R Notebook
Last updated: 2019-11-12
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Knit directory: 10x-adipocyte-analysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | 1c9d3ae | Pytrik Folkertsma | 2019-11-12 | Build site. |
| Rmd | cb1f113 | Pytrik Folkertsma | 2019-11-12 | wflow_publish(c(“analysis/wolfrum_U-L-branch-markers.Rmd”)) |
| html | de58caf | Pytrik Folkertsma | 2019-11-12 | Build site. |
| Rmd | b6a5a90 | Pytrik Folkertsma | 2019-11-12 | wflow_publish(c(“analysis/wolfrum_U-L-branch-markers.Rmd”)) |
| html | 6bd1632 | Pytrik Folkertsma | 2019-11-11 | Build site. |
| Rmd | 77918ba | Pytrik Folkertsma | 2019-11-11 | U and L branch markers in wolfrum data |
| html | cf6e507 | Pytrik Folkertsma | 2019-11-11 | Build site. |
| Rmd | 403855e | Pytrik Folkertsma | 2019-11-11 | U and L branch markers in wolfrum data |
library(Seurat)
library(cowplot)
library(dplyr)
library(tidyr)
library(knitr)
library(kableExtra)
library(dplyr)wolfrum <- readRDS('/projects/timshel/sc-scheele_lab_adipose_fluidigm_c1/data-wolfrum/wolfrum.compute.seurat_obj.rds')
markers <- read.table('/projects/pytrik/sc_adipose/analyze_10x_fluidigm/10x-adipocyte-analysis/output/markergenes/180831/markers_10x-180831_upperbranch_lowerbranch_negbinom.tsv', header=T)plot_featureplots <- function(data, markergenes, dir, branch){
plots <- FeaturePlot(data, features=as.vector(markergenes$gene[1:56]), combine = FALSE, pt.size=0.2, cols=c('gray', 'blue'))
plots_edited <- list()
for(i in 1:length(plots)) {
plots_edited[[i]] <- plots[[i]] + NoAxes()
save_plot(paste(dir, '/', branch, '/', as.vector(markergenes$gene[i]) ,'.pdf', sep=''), plots_edited[[i]], base_height=6, base_width=6)
}
grid_1 <- plot_grid(plotlist=plots_edited[1:28], ncol=3)
grid_2 <- plot_grid(plotlist=plots_edited[29:56], ncol=3)
#pdf
save_plot(paste(dir, 'markers_', branch, '_1-28.pdf', sep=''), grid_1, base_height=25, base_width=9)
save_plot(paste(dir, 'markers_', branch, '_29-56.pdf', sep=''), grid_2, base_height=25, base_width=9)
#png
save_plot(paste(dir, 'markers_', branch, '_1-28.png', sep=''), grid_1, base_height=25, base_width=9)
save_plot(paste(dir, 'markers_', branch, '_29-56.png', sep=''), grid_2, base_height=25, base_width=9)
return(list(grid_1, grid_2))
}#U branch marker genes all wolfrum data
markers_u <- markers[markers$avg_logFC > 0,]
markers_u <- markers_u[order(-markers_u$avg_logFC),]Top 56 markers for U branch sorted on logFC.
grids <- plot_featureplots(wolfrum, markergenes=markers_u, dir='/projects/pytrik/sc_adipose/analyze_10x_fluidigm/10x-adipocyte-analysis/figures/figures_paper/supplementary_figures/wolfrum/u_l_branch_markers/all_data', branch='U_branch')grids[[1]]
grids[[2]]
#L branch marker genes all wolfrum data
markers_l <- markers[markers$avg_logFC < 0,]
markers_l <- markers_l[order(markers_l$avg_logFC),]Top 56 markers for L branch sorted on logFC.
grids <- plot_featureplots(wolfrum, markergenes=markers_l, dir='/projects/pytrik/sc_adipose/analyze_10x_fluidigm/10x-adipocyte-analysis/figures/figures_paper/supplementary_figures/wolfrum/u_l_branch_markers/all_data', branch='L_branch')Warning in FetchData(object = object, vars = c(dims, "ident", features), :
The following requested variables were not found: RP11-572C15.6, PPAP2BWarning in FeaturePlot(data, features =
as.vector(markergenes$gene[1:56]), : All cells have the same value (0) of
IFITM1.grids[[1]]
grids[[2]]
#U branch markers for wolfrum preadipocyte subset
wolfrum_subset <- readRDS('/projects/pytrik/sc_adipose/analyze_10x_fluidigm/10x-adipocyte-analysis/output/seurat_objects/wolfrum/wolfrum.preadipocyte_subset.rds')Top 56 markers for U branch sorted on logFC.
