5 Session info
## R version 3.4.4 (2018-03-15)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 16.04.4 LTS
##
## Matrix products: default
## BLAS: /usr/lib/openblas-base/libblas.so.3
## LAPACK: /usr/lib/libopenblasp-r0.2.18.so
##
## locale:
## [1] LC_CTYPE=fr_FR.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=fr_FR.UTF-8 LC_COLLATE=fr_FR.UTF-8
## [5] LC_MONETARY=fr_FR.UTF-8 LC_MESSAGES=fr_FR.UTF-8
## [7] LC_PAPER=fr_FR.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=fr_FR.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] methods stats graphics grDevices utils datasets base
##
## other attached packages:
## [1] ggplot2_3.3.3 Seurat_3.1.1
##
## loaded via a namespace (and not attached):
## [1] tsne_0.1-3 nlme_3.1-142 bitops_1.0-6
## [4] RcppAnnoy_0.0.14 RColorBrewer_1.1-2 httr_1.4.2
## [7] sctransform_0.2.0 tools_3.4.4 R6_2.5.0
## [10] irlba_2.3.3 KernSmooth_2.23-15 uwot_0.1.4
## [13] DBI_1.1.1 lazyeval_0.2.2 colorspace_2.0-0
## [16] withr_2.4.0 npsurv_0.4-0 gridExtra_2.3
## [19] tidyselect_1.1.0 compiler_3.4.4 plotly_4.9.1
## [22] labeling_0.4.2 bookdown_0.17 caTools_1.17.1.2
## [25] scales_1.1.1 lmtest_0.9-37 ggridges_0.5.1
## [28] pbapply_1.4-3 rappdirs_0.3.1 stringr_1.4.0
## [31] digest_0.6.27 rmarkdown_1.17 R.utils_2.9.0
## [34] pkgconfig_2.0.3 htmltools_0.5.1 bibtex_0.4.2
## [37] htmlwidgets_1.5.3 rlang_0.4.10 rstudioapi_0.13
## [40] farver_2.0.3 generics_0.1.0 zoo_1.8-6
## [43] jsonlite_1.7.2 ica_1.0-2 gtools_3.8.1
## [46] dplyr_1.0.3 R.oo_1.23.0 magrittr_2.0.1
## [49] Matrix_1.2-14 Rcpp_1.0.6 munsell_0.5.0
## [52] ape_5.3 reticulate_1.18 lifecycle_0.2.0
## [55] R.methodsS3_1.7.1 stringi_1.5.3 yaml_2.2.1
## [58] gbRd_0.4-11 MASS_7.3-51.4 gplots_3.0.1.1
## [61] Rtsne_0.15 plyr_1.8.6 grid_3.4.4
## [64] parallel_3.4.4 gdata_2.18.0 listenv_0.7.0
## [67] ggrepel_0.9.0 crayon_1.3.4 lattice_0.20-35
## [70] cowplot_0.9.4 splines_3.4.4 SDMTools_1.1-221.1
## [73] knitr_1.30 pillar_1.4.7 igraph_1.2.6
## [76] reshape2_1.4.4 future.apply_1.3.0 codetools_0.2-15
## [79] leiden_0.3.1 glue_1.4.2 evaluate_0.14
## [82] lsei_1.2-0 metap_1.1 RcppParallel_5.0.2
## [85] data.table_1.13.6 png_0.1-7 vctrs_0.3.6
## [88] Rdpack_0.11-0 tidyr_1.1.2 gtable_0.3.0
## [91] RANN_2.6.1 purrr_0.3.4 future_1.15.0
## [94] xfun_0.19 rsvd_1.0.2 RSpectra_0.16-0
## [97] viridisLite_0.3.0 survival_3.1-7 tibble_3.0.5
## [100] cluster_2.1.0 globals_0.12.4 fitdistrplus_1.0-14
## [103] ellipsis_0.3.1 ROCR_1.0-7
Amezquita, Robert A., Aaron T. L. Lun, Etienne Becht, Vince J. Carey, Lindsay N. Carpp, Ludwig Geistlinger, Federico Marini, et al. 2019. “Orchestrating Single-Cell Analysis with Bioconductor.” Nature Methods 17 (2): 137–45. https://doi.org/10.1038/s41592-019-0654-x.
