Last updated: 2020-07-14

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Knit directory: jesslyn_ovca/analysis/

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Read data and glimpse

ovca_10x = here::here("data","Izar_2020","Izar_2020_10x.RDS") %>% readRDS()
glimpse(ovca_10x@meta.data)
Rows: 9,333
Columns: 13
$ orig.ident       <fct> Izar 2020: cohort 1 - 10x, Izar 2020: cohort 1 - 10x…
$ nCount_RNA       <dbl> 4677.995, 5899.826, 6746.130, 10510.568, 9628.698, 4…
$ nFeature_RNA     <int> 839, 1196, 1343, 2358, 2022, 875, 1117, 747, 775, 21…
$ patient          <dbl> 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5…
$ time             <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
$ sample.ID        <fct> 5.1, 5.1, 5.1, 5.1, 5.1, 5.1, 5.1, 5.1, 5.1, 5.1, 5.…
$ clst             <dbl> 1, 1, 1, 2, 2, 1, 1, 1, 1, 2, 2, 1, 3, 1, 2, 2, 2, 4…
$ TSNE.x           <dbl> 45.336310, 35.076090, 27.412720, -12.224810, -1.3493…
$ TSNE.y           <dbl> 46.9334800, -20.1010500, -1.8764310, -86.4608200, -6…
$ percent.mt       <dbl> 1.82006622, 0.94261326, 0.63322995, 0.65250061, 0.89…
$ n.exp.hkgenes    <int> 49, 56, 44, 67, 64, 56, 55, 51, 48, 64, 63, 46, 68, …
$ cell.type        <chr> "Malignant", "Malignant", "Malignant", "Malignant", …
$ treatment.status <chr> "After 1 cycle of chemotherapy", "After 1 cycle of c…
ovca_SS2 = here::here("data","Izar_2020","Izar_2020_SS2.RDS") %>% readRDS()
glimpse(ovca_SS2@meta.data)
Rows: 1,297
Columns: 12
$ orig.ident       <fct> SS2, SS2, SS2, SS2, SS2, SS2, SS2, SS2, SS2, SS2, SS…
$ nCount_RNA       <dbl> 22621.85, 24485.64, 24483.62, 30512.32, 28782.77, 24…
$ nFeature_RNA     <int> 6047, 6892, 7090, 9473, 8803, 6414, 5093, 7327, 6228…
$ tSNE1            <dbl> 15.14379, 13.51832, 17.96613, 17.31081, 18.68905, 16…
$ tSNE2            <dbl> 25.80910, 27.50209, 28.69189, 38.13392, 38.33419, 29…
$ Patient          <dbl> 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7…
$ Time             <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
$ clst             <dbl> 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5…
$ percent.mt       <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
$ n.exp.hkgenes    <int> 83, 81, 84, 89, 88, 85, 84, 85, 81, 82, 88, 85, 80, …
$ treatment.status <chr> "On-treatment", "On-treatment", "On-treatment", "On-…
$ cell.type        <fct> Malignant, Malignant, Malignant, Malignant, Malignan…
ovca_PDX = here::here("data","Izar_2020","Izar_2020_PDX.RDS") %>% readRDS()
glimpse(ovca_PDX@meta.data)
Rows: 795
Columns: 8
$ orig.ident       <fct> Izar 2020: PDX data, Izar 2020: PDX data, Izar 2020:…
$ nCount_RNA       <dbl> 11951.870, 18239.655, 16691.926, 14353.022, 9443.317…
$ nFeature_RNA     <int> 3628, 10004, 8821, 7698, 3728, 3284, 7296, 10075, 54…
$ mouse_ID         <dbl> 500, 494, 494, 500, 494, 500, 500, 500, 496, 500, 50…
$ model_ID         <chr> "DF20", "DF20", "DF20", "DF20", "DF20", "DF20", "DF2…
$ treatment.status <chr> "relapse", "vehicle", "vehicle", "relapse", "vehicle…
$ n.exp.hkgenes    <int> 72, 91, 88, 83, 76, 73, 83, 86, 80, 84, 81, 82, 84, …
$ percent.mt       <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…

