Code used in the paper: Single Cell Analysis Identifies Conserved Features of Immune Dysfunction in Simulated Microgravity and Spaceflight
R version 4.2.2 Patched (2022-11-10 r83330) Platform: x86_64-pc-linux-gnu (64-bit) Running under: Ubuntu 20.04.5 LTS
Matrix products: default BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0 LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8
[4] LC_COLLATE=en_US.UTF-8 LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C LC_ADDRESS=C
[10] LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages: [1] stats4 stats graphics grDevices utils datasets methods base
other attached packages:
[1] corrplot_0.92 ggpubr_0.4.0 ggridges_0.5.3
[4] magrittr_2.0.3 SeuratWrappers_0.3.0 monocle3_1.2.9
[7] SingleCellExperiment_1.18.0 SummarizedExperiment_1.26.1 GenomicRanges_1.48.0
[10] GenomeInfoDb_1.32.2 IRanges_2.30.0 S4Vectors_0.34.0
[13] MatrixGenerics_1.8.1 matrixStats_0.62.0 Biobase_2.56.0
[16] BiocGenerics_0.42.0 RColorBrewer_1.1-3 tidyr_1.2.0
[19] DoubletFinder_2.0.3 dplyr_1.0.9 tibble_3.1.8
[22] future_1.27.0 glmGamPoi_1.8.0 sctransform_0.3.3
[25] ggplot2_3.3.6 patchwork_1.1.1 sp_1.5-0
[28] SeuratObject_4.1.0 Seurat_4.1.1
loaded via a namespace (and not attached):
[1] readxl_1.4.1 backports_1.4.1 plyr_1.8.7
[4] igraph_1.3.4 lazyeval_0.2.2 splines_4.2.2
[7] listenv_0.8.0 scattermore_0.8 digest_0.6.29
[10] htmltools_0.5.3 viridis_0.6.2 fansi_1.0.3
[13] tensor_1.5 cluster_2.1.4 ROCR_1.0-11
[16] remotes_2.4.2 globals_0.16.0 R.utils_2.12.0
[19] spatstat.sparse_2.1-1 colorspace_2.0-3 ggrepel_0.9.1
[22] RCurl_1.98-1.8 jsonlite_1.8.0 lme4_1.1-30
[25] progressr_0.10.1 spatstat.data_2.2-0 survival_3.4-0
[28] zoo_1.8-10 glue_1.6.2 polyclip_1.10-0
[31] gtable_0.3.0 zlibbioc_1.42.0 XVector_0.36.0
[34] leiden_0.4.2 DelayedArray_0.22.0 car_3.1-0
[37] future.apply_1.9.0 abind_1.4-5 scales_1.2.0
[40] DBI_1.1.3 rstatix_0.7.0 spatstat.random_2.2-0
[43] miniUI_0.1.1.1 Rcpp_1.0.9 viridisLite_0.4.0
[46] xtable_1.8-4 reticulate_1.25 spatstat.core_2.4-4
[49] rsvd_1.0.5 htmlwidgets_1.5.4 httr_1.4.3
[52] ellipsis_0.3.2 ica_1.0-3 R.methodsS3_1.8.2
[55] pkgconfig_2.0.3 uwot_0.1.11 deldir_1.0-6
[58] utf8_1.2.2 tidyselect_1.1.2 rlang_1.0.4
[61] reshape2_1.4.4 later_1.3.0 munsell_0.5.0
[64] cellranger_1.1.0 tools_4.2.2 cli_3.3.0
[67] generics_0.1.3 broom_1.0.0 stringr_1.4.0
[70] fastmap_1.1.0 goftest_1.2-3 fitdistrplus_1.1-8
[73] purrr_0.3.4 RANN_2.6.1 pbapply_1.5-0
[76] nlme_3.1-161 mime_0.12 R.oo_1.25.0
[79] compiler_4.2.2 rstudioapi_0.13 plotly_4.10.0
[82] png_0.1-7 ggsignif_0.6.3 spatstat.utils_2.3-1
[85] stringi_1.7.8 rgeos_0.5-9 lattice_0.20-45
[88] Matrix_1.5-1 nloptr_2.0.3 vctrs_0.4.1
[91] pillar_1.8.0 lifecycle_1.0.1 BiocManager_1.30.18
[94] spatstat.geom_2.4-0 lmtest_0.9-40 RcppAnnoy_0.0.19
[97] data.table_1.14.2 cowplot_1.1.1 bitops_1.0-7
[100] irlba_2.3.5 httpuv_1.6.5 R6_2.5.1
[103] promises_1.2.0.1 KernSmooth_2.23-20 gridExtra_2.3
[106] parallelly_1.32.1 codetools_0.2-18 boot_1.3-28
[109] MASS_7.3-58 assertthat_0.2.1 withr_2.5.0
[112] GenomeInfoDbData_1.2.8 mgcv_1.8-41 parallel_4.2.2
[115] terra_1.6-3 grid_4.2.2 rpart_4.1.19
[118] minqa_1.2.4 carData_3.0-5 Rtsne_0.16
[121] shiny_1.7.2
Cellranger software and its instruction is on https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/what-is-cell-ranger
Seurat and its instruction is on https://satijalab.org/seurat/index.html
MTD software and its instruction is on https://github.com/FEI38750/MTD MTD is running under Conda environments with Bash, its versions of dependencies are in the .yml files in the MTD/Installation.
Raw and processed 10x Genomics and bulk RNA-seq data can be found at Gene Expression Omnibus (GEO) using accession number GSE218937.
The figures and results relevant to PBMC sequencing in the main body and supplementary of the paper can be generated by using the code and following the steps. Please adjust the path and thread number properly in the script depending on the user's actual computational environment.
To start from the reads count matrixes, both cellranger and MTD processed, we recommend to use desktop with 128GB RAM and 8 CPUs or HPC to run the scripts. (from step1: 1_Preprocess_the_count_matrix.r). The running is expected to be done in one day.
To start from the fastq files, we recommend to use desktop with 160GB RAM and 16 CPUs or HPC to run the scripts. (from step0: 0_CountMatrix_making.sh)