Cao, Junyue, Diana R. O’Day, Hannah A. Pliner, Paul D. Kingsley, Mei Deng, Riza M. Daza, Michael A. Zager, et al. 2020. “A Human Cell Atlas of Fetal Gene Expression.” Science 370 (6518): eaba7721. https://doi.org/10.1126/science.aba7721.
Delile, Julien, Teresa Rayon, Manuela Melchionda, Amelia Edwards, James Briscoe, and Andreas Sagner. 2019. “Single Cell Transcriptomics Reveals Spatial and Temporal Dynamics of Gene Expression in the Developing Mouse Spinal Cord.” Development 146 (12): dev173807. https://doi.org/10.1242/dev.173807.
Karaiskos, Nikos, Philipp Wahle, Jonathan Alles, Anastasiya Boltengagen, Salah Ayoub, Claudia Kipar, Christine Kocks, Nikolaus Rajewsky, and Robert P. Zinzen. 2017. “TheDrosophilaembryo at Single-Cell Transcriptome Resolution.” Science 358 (6360): 194–99. https://doi.org/10.1126/science.aan3235.
Litviňuková, Monika, Carlos Talavera-López, Henrike Maatz, Daniel Reichart, Catherine L. Worth, Eric L. Lindberg, Masatoshi Kanda, et al. 2020. “Cells of the Adult Human Heart.” Nature 588 (7838): 466–72. https://doi.org/10.1038/s41586-020-2797-4.
Pijuan-Sala, Blanca, Jonathan A. Griffiths, Carolina Guibentif, Tom W. Hiscock, Wajid Jawaid, Fernando J. Calero-Nieto, Carla Mulas, et al. 2019. “A Single-Cell Molecular Map of Mouse Gastrulation and Early Organogenesis.” Nature 566 (7745): 490–95. https://doi.org/10.1038/s41586-019-0933-9.
Saelens, Wouter, Robrecht Cannoodt, Helena Todorov, and Yvan Saeys. 2019. “A Comparison of Single-Cell Trajectory Inference Methods.” Nature Biotechnology 37 (5): 547–54. https://doi.org/10.1038/s41587-019-0071-9.
Satija, Rahul, Jeffrey A Farrell, David Gennert, Alexander F Schier, and Aviv Regev. 2015. “Spatial Reconstruction of Single-Cell Gene Expression Data.” Nature Biotechnology 33 (5): 495–502. https://doi.org/10.1038/nbt.3192.
Stegle, Oliver, Sarah A. Teichmann, and John C. Marioni. 2015. “Computational and Analytical Challenges in Single-Cell Transcriptomics.” Nature Reviews Genetics 16 (3): 133–45. https://doi.org/10.1038/nrg3833.
Villani, Alexandra-Chloé, Rahul Satija, Gary Reynolds, Siranush Sarkizova, Karthik Shekhar, James Fletcher, Morgane Griesbeck, et al. 2017. “Single-Cell RNA-Seq Reveals New Types of Human Blood Dendritic Cells, Monocytes, and Progenitors.” Science 356 (6335): eaah4573. https://doi.org/10.1126/science.aah4573.
Wolf, F. Alexander, Fiona K. Hamey, Mireya Plass, Jordi Solana, Joakim S. Dahlin, Berthold Göttgens, Nikolaus Rajewsky, Lukas Simon, and Fabian J. Theis. 2019. “PAGA: Graph Abstraction Reconciles Clustering with Trajectory Inference Through a Topology Preserving Map of Single Cells.” Genome Biology 20 (1). https://doi.org/10.1186/s13059-019-1663-x.