单细胞数据分析||Statistical analysis fo
参考
Andrews, T. S. and Hemberg, M. (2018). Identifying cell populations with scrnaseq.
Molecular aspects of medicine, 59:114–122.
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).
Accounting for technical noise in single-cell rna-seq experiments. Nature methods, 10(11):1093.
Butler, A., Hoffman, P., Smibert, P., Papalexi, E., and Satija, R. (2018).
Integrating single-cell transcriptomic data across different conditions, technologies, and species.
Nature biotechnology, 36(5):411.
Cannoodt, R., Saelens, W., and Saeys, Y. (2016).
Computational methods for trajectory inference from single-cell transcriptomics.
European journal of immunology, 46(11):2496–2506.
Duò, A., Robinson, M. D., and Soneson, C. (2018).
A systematic performance evaluation of clustering methods for single-cell rna-seq data.
F1000Research, 7.
Freytag, S., Tian, L., Lönnstedt, I., Ng, M., and Bahlo, M. (2018).
Comparison of clustering tools in r for medium-sized 10x genomics single-cell rna-sequencing data.
F1000Research, 7.
Grün, D., Lyubimova, A., Kester, L., Wiebrands, K., Basak, O., Sasaki, N., Clevers, H., and van Oudenaarden, A. (2015).
Single-cell messenger rna sequencing reveals rare intestinal cell types.
Nature, 525(7568):251.
Guo, M., Wang, H., Potter, S. S., Whitsett, J. A., and Xu, Y. (2015).
Sincera: a pipeline for single-cell rna-seq profiling analysis.
PLoS computational biology, 11(11):e1004575.
Juliá, M., Telenti, A., and Rausell, A. (2015).
Sincell: an r/bioconductor package for statistical assessment of cell-state hierarchies from single-cell rna-seq.
Bioinformatics, 31(20):3380–3382.
Kim, J. K., Kolodziejczyk, A. A., Ilicic, T., Teichmann, S. A., and Marioni, J. C. (2015).
Characterizing noise structure in single-cell rna-seq distinguishes genuine from technical stochastic allelic expression.
Nature communications, 6:8687.
Kiselev, V. Y., Kirschner, K., Schaub, M. T., Andrews, T., Yiu, A., Chandra, T., Natarajan, K. N., Reik, W., Barahona, M., Green, A. R., and Hemberg, M. (2017).
Sc3: consensus clustering of single-cell rna-seq data.
Nature Methods, 14:483 EP –.
Lin, P., Troup, M., and Ho, J. W. (2017).
Cidr: Ultrafast and accurate clustering through imputation for single-cell rna-seq data.
Genome biology, 18(1):59.
Lun, A. T., McCarthy, D. J., and Marioni, J. C. (2016).
A step-by-step workflow for low-level analysis of single-cell rna-seq data with bioconductor.
F1000Research, 5.
Pierson, E. and Yau, C. (2015).
Zifa: Dimensionality reduction for zero-inflated single-cell gene expression analysis.
Genome biology, 16(1):241.
Poirion, O. B., Zhu, X., Ching, T., and Garmire, L. (2016).
Single-cell transcriptomics bioinformatics and computational challenges.
Frontiers in genetics, 7:163.
Prabhakaran, S., Azizi, E., Carr, A., and Pe’er, D. (2016).
Dirichlet process mixture model for correcting technical variation in single-cell gene expression data. In International Conference on Machine Learning, pages 1070–1079.
Risso, D., Perraudeau, F., Gribkova, S., Dudoit, S., and Vert, J.-P. (2017).
Zinb-wave: A general and flexible method for signal extraction from single-cell rna-seq data.
bioRxiv.
Saelens, W., Cannoodt, R., Todorov, H., and Saeys, Y. (2018).
A comparison of single-cell trajectory inference methods: towards more accurate and robust tools.
bioRxiv, page 276907.
Satija, R., Farrell, J. A., Gennert, D., Schier, A. F., and Regev, A. (2015).
Spatial reconstruction of single-cell gene expression data.
Nature biotechnology, 33(5):495.
Soneson, C. and Robinson, M. (2018).
Bias, robustness and scability in single-cell differential expression analysis.
Nature Methods, 15:255–261.
Vallejos, C., Marioni, J., and Richardson, S. (2015).
Basics: Bayesian analysis of single-cell sequencing data.
PLOS COMPUTATIONAL BIOLOGY, 11.
Wang, B., Zhu, J., Pierson, E., Ramazzotti, D., and Batzoglou, S. (2017).
Visualization and analysis of single-cell rna-seq data by kernel-based similarity learning.
Nature methods, 14(4):414.
Wolf, F. A., Angerer, P., and Theis, F. J. (2018).
Scanpy: large-scale single-cell gene expression data analysis.
Genome biology, 19(1):15.
Xu, C. and Su, Z. (2015).
Identification of cell types from single-cell transcriptomes using a novel clustering method.
Bioinformatics, 31(12):1974–1980.
Yang, Y., Huh, R., Culpepper, H. W., Lin, Y., Love, M. I., and Li, Y. (2017).
SAFE-clustering: Single-cell aggregated (from ensemble) clustering for single-cell RNA-seq data.
bioRxiv.
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).
Cell types in the mouse cortex and hippocampus revealed by single-cell rna-seq.
Science, 347(6226):1138–1142.
Žurauskiene˙, J. and Yau, C. (2016).
pcareduce: hierarchical clustering of single cell transcriptional profiles.
BMC Bioinformatics, 17.