Descriptive tables

dims = map_dfr(list("10x" = ovca_10x, "SS2" = ovca_SS2, "PDX" = ovca_PDX),
               function(i){
                 x = tibble(.rows = 1)
                 x$dataset = i@project.name
                 x$genes = dim(i)[1]
                 x$cells = dim(i)[2]
                 x$patients = try_default(length(unique(select(i@meta.data, matches("patient"))[[1]])), NA)
                 x$mouse_models = try_default(length(unique(i$model_ID)),NA)
                 return(x)
                 }
               )
Error in `[[.Seurat`(x, i, drop = TRUE) : 
  Cannot find 'model_ID' in this Seurat object
Error in `[[.Seurat`(x, i, drop = TRUE) : 
  Cannot find 'model_ID' in this Seurat object
Error in .subset2(x, i, exact = exact) : subscript out of bounds
gt(dims)
dataset genes cells patients mouse_models
Izar 2020: cohort 1 - 10x 11548 9333 6 NA
Izar 2020: cohort 2 - SS2 23687 1297 9 NA
Izar 2020: PDX data 23686 795 NA 3 
source(here::here('code','seurat_tab.R'))

Attaching package: 'reshape2'
The following object is masked from 'package:tidyr':

    smiths
# create summary tables using Mike's function
seurat_tab(ovca_10x, col_var = "patient", row_var = "cell.type", title = "Patient cells")
Patient cells
dataset: Izar 2020: cohort 1 - 10x
patient total
1 2 3 4 5 6
B cells 0 14 8 0 0 1 23
DC 3 63 99 48 36 8 257
Erythrocytes 9 0 14 0 0 0 23
Fibroblast 5 427 361 0 36 1 830
Macrophage 135 4 2 618 1027 1166 2952
Malignant 50 51 92 2 4893 11 5099
T cells 149 0 0 0 0 0 149
total 351 559 576 668 5,992 1,187 9,333 
# seurat_tab(ovca_10x, col_var = "patient", row_var = "cell.type", group_var = "treatment.status", title = "Patient cells by treatment status") %>% row_group_order(c("Treatment-naïve","After 1 cycle of chemotherapy","On-treatment"))
seurat_tab(ovca_SS2, col_var = "Patient", row_var = "cell.type", title = "Patient cells")
Patient cells
dataset: Izar 2020: cohort 2 - SS2
Patient total
5 7 8 9 10 11 21 22 23
Fibroblast 0 0 0 0 0 6 55 11 33 105
Macrophage 0 3 2 0 0 10 9 6 0 30
Malignant 157 252 179 221 299 52 0 2 0 1162
total 157 255 181 221 299 68 64 19 33 1,297 
# seurat_tab(ovca_SS2, col_var = "Patient", row_var = "cell.type", group_var = "treatment.status", title = "Patient cells by treatment status")
seurat_tab(ovca_PDX, col_var = "model_ID", row_var = "treatment.status", title = "Mouse model cells by Treatment Status")
Mouse model cells by Treatment Status
dataset: Izar 2020: PDX data
model_ID total
DF101 DF20 DF68
MRD 60 171 51 282
relapse 55 172 73 300
vehicle 39 151 23 213
total 154 494 147 795

Scale and Center

ovca_10x = ScaleData(ovca_10x, do.scale = T, do.center = T)
Centering and scaling data matrix
ovca_SS2 = ScaleData(ovca_SS2, do.scale = T, do.center = T)
Centering and scaling data matrix
ovca_PDX = ScaleData(ovca_PDX, do.scale = T, do.center = T)
Centering and scaling data matrix

