Chapter 19 Bibliography

Anders, S., and W. Huber. 2010. “Differential expression analysis for sequence count data.” Genome Biol. 11 (10):R106.

Berger, R. L., and J. C. Hsu. 1996. “Bioequivalence Trials, Intersection-Union Tests and Equivalence Confidence Sets.” Statist. Sci. 11 (4). The Institute of Mathematical Statistics:283–319.

Brennecke, P., S. Anders, J. K. Kim, A. A. Kołodziejczyk, X. Zhang, V. Proserpio, B. Baying, et al. 2013. “Accounting for technical noise in single-cell RNA-seq experiments.” Nat. Methods 10 (11):1093–5.

Butler, A., P. Hoffman, P. Smibert, E. Papalexi, and R. Satija. 2018. “Integrating single-cell transcriptomic data across different conditions, technologies, and species.” Nat. Biotechnol. 36 (5):411–20.

Haghverdi, L., A. T. L. Lun, M. D. Morgan, and J. C. Marioni. 2018. “Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors.” Nat. Biotechnol. 36 (5):421–27.

Ilicic, T., J. K. Kim, A. A. Kołodziejczyk, F. O. Bagger, D. J. McCarthy, J. C. Marioni, and S. A. Teichmann. 2016. “Classification of low quality cells from single-cell RNA-seq data.” Genome Biol. 17 (1):29.

Islam, S., U. Kjallquist, A. Moliner, P. Zajac, J. B. Fan, P. Lonnerberg, and S. Linnarsson. 2011. “Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq.” Genome Res. 21 (7):1160–7.

Islam, S., A. Zeisel, S. Joost, G. La Manno, P. Zajac, M. Kasper, P. Lonnerberg, and S. Linnarsson. 2014. “Quantitative single-cell RNA-seq with unique molecular identifiers.” Nat. Methods 11 (2):163–66.

Ji, Z., and H. Ji. 2016. “TSCAN: Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis.” Nucleic Acids Res. 44 (13):e117.

Law, C. W., Y. Chen, W. Shi, and G. K. Smyth. 2014. “voom: Precision weights unlock linear model analysis tools for RNA-seq read counts.” Genome Biol. 15 (2):R29.

Lawlor, N., J. George, M. Bolisetty, R. Kursawe, L. Sun, V. Sivakamasundari, I. Kycia, P. Robson, and M. L. Stitzel. 2017. “Single-cell transcriptomes identify human islet cell signatures and reveal cell-type-specific expression changes in type 2 diabetes.” Genome Res. 27 (2):208–22.

Leek, J. T., W. E. Johnson, H. S. Parker, A. E. Jaffe, and J. D. Storey. 2012. “The sva package for removing batch effects and other unwanted variation in high-throughput experiments.” Bioinformatics 28 (6):882–83.

Lin, Y., S. Ghazanfar, K. Y. X. Wang, J. A. Gagnon-Bartsch, K. K. Lo, X. Su, Z. G. Han, et al. 2019. “scMerge leverages factor analysis, stable expression, and pseudoreplication to merge multiple single-cell RNA-seq datasets.” Proc. Natl. Acad. Sci. U.S.A. 116 (20):9775–84.

Love, M. I., W. Huber, and S. Anders. 2014. “Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2.” Genome Biol. 15 (12):550.

Lun, A. 2018. “Overcoming Systematic Errors Caused by Log-Transformation of Normalized Single-Cell Rna Sequencing Data.” bioRxiv.

Lun, A., S. Riesenfeld, T. Andrews, T. P. Dao, T. Gomes, participants in the 1st Human Cell Atlas Jamboree, and J. Marioni. 2019. “EmptyDrops: distinguishing cells from empty droplets in droplet-based single-cell RNA sequencing data.” Genome Biol. 20 (1):63.

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

Lun, A. T. L., F. J. Calero-Nieto, L. Haim-Vilmovsky, B. Gottgens, and J. C. Marioni. 2017. “Assessing the reliability of spike-in normalization for analyses of single-cell RNA sequencing data.” Genome Res. 27 (11):1795–1806.

Lun, A. T. L., and J. C. Marioni. 2017. “Overcoming confounding plate effects in differential expression analyses of single-cell RNA-seq data.” Biostatistics 18 (3):451–64.

McCarthy, D. J., K. R. Campbell, A. T. Lun, and Q. F. Wills. 2017. “Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R.” Bioinformatics 33 (8):1179–86.

McCarthy, D. J., and G. K. Smyth. 2009. “Testing significance relative to a fold-change threshold is a TREAT.” Bioinformatics 25 (6):765–71.

Phipson, B., and G. K. Smyth. 2010. “Permutation P-values should never be zero: calculating exact P-values when permutations are randomly drawn.” Stat. Appl. Genet. Mol. Biol. 9:Article 39.

Richard, A. C., A. T. L. Lun, W. W. Y. Lau, B. Gottgens, J. C. Marioni, and G. M. Griffiths. 2018. “T cell cytolytic capacity is independent of initial stimulation strength.” Nat. Immunol. 19 (8):849–58.

Ritchie, M. E., B. Phipson, D. Wu, Y. Hu, C. W. Law, W. Shi, and G. K. Smyth. 2015. “limma powers differential expression analyses for RNA-sequencing and microarray studies.” Nucleic Acids Res. 43 (7):e47.

Robinson, M. D., and A. Oshlack. 2010. “A scaling normalization method for differential expression analysis of RNA-seq data.” Genome Biol. 11 (3):R25.

Simes, R. J. 1986. “An Improved Bonferroni Procedure for Multiple Tests of Significance.” Biometrika 73 (3):751–54.

Van der Maaten, L., and G. Hinton. 2008. “Visualizing Data Using T-SNE.” J. Mach. Learn. Res. 9 (2579-2605):85.

Wolf, F. Alexander, Fiona Hamey, Mireya Plass, Jordi Solana, Joakim S. Dahlin, Berthold Gottgens, Nikolaus Rajewsky, Lukas Simon, and Fabian J. Theis. 2017. “Graph Abstraction Reconciles Clustering with Trajectory Inference Through a Topology Preserving Map of Single Cells.” bioRxiv. Cold Spring Harbor Laboratory.

Xu, C., and Z. Su. 2015. “Identification of cell types from single-cell transcriptomes using a novel clustering method.” Bioinformatics 31 (12):1974–80.

Zeisel, A., A. B. Munoz-Manchado, S. Codeluppi, P. Lonnerberg, G. La Manno, A. Jureus, S. Marques, et al. 2015. “Brain structure. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq.” Science 347 (6226):1138–42.