The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
library(dplyr)
library(ggplot2)
library(recipes)
library(scimo)
theme_set(theme_light())
data("pedcan_expression")
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>, …
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 %>%
juice() %>%
ggplot() +
aes(x = PC1, y = PC2, color = disease) +
geom_point()
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 %>%
juice() %>%
ggplot() +
aes(x = PC1, y = PC2, color = disease) +
geom_point()
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_6wLkC
#> 2 A1CF 4.60 TRUE select_cv_6wLkC
#> 3 A2M 1.69 TRUE select_cv_6wLkC
#> 4 A2ML1 2.45 TRUE select_cv_6wLkC
#> 5 A3GALT2 2.37 TRUE select_cv_6wLkC
#> 6 A4GALT 0.979 FALSE select_cv_6wLkC
#> 7 A4GNT 1.53 FALSE select_cv_6wLkC
#> 8 AAAS 0.0934 FALSE select_cv_6wLkC
#> 9 AACS 0.194 FALSE select_cv_6wLkC
#> 10 AADAC 3.40 TRUE select_cv_6wLkC
#> # ℹ 19,183 more rows
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.