Visualize cell-wise and gene-wise distributions

cells = tibble(); geneMeans = tibble(); geneSDs = tibble(); features = tibble()

for (i in list("10x" = ovca_10x, "SS2" = ovca_SS2, "PDX" = ovca_PDX)){
  x = Matrix::colMeans(i[["RNA"]]@data) %>% as_tibble()
  names(x) = "normalized"
  x$scaled_centered = Matrix::colMeans(i[["RNA"]]@scale.data) 
  x$dataset = i@project.name
  cells = rbind(cells, x)
  
  y = Matrix::rowMeans(i[["RNA"]]@data) %>% as_tibble()
  names(y) = "normalized"
  y$scaled_centered = Matrix::rowMeans(i[["RNA"]]@scale.data)
  y$dataset = i@project.name
  geneMeans = rbind(geneMeans, y)

  w = apply(i[["RNA"]]@data, 1, sd) %>% as_tibble()
  names(w) = "normalized"
  w$scaled_centered = Matrix::rowMeans(i[["RNA"]]@scale.data)
  w$dataset = i@project.name
  geneSDs = rbind(geneSDs, w)
  
  z = select(i@meta.data, c("orig.ident","nCount_RNA","nFeature_RNA"))
  features = rbind(features, z)
}

melt(cells) %>%
  ggplot(aes(x = dataset, y = value)) +
  geom_violin(aes(fill = variable), trim = FALSE, position = position_dodge(0.9) ) +
  geom_boxplot(aes(position = variable), width = 0.15, position = position_dodge(0.9), alpha = 0.3) +
  labs(title = "Mean expression across cells",
       x = "dataset",
       y = "expression value",
       fill = NULL) +
  theme_bw()
Using dataset as id variables
Warning: attributes are not identical across measure variables; they will be
dropped
Warning: Ignoring unknown aesthetics: position

melt(geneMeans) %>%
  ggplot(aes(x = dataset, y = value)) +
  geom_violin(aes(fill = variable), trim = FALSE, position = position_dodge(0.9) ) +
  geom_boxplot(width = 0.15, position = position_dodge(0.9), alpha = 0.3) +
  facet_wrap(. ~ variable, scales = "free") +
  labs(title = "Mean expression across genes",
       x = "dataset",
       y = "expression value",
       fill = NULL) +
  guides(fill = F) +
  theme_bw()
Using dataset as id variables
Warning: attributes are not identical across measure variables; they will be
dropped

melt(geneSDs) %>%
  ggplot(aes(x = dataset, y = value)) +
  geom_violin(aes(fill = variable), trim = FALSE, position = position_dodge(0.9) ) +
  geom_boxplot(aes(position = variable), width = 0.15, position = position_dodge(0.9), alpha = 0.3) +
  geom_violin(aes(fill = variable), trim = FALSE, position = position_dodge(0.9) ) +
  geom_boxplot(width = 0.15, position = position_dodge(0.9), alpha = 0.3) +
  facet_wrap(. ~ variable, scales = "free") +
  labs(title = "SD across genes",
       x = "dataset",
       y = "expression value",
       fill = NULL) +
  guides(fill = F) +
  theme_bw()
Using dataset as id variables
Warning: attributes are not identical across measure variables; they will be
dropped

Warning: Ignoring unknown aesthetics: position

melt(features) %>%
  ggplot(aes(x = orig.ident, y = value)) + 
  geom_violin(aes(fill = variable), trim = FALSE, position = position_dodge(0.9) ) +
  geom_boxplot(width = 0.15, position = position_dodge(0.9), alpha = 0.3) +
  facet_wrap(. ~ variable, scales = "free") +
  labs(title = "UMIs (nCount) and Genes (nFeature) per cell",
       x = "dataset",
       y = "expression value",
       fill = NULL) +
  guides(fill = F) +
  theme_bw()
Using orig.ident as id variables


sessionInfo()
R version 4.0.2 (2020-06-22)
Platform: x86_64-apple-darwin19.5.0 (64-bit)
Running under: macOS Catalina 10.15.5

