subset-and-reorder

Single variable (default)

For most survival analysis, a simple, one-variable formula is used. Call: Surv(day, status) ~ condition

p <- run_bulksurv(sample_data)
#> Joining with `by = join_by(x, condition, day, sex, status)`
#> call: formula = Surv(day, status) ~ condition

Subsetting one-variable formulae

To compare fewer conditions, change the sample_order parameter.

Here, we subset AND invert the order:

p <- run_bulksurv(sample_data, 
                  sample_order = c("Drug2", "WT"), # Compare WT vs Drug2, invert order
                  print_stats = FALSE)
#> Joining with `by = join_by(x, condition, day, sex, status)`
#> call: formula = Surv(day, status) ~ condition

Note: sample_order only works for reordering the condition column. If you have a more complex design, an error message will be shown.

More than 1 variable (complex)

What happens when we want to model the effect of condition (drug) and another variable, such as sex?

Let’s take a look at sample_data”

head(sample_data)
#>   X condition day dead censored sex
#> 1 1        WT   0    0        0   M
#> 2 2        WT   1    2        1   M
#> 3 3        WT   2    1        0   M
#> 4 4        WT   3    1        0   M
#> 5 5        WT   4    3        0   M
#> 6 6        WT   5    1        0   M

Fitting a custom formula

Use the formula parameter to specify a custom survival formula.

p <- run_bulksurv(sample_data, 
                  formula = "Surv(day, status) ~ condition + sex")
#> Joining with `by = join_by(x, condition, day, sex, status)`
#> call: formula = Surv(day, status) ~ condition + sex

Customizing colors and linetypes

We can use the palette parameter to specify custom colors:

p <- run_bulksurv(sample_data, 
                  formula = "Surv(day, status) ~ condition + sex", 
                  # Specify colors
                  palette = c("lightgreen", "forestgreen", 
                            "lightblue", "blue", 
                            "pink", "firebrick"))
#> Joining with `by = join_by(x, condition, day, sex, status)`
#> call: formula = Surv(day, status) ~ condition + sex

We can further customize colors by groups in the data. Here, we color by condition and split linetypes by sex.

p <- run_bulksurv(sample_data, 
                  formula = "Surv(day, status) ~ condition + sex", 
                  color = "condition", 
                  linetype = "sex", 
                  palette = c("red", "blue", "forestgreen"))
#> Joining with `by = join_by(x, condition, day, sex, status)`
#> call: formula = Surv(day, status) ~ condition + sex