grids <- plot_featureplots(wolfrum_subset, markergenes=markers_u, dir='/projects/pytrik/sc_adipose/analyze_10x_fluidigm/10x-adipocyte-analysis/figures/figures_paper/supplementary_figures/wolfrum/u_l_branch_markers/preadipocyte_subset', branch='U_branch')grids[[1]]
grids[[2]]
#L branch markers for wolfrum adipocyte subset
Top 56 markers for L branch sorted on logFC.
grids <- plot_featureplots(wolfrum_subset, markergenes=markers_l, dir='/projects/pytrik/sc_adipose/analyze_10x_fluidigm/10x-adipocyte-analysis/figures/figures_paper/supplementary_figures/wolfrum/u_l_branch_markers/preadipocyte_subset', branch='L_branch')Warning in FetchData(object = object, vars = c(dims, "ident", features), :
The following requested variables were not found: RP11-572C15.6, PPAP2BWarning in FeaturePlot(data, features =
as.vector(markergenes$gene[1:56]), : All cells have the same value (0) of
IFITM1.grids[[1]]
grids[[2]]
sessionInfo()R version 3.5.3 (2019-03-11)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Storage
Matrix products: default
BLAS/LAPACK: /usr/lib64/libopenblas-r0.3.3.so
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
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] kableExtra_1.1.0 knitr_1.22 tidyr_0.8.3 dplyr_0.8.0.1
[5] cowplot_0.9.4 ggplot2_3.1.0 Seurat_3.1.1
loaded via a namespace (and not attached):
[1] tsne_0.1-3 nlme_3.1-140 bitops_1.0-6
[4] fs_1.2.7 webshot_0.5.1 RcppAnnoy_0.0.13
[7] RColorBrewer_1.1-2 httr_1.4.0 rprojroot_1.3-2
[10] sctransform_0.2.0 tools_3.5.3 backports_1.1.3
[13] R6_2.4.0 irlba_2.3.3 KernSmooth_2.23-15
[16] uwot_0.1.4 lazyeval_0.2.2 colorspace_1.4-1
[19] withr_2.1.2 npsurv_0.4-0 gridExtra_2.3
[22] tidyselect_0.2.5 compiler_3.5.3 git2r_0.25.2
[25] rvest_0.3.3 xml2_1.2.0 plotly_4.8.0
[28] labeling_0.3 caTools_1.17.1.2 scales_1.0.0
[31] lmtest_0.9-36 readr_1.3.1 ggridges_0.5.1
[34] pbapply_1.4-0 stringr_1.4.0 digest_0.6.18
[37] rmarkdown_1.12 R.utils_2.8.0 pkgconfig_2.0.2
[40] htmltools_0.3.6 bibtex_0.4.2 htmlwidgets_1.3
[43] rlang_0.3.2 rstudioapi_0.10 zoo_1.8-5
[46] jsonlite_1.6 ica_1.0-2 gtools_3.8.1
[49] R.oo_1.22.0 magrittr_1.5 Matrix_1.2-17
[52] Rcpp_1.0.1 munsell_0.5.0 ape_5.3
[55] reticulate_1.11.1 R.methodsS3_1.7.1 stringi_1.4.3
[58] whisker_0.3-2 yaml_2.2.0 gbRd_0.4-11
[61] MASS_7.3-51.4 gplots_3.0.1.1 Rtsne_0.15
[64] plyr_1.8.4 grid_3.5.3 parallel_3.5.3
[67] gdata_2.18.0 listenv_0.7.0 ggrepel_0.8.0
[70] crayon_1.3.4 lattice_0.20-38 splines_3.5.3
[73] hms_0.4.2 SDMTools_1.1-221 pillar_1.3.1
[76] igraph_1.2.4 reshape2_1.4.3 future.apply_1.3.0
[79] codetools_0.2-16 leiden_0.3.1 glue_1.3.1
[82] evaluate_0.13 lsei_1.2-0 metap_1.1
[85] RcppParallel_4.4.4 data.table_1.12.0 png_0.1-7
[88] Rdpack_0.10-1 gtable_0.3.0 RANN_2.6.1
[91] purrr_0.3.2 future_1.15.0 assertthat_0.2.1
[94] xfun_0.5 rsvd_1.0.2 survival_2.43-3
[97] viridisLite_0.3.0 tibble_2.1.1 workflowr_1.2.0
[100] cluster_2.1.0 globals_0.12.4 fitdistrplus_1.0-14
[103] ROCR_1.0-7