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Pediatric cancers

library(dplyr)
library(ggplot2)
library(recipes)
library(scimo)

theme_set(theme_light())

data("pedcan_expression")

pedcan.R

Dataset

pedcan_expression contains the expression of 108 cell lines from 5 different pediatric cancers. Additionally, it includes information on the sex of the original donor, the type of cancer it represents, and whether it is a primary tumor or a metastasis.

pedcan_expression
#> # A tibble: 108 × 19,197
#>    cell_line sex   event disease  A1BG   A1CF   A2M  A2ML1 A3GALT2 A4GALT  A4GNT
#>    <chr>     <chr> <chr> <chr>   <dbl>  <dbl> <dbl>  <dbl>   <dbl>  <dbl>  <dbl>
#>  1 143B      Fema… Prim… Osteos…  3.02 0.0566 2.78  0       0       2.13  0     
#>  2 A-673     Fema… Prim… Ewing …  4.87 0      2.00  3.19    0.0841  4.62  0.189 
#>  3 BT-12     Fema… Prim… Embryo…  3.52 0.0286 0.111 0       0       2.32  0.0704
#>  4 BT-16     Male  Unkn… Embryo…  3.51 0      0.433 0.0144  0       1.54  0.0144
#>  5 C396      Male  Meta… Osteos…  4.59 0      0.956 0       0       5.10  0     
#>  6 CADO-ES1  Fema… Meta… Ewing …  5.89 0      0.614 0.379   0.0704  6.60  0.151 
#>  7 CAL-72    Male  Prim… Osteos…  4.35 0.0426 0.333 0       0       0.614 0     
#>  8 CBAGPN    Fema… Prim… Ewing …  4.87 0.0976 1.33  0.111   0       0.722 0.0704
#>  9 CHLA-06   Fema… Unkn… Embryo…  5.05 0      0.124 0       0       0.848 0.138 
#> 10 CHLA-10   Fema… Unkn… Ewing …  5.05 0.0144 0.949 1.73    0.0704  0.506 0.0704
#> # ℹ 98 more rows
#> # ℹ 19,186 more variables: AAAS <dbl>, AACS <dbl>, AADAC <dbl>, AADACL2 <dbl>,
#> #   AADACL3 <dbl>, AADACL4 <dbl>, AADAT <dbl>, AAGAB <dbl>, AAK1 <dbl>,
#> #   AAMDC <dbl>, AAMP <dbl>, AANAT <dbl>, AAR2 <dbl>, AARD <dbl>, AARS1 <dbl>,
#> #   AARS2 <dbl>, AARSD1 <dbl>, AASDH <dbl>, AASDHPPT <dbl>, AASS <dbl>,
#> #   AATF <dbl>, AATK <dbl>, ABAT <dbl>, ABCA1 <dbl>, ABCA10 <dbl>,
#> #   ABCA12 <dbl>, ABCA13 <dbl>, ABCA2 <dbl>, ABCA3 <dbl>, ABCA4 <dbl>, …

pedcan.R

count(pedcan_expression, disease, sort = TRUE)
#> # A tibble: 5 × 2
#>   disease              n
#>   <chr>            <int>
#> 1 Neuroblastoma       33
#> 2 Ewing Sarcoma       22
#> 3 Rhabdomyosarcoma    20
#> 4 Embryonal Tumor     17
#> 5 Osteosarcoma        16

pedcan.R

Dimension reduction

One approach to exploring this dataset is by performing PCA.

rec_naive_pca <-
  recipe(pedcan_expression) %>% 
  update_role(-cell_line) %>% 
  step_zv(all_numeric_predictors()) %>% 
  step_normalize(all_numeric_predictors()) %>% 
  step_pca(all_numeric_predictors()) %>% 
  prep()

rec_naive_pca %>% 
  bake(new_data = NULL) %>% 
  ggplot() +
  aes(x = PC1, y = PC2, color = disease) +
  geom_point()

pedcan.R

To improve the appearance of PCA, one can precede it with a feature selection step based on the coefficient of variation. Here, step_select_cv keeps only one fourth of the original features.

rec_cv_pca <-
  recipe(pedcan_expression) %>% 
  update_role(-cell_line) %>% 
  step_select_cv(all_numeric_predictors(), prop_kept = 1/4) %>% 
  step_normalize(all_numeric_predictors()) %>%
  step_pca(all_numeric_predictors()) %>%
  prep()

rec_cv_pca %>% 
  bake(new_data = NULL) %>% 
  ggplot() +
  aes(x = PC1, y = PC2, color = disease) +
  geom_point()

pedcan.R

The tidy method allows to see which features are kept.

tidy(rec_cv_pca, 1)
#> # A tibble: 19,193 × 4
#>    terms       cv kept  id             
#>    <chr>    <dbl> <lgl> <chr>          
#>  1 A1BG    0.371  FALSE select_cv_5bV4K
#>  2 A1CF    4.60   TRUE  select_cv_5bV4K
#>  3 A2M     1.69   TRUE  select_cv_5bV4K
#>  4 A2ML1   2.45   TRUE  select_cv_5bV4K
#>  5 A3GALT2 2.37   TRUE  select_cv_5bV4K
#>  6 A4GALT  0.979  FALSE select_cv_5bV4K
#>  7 A4GNT   1.53   FALSE select_cv_5bV4K
#>  8 AAAS    0.0934 FALSE select_cv_5bV4K
#>  9 AACS    0.194  FALSE select_cv_5bV4K
#> 10 AADAC   3.40   TRUE  select_cv_5bV4K
#> # ℹ 19,183 more rows

pedcan.R

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They may not be fully stable and should be used with caution. We make no claims about them.