The splanchnic mesenchyme is the tissue of origin for fibroblasts in the pancreas during homeostasis and tumorigenesis
scRNA-seq analysis codes for the paper
Seurat oject used in codes should be found under GSE200903, FibroXplorer and Tabula Sapiens
R with packages
library(Seurat)
library(SeuratDisk)
library(sctransform)
library(patchwork)
library(dplyr)
library(ggplot2)
library(reshape)
library(writexl)
library(readxl)
library(ComplexHeatmap)
library(tidyverse)
library(circlize)
library(collections)
library(Matrix)
SessionInfo
R version 4.2.1 (2022-06-23)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Monterey 12.5
Matrix products: default
LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] grid stats graphics grDevices utils datasets methods base
other attached packages:
[1] Matrix_1.4-1 collections_0.3.5 circlize_0.4.15 forcats_0.5.1
[5] stringr_1.4.0 purrr_0.3.4 readr_2.1.2 tidyr_1.2.0
[9] tibble_3.1.8 tidyverse_1.3.2 ComplexHeatmap_2.12.0 readxl_1.4.0
[13] writexl_1.4.0 reshape_0.8.9 ggplot2_3.3.6 dplyr_1.0.9
[17] patchwork_1.1.1 sctransform_0.3.3 SeuratDisk_0.0.0.9020 sp_1.5-0
[21] SeuratObject_4.1.0 Seurat_4.1.1
loaded via a namespace (and not attached):
[1] backports_1.4.1 plyr_1.8.7 igraph_1.3.4 lazyeval_0.2.2
[5] splines_4.2.1 listenv_0.8.0 scattermore_0.8 digest_0.6.29
[9] foreach_1.5.2 htmltools_0.5.3 fansi_1.0.3 magrittr_2.0.3
[13] tensor_1.5 googlesheets4_1.0.0 cluster_2.1.3 doParallel_1.0.17
[17] ROCR_1.0-11 tzdb_0.3.0 globals_0.15.1 modelr_0.1.8
[21] matrixStats_0.62.0 spatstat.sparse_2.1-1 colorspace_2.0-3 rvest_1.0.2
[25] ggrepel_0.9.1 haven_2.5.0 crayon_1.5.1 jsonlite_1.8.0
[29] progressr_0.10.1 spatstat.data_2.2-0 survival_3.3-1 zoo_1.8-10
[33] iterators_1.0.14 glue_1.6.2 polyclip_1.10-0 gtable_0.3.0
[37] gargle_1.2.0 leiden_0.4.2 GetoptLong_1.0.5 future.apply_1.9.0
[41] shape_1.4.6 BiocGenerics_0.42.0 abind_1.4-5 scales_1.2.0
[45] DBI_1.1.3 spatstat.random_2.2-0 miniUI_0.1.1.1 Rcpp_1.0.9
[49] viridisLite_0.4.0 xtable_1.8-4 clue_0.3-61 reticulate_1.25
[53] spatstat.core_2.4-4 bit_4.0.4 stats4_4.2.1 htmlwidgets_1.5.4
[57] httr_1.4.3 RColorBrewer_1.1-3 ellipsis_0.3.2 ica_1.0-3
[61] pkgconfig_2.0.3 uwot_0.1.11 dbplyr_2.2.1 deldir_1.0-6
[65] utf8_1.2.2 tidyselect_1.1.2 rlang_1.0.4 reshape2_1.4.4
[69] later_1.3.0 munsell_0.5.0 cellranger_1.1.0 tools_4.2.1
[73] cli_3.3.0 generics_0.1.3 broom_1.0.0 ggridges_0.5.3
[77] fastmap_1.1.0 goftest_1.2-3 bit64_4.0.5 fs_1.5.2
[81] fitdistrplus_1.1-8 RANN_2.6.1 pbapply_1.5-0 future_1.27.0
[85] nlme_3.1-158 mime_0.12 xml2_1.3.3 hdf5r_1.3.5
[89] compiler_4.2.1 rstudioapi_0.13 plotly_4.10.0 png_0.1-7
[93] spatstat.utils_2.3-1 reprex_2.0.1 stringi_1.7.8 rgeos_0.5-9
[97] lattice_0.20-45 vctrs_0.4.1 pillar_1.8.0 lifecycle_1.0.1
[101] spatstat.geom_2.4-0 lmtest_0.9-40 GlobalOptions_0.1.2 RcppAnnoy_0.0.19
[105] data.table_1.14.2 cowplot_1.1.1 irlba_2.3.5 httpuv_1.6.5
[109] R6_2.5.1 promises_1.2.0.1 KernSmooth_2.23-20 gridExtra_2.3
[113] IRanges_2.30.0 parallelly_1.32.1 codetools_0.2-18 MASS_7.3-58
[117] assertthat_0.2.1 rjson_0.2.21 withr_2.5.0 S4Vectors_0.34.0
[121] hms_1.1.1 mgcv_1.8-40 parallel_4.2.1 rpart_4.1.16
[125] googledrive_2.0.0 Rtsne_0.16 lubridate_1.8.0 shiny_1.7.2
All dependent packages can be install with bioconductor or install.packages() command.
Installation time varies based on the computer. Should not be longer than 30 mins.
human_correlation.R will generate heatmap of genes highly expressed in both the splanchnic mesenchyme and human pancreatic CAFs compared to human pancreatic TRFs (upper block), and genes highly expressed in both the splanchnic mesenchyme and human pancreatic TRFs compared to human pancreatic CAFs (lower block). Spl, splanchnic; Meso, mesoderm; CAFs, cancer associated fibroblasts; TRFs, tissue resident fibroblasts.
mouse_analysis.R will generate 1. the heatmap of genes highly expressed in both the splanchnic mesenchyme and CAFs compared to TRFs (upper block), and genes highly expressed in both the splanchnic mesenchyme and TRFs compared to CAFs (lower block). 2. generate Dimplot for Eng. 3. Find DEGs of 6 vs 0,1,13 clusters and 4.UMAPs for different subtypes of fibroblasts in between IcreT and KPFIcreT tissues
1.
2.
4.
It is recommeded to run codes in Rstudio. Runing the code line by line :) All pics should be reproducible from the codes
Michael Zimmermann:[email protected]; Kun Fang: [email protected]; Lu Han: [email protected]
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