Matrix products: default
BLAS/LAPACK: /usr/local/Cellar/openblas/0.3.10_1/lib/libopenblasp-r0.3.10.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] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
 [1] reshape2_1.4.4   tidyselect_1.1.0 gt_0.2.1         plyr_1.8.6      
 [5] Seurat_3.1.5     forcats_0.5.0    stringr_1.4.0    dplyr_1.0.0     
 [9] purrr_0.3.4      readr_1.3.1      tidyr_1.1.0      tibble_3.0.3    
[13] ggplot2_3.3.2    tidyverse_1.3.0 

loaded via a namespace (and not attached):
  [1] Rtsne_0.15         colorspace_1.4-1   ellipsis_0.3.1    
  [4] ggridges_0.5.2     rprojroot_1.3-2    fs_1.4.2          
  [7] rstudioapi_0.11    farver_2.0.3       leiden_0.3.3      
 [10] listenv_0.8.0      ggrepel_0.8.2      fansi_0.4.1       
 [13] lubridate_1.7.9    xml2_1.3.2         codetools_0.2-16  
 [16] splines_4.0.2      knitr_1.29         jsonlite_1.7.0    
 [19] workflowr_1.6.2    broom_0.7.0        ica_1.0-2         
 [22] cluster_2.1.0      dbplyr_1.4.4       png_0.1-7         
 [25] uwot_0.1.8         sctransform_0.2.1  compiler_4.0.2    
 [28] httr_1.4.1         backports_1.1.8    assertthat_0.2.1  
 [31] Matrix_1.2-18      lazyeval_0.2.2     cli_2.0.2         
 [34] later_1.1.0.1      htmltools_0.5.0    tools_4.0.2       
 [37] rsvd_1.0.3         igraph_1.2.5       gtable_0.3.0      
 [40] glue_1.4.1         RANN_2.6.1         Rcpp_1.0.5        
 [43] cellranger_1.1.0   vctrs_0.3.1        ape_5.4           
 [46] nlme_3.1-148       lmtest_0.9-37      xfun_0.15         
 [49] globals_0.12.5     rvest_0.3.5        lifecycle_0.2.0   
 [52] irlba_2.3.3        renv_0.11.0-4      future_1.18.0     
 [55] MASS_7.3-51.6      zoo_1.8-8          scales_1.1.1      
 [58] hms_0.5.3          promises_1.1.1     parallel_4.0.2    
 [61] RColorBrewer_1.1-2 yaml_2.2.1         gridExtra_2.3     
 [64] reticulate_1.16    pbapply_1.4-2      sass_0.2.0        
 [67] stringi_1.4.6      checkmate_2.0.0    rlang_0.4.7       
 [70] pkgconfig_2.0.3    evaluate_0.14      lattice_0.20-41   
 [73] ROCR_1.0-11        labeling_0.3       patchwork_1.0.1   
 [76] htmlwidgets_1.5.1  cowplot_1.0.0      here_0.1          
 [79] RcppAnnoy_0.0.16   magrittr_1.5       R6_2.4.1          
 [82] generics_0.0.2     DBI_1.1.0          pillar_1.4.6      
 [85] haven_2.3.1        whisker_0.4        withr_2.2.0       
 [88] fitdistrplus_1.1-1 survival_3.2-3     tsne_0.1-3        
 [91] future.apply_1.6.0 modelr_0.1.8       crayon_1.3.4      
 [94] utf8_1.1.4         KernSmooth_2.23-17 plotly_4.9.2.1    
 [97] rmarkdown_2.3      grid_4.0.2         readxl_1.3.1      
[100] data.table_1.12.8  blob_1.2.1         git2r_0.27.1      
[103] reprex_0.3.0       digest_0.6.25      httpuv_1.5.4      
[106] munsell_0.5.0      viridisLite_0.3.0