Exploration

The goal of this script is to explore Ptcra expression in our dataset. This gene has been described by Toto et al. (once).

Environment

Libraries

We load the libraries of interest :

library(Seurat)
library(ggplot2)

.libPaths()

## [1] "/shared/ifbstor1/home/aonfroy/R/x86_64-conda-linux-gnu-library/4.1"
## [2] "/shared/ifbstor1/software/miniconda/envs/r-4.2.1/lib/R/library"    
## [3] "/shared/ifbstor1/software/miniconda/envs/r-4.2.3/lib/R/library"    
## [4] "/shared/home/aonfroy/R/x86_64-conda-linux-gnu-library/4.1"

Data

Note ! Data were previously prepared using the following code :

sobj = readRDS("/shared/projects/2325_ebaii/SingleCell/TD_DATA/DATA_START/CLUSTERING/TD3A.TDCT_Scal2K.IntH20.RDS")
sobj = Seurat::RunPCA(sobj)
sobj = Seurat::RunTSNE(sobj)
sobj = sobj[, 1:200]
saveRDS(sobj, file = "/shared/projects/2325_ebaii/SingleCell/TD_RMDs/Introduction_Rmd/sobj.RDS")

We load data :

sobj = readRDS("/shared/projects/2325_ebaii/SingleCell/TD_RMDs/Introduction_Rmd/sobj.RDS")
sobj

## An object of class Seurat 
## 12926 features across 200 samples within 1 assay 
## Active assay: RNA (12926 features, 2000 variable features)
##  3 dimensional reductions calculated: pca, harmony, tsne

Analysis

We visualize our favorite gene :

Seurat::FeaturePlot(sobj, reduction = "tsne", features = "Ptcra") +
  ggplot2::theme(aspect.ratio = 1)

Which cells are positive for Ptcra ?

sobj$is_Ptcra_pos = (Seurat::FetchData(sobj, "Ptcra")[, 1] > 0)
head(sobj$is_Ptcra_pos)

## AAACCTGAGACGCTTT.1_1 AAACCTGAGGCATTGG.1_1 AAACCTGGTCAACATC.1_1 
##                FALSE                FALSE                FALSE 
## AAACCTGTCGAGGTAG.1_1 AAACCTGTCGATCCCT.1_1 AAACGGGCACTTACGA.1_1 
##                 TRUE                FALSE                FALSE

We visualize the positive and negative cells :

Seurat::DimPlot(sobj, reduction = "tsne", group.by = "is_Ptcra_pos") +
  ggplot2::theme(aspect.ratio = 1)

How many cells are positive ?

table(sobj$is_Ptcra_pos)

## 
## FALSE  TRUE 
##   110    90

Half of cells are Ptcra+.

Save

Eventually here, we can save the dataset that has been processed through this notebook.

saveRDS(sobj, file = "/shared/projects/2325_ebaii/SingleCell/TD_RMDs/Introduction_Rmd/sobj_processed.rds")

R Session

## R version 4.2.3 (2023-03-15)
## Platform: x86_64-conda-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.6 LTS
## 
## Matrix products: default
## BLAS/LAPACK: /shared/ifbstor1/software/miniconda/envs/r-4.2.3/lib/libopenblasp-r0.3.21.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] ggplot2_3.4.0      SeuratObject_4.1.3 Seurat_4.2.1      
## 
## loaded via a namespace (and not attached):
##   [1] Rtsne_0.16             colorspace_2.0-3       deldir_1.0-6          
##   [4] ellipsis_0.3.2         ggridges_0.5.4         rstudioapi_0.14       
##   [7] spatstat.data_3.0-0    farver_2.1.1           leiden_0.4.3          
##  [10] listenv_0.8.0          ggrepel_0.9.2          fansi_1.0.3           
##  [13] codetools_0.2-18       splines_4.2.3          cachem_1.0.6          
##  [16] knitr_1.40             polyclip_1.10-4        jsonlite_1.8.3        
##  [19] ica_1.0-3              cluster_2.1.4          png_0.1-7             
##  [22] uwot_0.1.14            shiny_1.7.3            sctransform_0.3.5     
##  [25] spatstat.sparse_3.0-0  compiler_4.2.3         httr_1.4.4            
##  [28] assertthat_0.2.1       Matrix_1.5-3           fastmap_1.1.0         
##  [31] lazyeval_0.2.2         cli_3.4.1              later_1.3.0           
##  [34] htmltools_0.5.3        tools_4.2.3            igraph_1.3.5          
##  [37] gtable_0.3.1           glue_1.6.2             RANN_2.6.1            
##  [40] reshape2_1.4.4         dplyr_1.0.10           Rcpp_1.0.9            
##  [43] scattermore_0.8        jquerylib_0.1.4        vctrs_0.5.0           
##  [46] nlme_3.1-160           spatstat.explore_3.0-5 progressr_0.11.0      
##  [49] lmtest_0.9-40          spatstat.random_3.0-1  xfun_0.34             
##  [52] stringr_1.4.1          globals_0.16.1         mime_0.12             
##  [55] miniUI_0.1.1.1         lifecycle_1.0.3        irlba_2.3.5.1         
##  [58] goftest_1.2-3          future_1.29.0          MASS_7.3-58.1         
##  [61] zoo_1.8-11             scales_1.2.1           promises_1.2.0.1      
##  [64] spatstat.utils_3.0-1   parallel_4.2.3         RColorBrewer_1.1-3    
##  [67] yaml_2.3.6             reticulate_1.26        pbapply_1.5-0         
##  [70] gridExtra_2.3          sass_0.4.2             stringi_1.7.8         
##  [73] highr_0.9              rlang_1.0.6            pkgconfig_2.0.3       
##  [76] matrixStats_0.62.0     evaluate_0.18          lattice_0.20-45       
##  [79] tensor_1.5             ROCR_1.0-11            purrr_0.3.5           
##  [82] labeling_0.4.2         patchwork_1.1.2        htmlwidgets_1.5.4     
##  [85] cowplot_1.1.1          tidyselect_1.2.0       parallelly_1.32.1     
##  [88] RcppAnnoy_0.0.20       plyr_1.8.8             magrittr_2.0.3        
##  [91] R6_2.5.1               generics_0.1.3         DBI_1.1.3             
##  [94] withr_2.5.0            pillar_1.8.1           fitdistrplus_1.1-8    
##  [97] survival_3.4-0         abind_1.4-5            sp_1.5-1              
## [100] tibble_3.1.8           future.apply_1.10.0    KernSmooth_2.23-20    
## [103] utf8_1.2.2             spatstat.geom_3.0-3    plotly_4.10.1         
## [106] rmarkdown_2.18         grid_4.2.3             data.table_1.14.4     
## [109] digest_0.6.30          xtable_1.8-4           tidyr_1.2.1           
## [112] httpuv_1.6.6           munsell_0.5.0          viridisLite_0.4.1     
## [115] bslib_0.4.1