Set-up

Bellow we load the HCA object that has been filtered for bad quality samples (filter_bad_oligos_and_samples.R), for clusters that were formed by a single individual(filter_bad_clusters.Rmd) and annotated.

General Cluster

Plot the proportions for caseNo

General parameters

Some general distributions about the data. We started with equal number of sex/age/tissues but because we deleted samples these are not equal any more. Also the number of cells from each one might differ

##     Old men   Old women   Young men Young women 
##          13          13          12          10
## BA4  CB CSC 
##  15  14  19
##              
##               BA4 CB CSC
##   Old men       3  5   5
##   Old women     5  4   4
##   Young men     4  3   5
##   Young women   3  2   5
## 
##     Old men   Old women   Young men Young women 
##       14486       10755       11582        8705
## 
##   BA4    CB   CSC 
##  9915 15963 19650
##              
##                BA4   CB  CSC
##   Old men     2025 6322 6139
##   Old women   2630 3940 4185
##   Young men   3613 3600 4369
##   Young women 1647 2101 4957

At a sample level more young women and CB samples were deleted.We can see there are less cells from young women, however there are less cells in the BA4 than the other two.

Age and Sex Grouping

Separate by both things in 4 plots. The plots are corrected by number of cells per sexage group first (looking at the distribution of each group across clusters) and then corrected for the number of cells per cluster (to visualize the small and big clusters equally).

Calculate proportion clusters for each AgeSex

Tissue

split the clustering by the original tissues

Calculate proportion clusters for each Tissue

tables shown with numbers

To better understand the proportions I show the different steps with tables

Click to expand 
##                          
##                           Old men Old women Young men Young women
##   Astrocyte_1                 952       593       376         535
##   Astrocyte_2                 132       148       311          73
##   Astrocyte_3                  75        36        41         192
##   Astrocyte_4                  65        23         6          38
##   Endothelial-Pericyte_1      779       408       483         346
##   Endothelial-Pericyte_2      635       460       503         325
##   Stromal_1                   159       103       124          89
##   Stromal_2                    83       244        50          89
##   Immune                      164       124       125          86
##   Microglia-Macrophages_1     782       468       368         476
##   Microglia-Macrophages_2     588       225       491         453
##   Neuron_Ex_1                 275       292       301         150
##   Neuron_Ex_2                 328       224       306         133
##   Neuron_Ex_3                  78        70       212         133
##   Neuron_In_1                 424       542       305         213
##   Neuron_In_2                 509       198       276         401
##   Neuron_In_3                 146       113       172          76
##   Neuron_In_4                  51        29        96          18
##   Neuron_RELN+_1              731       477      1209          55
##   Neuron_RELN+_2              639       439       344          24
##   Neuron_RELN+_3              128       101       488          28
##   Oligo                      5672      4716      4211        3978
##   OPC                        1091       722       784         794
##                          
##                               Old men   Old women   Young men Young women
##   Astrocyte_1              6.57186249  5.51371455  3.24641685  6.14589316
##   Astrocyte_2              0.91122463  1.37610414  2.68520117  0.83859851
##   Astrocyte_3              0.51774127  0.33472803  0.35399758  2.20562895
##   Astrocyte_4              0.44870910  0.21385402  0.05180452  0.43653073
##   Endothelial-Pericyte_1   5.37760596  3.79358438  4.17026420  3.97472717
##   Endothelial-Pericyte_2   4.38354273  4.27708043  4.34294595  3.73348650
##   Stromal_1                1.09761149  0.95769410  1.07062683  1.02240092
##   Stromal_2                0.57296700  2.26871223  0.43170437  1.02240092
##   Immune                   1.13212757  1.15295212  1.07926092  0.98793797
##   Microglia-Macrophages_1  5.39831562  4.35146444  3.17734415  5.46812177
##   Microglia-Macrophages_2  4.05909154  2.09205021  4.23933690  5.20390580
##   Neuron_Ex_1              1.89838465  2.71501627  2.59886030  1.72314762
##   Neuron_Ex_2              2.26425514  2.08275221  2.64203074  1.52785755
##   Neuron_Ex_3              0.53845092  0.65086007  1.83042652  1.52785755
##   Neuron_In_1              2.92696397  5.03951650  2.63339665  2.44686962
##   Neuron_In_2              3.51373740  1.84100418  2.38300812  4.60654796
##   Neuron_In_3              1.00786967  1.05067411  1.48506303  0.87306146
##   Neuron_In_4              0.35206406  0.26964203  0.82887239  0.20677771
##   Neuron_RELN+_1           5.04625155  4.43514644 10.43861164  0.63182079
##   Neuron_RELN+_2           4.41115560  4.08182241  2.97012606  0.27570362
##   Neuron_RELN+_3           0.88361176  0.93909809  4.21343464  0.32165422
##   Oligo                   39.15504625 43.84937238 36.35814194 45.69787478
##   OPC                      7.53140964  6.71315667  6.76912450  9.12119472

