Bayesian inference of gene expression states from single-cell RNA-seq data thumbnail

Bayesian inference of gene expression states from single-cell RNA-seq data

  • 1.

    Picelli, S. et al. Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat. Methods 10, 1096–1098 (2013).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  • 2.

    Hashimshony, T., Wagner, F., Sher, N. & Yanai, I. CEL-seq: single-cell RNA-seq by multiplexed linear amplification. Cell Rep. 2, 666–673 (2012).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  • 3.

    Macosko, E. Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 4.

    Klein, A. M. et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161, 1187–1201 (2015).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 5.

    Buenrostro, J. D. et al. Single-cell chromatin accessibility reveals principles of regulatory variation. Nature 523, 486–490 (2015).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 6.

    Cusanovich, D. A. et al. Multiplex single cell profiling of chromatin accessibility by combinatorial cellular indexing. Science 348, 910–914 (2015).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 7.

    Rotem, A. et al. Single-cell ChIP–seq reveals cell subpopulations defined by chromatin state. Nat. Biotechnol. 33, 1165–1172 (2015).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 8.

    Smallwood, S. A. et al. Single-cell genome-wide bisulfite sequencing for assessing epigenetic heterogeneity. Nat. Methods 11, 817–820 (2014).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 9.

    Nagano, T. et al. Single-cell Hi-C reveals cell-to-cell variability in chromosome structure. Nature 502, 59–64 (2013).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  • 10.

    McKenna, A. et al. Whole-organism lineage tracing by combinatorial and cumulative genome editing. Science 353, aaf7907 (2016).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar
     

  • 11.

    Kalhor, R. et al. Developmental barcoding of whole mouse via homing CRISPR. Science 361, eaat9804 (2018).

  • 12.

    Frieda, K. L. et al. Synthetic recording and in situ readout of lineage information in single cells. Nature 541, 107–111 (2017).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  • 13.

    Frei, A. P. et al. Highly multiplexed simultaneous detection of RNAs and proteins in single cells. Nat. Methods 13, 269–275 (2016).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 14.

    Raj, B. et al. Simultaneous single-cell profiling of lineages and cell types in the vertebrate brain. Nat. Biotechnol. 36, 442–450 (2018).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 15.

    Spanjaard, B. et al. Simultaneous lineage tracing and cell-type identification using CRISPR–Cas9-induced genetic scars. Nat. Biotechnol. 36, 469–473 (2018).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 16.

    Wagner, D. E. et al. Single-cell mapping of gene expression landscapes and lineage in the zebrafish embryo. Science 360, 981–987 (2018).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 17.

    Angermueller, C. et al. Parallel single-cell sequencing links transcriptional and epigenetic heterogeneity. Nat. Methods 13, 229–232 (2016).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 18.

    Clark, S. J. et al. scNMT-seq enables joint profiling of chromatin accessibility DNA methylation and transcription in single cells. Nat. Commun. 9, 781 (2018).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar
     

  • 19.

    Adamson, B. et al. A multiplexed single-cell CRISPR screening platform enables systematic dissection of the unfolded protein response. Cell 167, 1867–1882 (2016).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 20.

    Dixit, A. et al. Perturb-seq: dissecting molecular circuits with scalable single-cell RNA profiling of pooled genetic screens. Cell 167, 1853–1866 (2016).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 21.

    Jaitin, D. A. et al. Dissecting immune circuits by linking CRISPR-pooled screens with single-cell RNA-seq. Cell 167, 1883–1896 (2016).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 22.

    Datlinger, P. et al. Pooled CRISPR screening with single-cell transcriptome readout. Nat. Methods 14, 297–301 (2017).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 23.

    Regev, A. et al. The human cell atlas. eLife 6, e27041 (2017).

  • 24.

    Rajewsky, N. et al. LifeTime and improving European healthcare through cell-based interceptive medicine. Nature 587, 377–386 (2020).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  • 25.

    Raj, A., van den Bogaard, P., Rifkin, S. A., van Oudenaarden, A. & Tyagi, S. Imaging individual mRNA molecules using multiple singly labeled probes. Nat. Methods 5, 877–879 (2008).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 26.

    Van Der Maaten, L. & Hinton, G. Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008).


    Google Scholar
     

  • 27.

    McInnes, L. & Healy, J. UMAP: uniform manifold approximation and projection for dimension reduction. Preprint at arXiv http://arxiv.org/abs/1802.03426 (2018).

  • 28.

    Eraslan, G., Simon, L. M., Mircea, M., Mueller, N. S. & Theis, F. J. Single-cell RNA-seq denoising using a deep count autoencoder. Nat. Commun. 10, 390 (2019).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 29.

    Lun, A. T., Bach, K. & Marioni, J. C. Pooling across cells to normalize single-cell RNA sequencing data with many zero counts. Genome Biol. 17, 75 (2016).

    PubMed 
    Article 
    CAS 

    Google Scholar
     

  • 30.

    van Dijk, D. et al. Recovering gene interactions from single-cell data using data diffusion. Cell 174, 716–729 (2018).


    Google Scholar
     

  • 31.

