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Single cell resolution landscape of equine peripheral blood mononuclear cells reveals diverse cell types including T-bet+ B cells.

This repository contains the data analysis scripts and code for our paper:

Roosheel S. Patel*, Joy E. Tomlinson*, Thomas J. Divers, Gerlinde R. Van de Walle, Brad R. Rosenberg, (2020). Single cell resolution landscape of equine peripheral blood mononuclear cells reveals diverse cell types including T-bet+ B cells. BMC Biology

*indicates equal contribution

Our pre-print is online here:

Roosheel S. Patel*, Joy E. Tomlinson*, Thomas J. Divers, Gerlinde R. Van de Walle, Brad R. Rosenberg, (2020). Single cell resolution landscape of equine peripheral blood mononuclear cells reveals diverse cell types including T-bet+ B cells biorxiv, Accessed 31 Dec 2020. Online at https://doi.org/10.1101/2020.05.05.077362

Contents

The analysis directory contains:

  • 📁 paper: R Markdown document which includes code to reproduce the figures generated by our analysis.
  • 📁 data: Supplementary data files and metadata used in this analysis

Additional files required for analysis

For surface annotation of differentially expressed genes, surface protein annotations were pulled from the Cell Surface Protein Atlas website.

Bausch-Fluck D, Hofmann A, Bock T, Frei AP, Cerciello F, et al. (2015) A Mass Spectrometric-Derived Cell Surface Protein Atlas. PLoS One https://doi.org/10.1371/journal.pone.0121314

Licenses

Code : MIT License, Copyright (c) 2020 Roosheel Patel

The organization of this repository was adapted from the rrtools research compendium structure

Marwick, B. (2017). Computational reproducibility in archaeological research: Basic principles and a case study of their implementation.Journal of Archaeological Method and Theory, 24(2), 424-450.https://doi.org/10.1007/s10816-015-9272-9

Session Information

Session information (computer environment and R/package versions) used to generate the analyses and figures in the manuscript:

R version 3.6.2 (2019-12-12) Platform: x86_64-pc-linux-gnu (64-bit) Running under: CentOS Linux 7 (Core) Matrix products: default BLAS/LAPACK: /hpc/packages/minerva-centos7/intel/parallel_studio_xe_2019/compilers_and_libraries_2019.0.117/linux/mkl/lib/intel64_lin/libmkl_gf_lp64.so Random number generation: RNG: Mersenne-Twister Normal: Inversion Sample: Rounding locale: [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
other attached packages: [1] readr_1.4.0 modelr_0.1.8 tidyr_1.1.2 reshape2_1.4.4 here_1.0.0 circlize_0.4.11 ComplexHeatmap_2.2.0 [8] GEOquery_2.54.1 factoextra_1.0.7 cluster_2.1.0 openxlsx_4.2.3 legocolors_0.2.0 ggforce_0.3.2 ggtree_2.0.4
[15] viridis_0.5.1 viridisLite_0.3.0 BiocManager_1.30.10 jntools_0.1.0 mgsub_1.7.2 ape_5.4-1 patchwork_1.1.0
[22] dplyr_1.0.2 edgeR_3.28.1 limma_3.42.2 scales_1.1.1 lemon_0.4.5 ggpubr_0.4.0 readxl_1.3.1
[29] ggrepel_0.8.2 RColorBrewer_1.1-2 ggnetwork_0.5.8 sctransform_0.2.0 uwot_0.1.9 rlang_0.4.9 rrtools_0.1.0
[36] future_1.20.1 clustree_0.4.3 ggraph_2.0.4 ggplot2_3.3.2 gdata_2.18.0 cowplot_1.1.0 gtools_3.8.2
[43] tibble_3.0.4 AnnotationDbi_1.48.0 IRanges_2.20.2 S4Vectors_0.24.4 Biobase_2.46.0 BiocGenerics_0.32.0 Matrix_1.2-18
[50] renv_0.12.3 Seurat_3.1.0
loaded via a namespace (and not attached): [1] reticulate_1.18 tidyselect_1.1.0 RSQLite_2.2.1 htmlwidgets_1.5.2 Rtsne_0.15 devtools_2.3.2
[7] munsell_0.5.0 codetools_0.2-18 ica_1.0-2 miniUI_0.1.1.1 withr_2.3.0 colorspace_2.0-0
[13] knitr_1.30 rstudioapi_0.13 ROCR_1.0-11 ggsignif_0.6.0 tensor_1.5 listenv_0.8.0
[19] labeling_0.4.2 git2r_0.27.1 polyclip_1.10-0 bit64_4.0.5 farver_2.0.3 rprojroot_2.0.2
[25] treeio_1.10.0 parallelly_1.21.0 vctrs_0.3.5 generics_0.1.0 xfun_0.19 R6_2.5.0
[31] clue_0.3-58 graphlayouts_0.7.1 rsvd_1.0.3 locfit_1.5-9.4 spatstat.utils_1.17-0 assertthat_0.2.1
[37] promises_1.1.1 gtable_0.3.0 globals_0.14.0 processx_3.4.5 goftest_1.2-2 tidygraph_1.2.0
[43] clisymbols_1.2.0 GlobalOptions_0.1.2 splines_3.6.1 rstatix_0.6.0 lazyeval_0.2.2 broom_0.7.2
[49] yaml_2.2.1 abind_1.4-5 backports_1.2.1 httpuv_1.5.4 tools_3.6.1 usethis_1.6.3
[55] bookdown_0.21 ellipsis_0.3.1 sessioninfo_1.1.1 ggridges_0.5.2 Rcpp_1.0.5 plyr_1.8.6
[61] purrr_0.3.4 ps_1.5.0 prettyunits_1.1.1 rpart_4.1-15 deldir_0.2-3 GetoptLong_1.0.4
[67] pbapply_1.4-3 zoo_1.8-8 haven_2.3.1 fs_1.5.0 magrittr_2.0.1 data.table_1.13.4
[73] lmtest_0.9-38 RANN_2.6.1 fitdistrplus_1.1-3 matrixStats_0.57.0 pkgload_1.1.0 hms_0.5.3
[79] mime_0.9 evaluate_0.14 xtable_1.8-4 rio_0.5.16 shape_1.4.5 gridExtra_2.3
[85] testthat_3.0.0 compiler_3.6.1 KernSmooth_2.23-18 crayon_1.3.4 htmltools_0.5.0 mgcv_1.8-33
[91] later_1.1.0.1 DBI_1.1.0 tweenr_1.0.1 MASS_7.3-53 car_3.0-10 cli_2.2.0
[97] igraph_1.2.6 forcats_0.5.0 pkgconfig_2.0.3 rvcheck_0.1.8 foreign_0.8-71 plotly_4.9.2.1
[103] xml2_1.3.2 stringr_1.4.0 callr_3.5.1 digest_0.6.27 RcppAnnoy_0.0.17 spatstat.data_1.5-2
[109] rmarkdown_2.5 cellranger_1.1.0 leiden_0.3.6 tidytree_0.3.3 curl_4.3 shiny_1.5.0
[115] rjson_0.2.20 lifecycle_0.2.0 nlme_3.1-150 jsonlite_1.7.1 carData_3.0-4 desc_1.2.0
[121] fansi_0.4.1 pillar_1.4.7 lattice_0.20-41 fastmap_1.0.1 httr_1.4.2 pkgbuild_1.1.0
[127] survival_3.2-7 glue_1.4.2 remotes_2.2.0 zip_2.1.1 spatstat_1.64-1 png_0.1-7
[133] bit_4.0.4 stringi_1.5.3 blob_1.2.1 memoise_1.1.0 irlba_2.3.3 future.apply_1.6.0

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