Session information

Click to expand 
## R version 4.0.4 (2021-02-15)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19041)
## 
## Matrix products: default
## 
## locale:
## [1] LC_COLLATE=English_United Kingdom.1252 
## [2] LC_CTYPE=English_United Kingdom.1252   
## [3] LC_MONETARY=English_United Kingdom.1252
## [4] LC_NUMERIC=C                           
## [5] LC_TIME=English_United Kingdom.1252    
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] ggsci_2.9          tidyr_1.1.3        ggplot2_3.3.3      here_1.0.1        
## [5] SeuratObject_4.0.0 Seurat_4.0.0      
## 
## loaded via a namespace (and not attached):
##   [1] Rtsne_0.15           colorspace_2.0-0     deldir_0.2-10       
##   [4] ellipsis_0.3.1       ggridges_0.5.3       rprojroot_2.0.2     
##   [7] spatstat.data_2.0-0  farver_2.1.0         leiden_0.3.7        
##  [10] listenv_0.8.0        ggrepel_0.9.1        fansi_0.4.2         
##  [13] codetools_0.2-18     splines_4.0.4        knitr_1.31          
##  [16] polyclip_1.10-0      jsonlite_1.7.2       ica_1.0-2           
##  [19] cluster_2.1.1        png_0.1-7            uwot_0.1.10         
##  [22] shiny_1.6.0          sctransform_0.3.2    compiler_4.0.4      
##  [25] httr_1.4.2           assertthat_0.2.1     Matrix_1.3-2        
##  [28] fastmap_1.1.0        lazyeval_0.2.2       later_1.1.0.1       
##  [31] htmltools_0.5.1.1    tools_4.0.4          igraph_1.2.6        
##  [34] gtable_0.3.0         glue_1.4.2           RANN_2.6.1          
##  [37] reshape2_1.4.4       dplyr_1.0.5          Rcpp_1.0.6          
##  [40] spatstat_1.64-1      scattermore_0.7      jquerylib_0.1.3     
##  [43] vctrs_0.3.6          nlme_3.1-152         lmtest_0.9-38       
##  [46] xfun_0.21            stringr_1.4.0        globals_0.14.0      
##  [49] mime_0.10            miniUI_0.1.1.1       lifecycle_1.0.0     
##  [52] irlba_2.3.3          goftest_1.2-2        future_1.21.0       
##  [55] MASS_7.3-53.1        zoo_1.8-9            scales_1.1.1        
##  [58] promises_1.2.0.1     spatstat.utils_2.1-0 parallel_4.0.4      
##  [61] RColorBrewer_1.1-2   yaml_2.2.1           reticulate_1.18     
##  [64] pbapply_1.4-3        gridExtra_2.3        sass_0.3.1          
##  [67] rpart_4.1-15         stringi_1.5.3        highr_0.8           
##  [70] rlang_0.4.10         pkgconfig_2.0.3      matrixStats_0.58.0  
##  [73] evaluate_0.14        lattice_0.20-41      ROCR_1.0-11         
##  [76] purrr_0.3.4          tensor_1.5           patchwork_1.1.1     
##  [79] htmlwidgets_1.5.3    labeling_0.4.2       cowplot_1.1.1       
##  [82] tidyselect_1.1.0     parallelly_1.24.0    RcppAnnoy_0.0.18    
##  [85] plyr_1.8.6           magrittr_2.0.1       R6_2.5.0            
##  [88] generics_0.1.0       DBI_1.1.1            pillar_1.5.1        
##  [91] withr_2.4.1          mgcv_1.8-34          fitdistrplus_1.1-3  
##  [94] survival_3.2-10      abind_1.4-5          tibble_3.1.0        
##  [97] future.apply_1.7.0   crayon_1.4.1         KernSmooth_2.23-18  
## [100] utf8_1.2.1           plotly_4.9.3         rmarkdown_2.7       
## [103] grid_4.0.4           data.table_1.14.0    digest_0.6.27       
## [106] xtable_1.8-4         httpuv_1.5.5         munsell_0.5.0       
## [109] viridisLite_0.3.0    bslib_0.2.4