    Huang, M. et al. SAVER: gene expression recovery for single-cell RNA sequencing. Nat. Methods 15, 539–542 (2018).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 32.

    Li, W. V. & Li, J. J. An accurate and robust imputation method scImpute for single-cell RNA-seq data. Nat. Commun. 9, 997 (2018).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar
     

  • 33.

    Hafemeister, C. & Satija, R. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biol. 20, 296 (2019).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 34.

    Lopez, R., Regier, J., Cole, M. B., Jordan, M. I. & Yosef, N. Deep generative modeling for single-cell transcriptomics. Nat. Methods 15, 1053–1058 (2018).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 35.

    Grün, D., Kester, L. & Van Oudenaarden, A. Validation of noise models for single-cell transcriptomics. Nat. Methods 11, 637–640 (2014).

    PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar
     

  • 36.

    Brennecke, P. et al. Accounting for technical noise in single-cell RNA-seq experiments. Nat. Methods 10, 1093–1095 (2013).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  • 37.

    Zeisel, A. et al. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 347, 1138–1142 (2015).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  • 38.

    Lloyd, S. P. Least squares quantization in PCM. IEEE Trans. Inf. Theory 28, 129–137 (1982).

    Article 

    Google Scholar
     

  • 39.

    Ward, J. H. Hierarchical grouping to optimize an objective function. J. Am. Stat. Assoc. 58, 236–244 (1963).

    Article 

    Google Scholar
     

  • 40.

    Blondel, V. D., Guillaume, J.-L., Lambiotte, R. & Lefebvre, E. Fast unfolding of communities in large networks. J. Stat. Mech. Theory Exp. 2008, P10008 (2008).

    Article 

    Google Scholar
     

  • 41.

    Thattai, M. Universal Poisson statistics of mRNAs with complex decay pathways. Biophys. J. 110, 301–305 (2016).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  • 42.

    La Manno, G. et al. RNA velocity of single cells. Nature 560, 494–498 (2018).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar
     

  • 43.

    Bergen, V., Lange, M., Peidli, S., Wolf, F. A. & Theis, F. J. Generalizing RNA velocity to transient cell states through dynamical modeling. Nat. Biotechnol. 38, 1408–1414 (2020).

    PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar
     

  • 44.

    Padovan-Merhar, O. et al. Single mammalian cells compensate for differences in cellular volume and DNA copy number through independent global transcriptional mechanisms. Mol. Cell 58, 339–352 (2015).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 45.

    Stegle, O., Teichmann, S. A. & Marioni, J. C. Computational and analytical challenges in single-cell transcriptomics. Nat. Rev. Genet. 16, 133–145 (2015).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  • 46.

    Hoyle, D. C., Rattray, M., Jupp, R. & Brass, A. Making sense of microarray data distributions. Bioinformatics 18, 576–584 (2002).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  • 47.

    Beal, J. Biochemical complexity drives log-normal variation in genetic expression. Eng. Biol. 1, 55–60 (2017).

    Article 

    Google Scholar
     

  • 48.

    Love, M. I., Anders, S., Kim, V. & Huber, W. RNA-seq workflow: gene-level exploratory analysis and differential expression. F1000Res 4, 1070 (2015).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 49.

    Bray, N. L., Pimentel, H., Melsted, P. & Pachter, L. Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol. 34, 525–527 (2016).

    CAS 
    Article 

    Google Scholar
     

  • 50.

    Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    CAS 
    Article 

    Google Scholar
     

  • 51.

    Cell Ranger DNA. https://support.10xgenomics.com/single-cell-dna/software/pipelines/latest/what-is-cell-ranger-dna

  • 52.

    Islam, S. et al. Quantitative single-cell RNA-seq with unique molecular identifiers. Nat. Methods 11, 163–166 (2014).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  • 53.

    AlJanahi, A. A., Danielsen, M. & Dunbar, C. E. An introduction to the analysis of single-cell RNA-sequencing data. Mol. Ther. Methods Clin. Dev. 10, 189–196 (2018).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 54.

    10X Genomics. What fraction of mRNA transcripts are captured per cell? https://kb.10xgenomics.com/hc/en-us/articles/360001539051-what-fraction-of-mrna-transcripts-are-captured-per-cell- (2018).

  • 55.

    Jaynes, E. T. Probability Theory: The Logic of Science (Cambridge Univ. Press, 2003).

  • 56.

    Svensson, V. Droplet scRNA-seq is not zero-inflated. Nat. Biotechnol. 38, 147–150 (2020).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  • 57.

    Baron, M. et al. A single-cell transcriptomic map of the human and mouse pancreas reveals inter- and intra-cell population structure. Cell Syst. 3, 346–360 (2016).


    Google Scholar
     

  • 58.

    Chen, R., Wu, X., Jiang, L. & Zhang, Y. Single-cell RNA-seq reveals hypothalamic cell diversity. Cell Rep. 18, 3227–3241 (2017).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 59.

    La Manno, G. et al. Molecular diversity of midbrain development in mouse, human, and stem cells. Cell 167, 566–580 (2016).

    Article 
    CAS 

    Google Scholar
     

  • Read More

    Leave a Reply

    Your email address will not be published. Required fields are marked *