, burst size, burst frequency, synthetic data evaluation
- MeSH terms
- Algorithms, Humans, RNA, Statistical Distributions, Sequence Analysis, RNA, Gene Expression Profiling, Single-Cell Analysis, High-Throughput Nucleotide Sequencing
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
