Constantin Ahlmann-Eltze

@const-ae.bsky.social

2051 Followers 682 Following

Scientist at Isomorphic Labs. Previously at UCL and EMBL working with Wolfgang Huber. Biology, ML, Python, R, cancer immunology

Picture of myself in front of the Iso logo
Beeswarm plot of the prediction error across different methods of double perturbations showing that all methods (scGPT, scFoundation, UCE, scBERT, Geneformer, GEARS, and CPA) perform worse than the additive baseline.
wrap_in_tibble <- function(...){ dots <- rlang::enquos(...) # Let's assume I can be sure that the environment is the same across dots eval_in_mtcars(tibble::tibble(!!! dots), env = rlang::quo_get_env(dots[[1]])) } eval_in_mtcars <- function(expr, env){ quo <- rlang::enquo(expr) quo <- rlang::new_quosure(rlang::quo_squash(quo), env) rlang::eval_tidy(quo, data = mtcars[1:3,]) } wrap_in_tibble(mpg * 3) #> # A tibble: 3 × 1 #> `mpg * 3` #> <dbl> #> 1 63 #> 2 63 #> 3 68.4
wrap_in_tibble2 <- function(...){ dots <- rlang::enexprs(...) eval_in_mtcars(tibble::tibble(!!! dots)) } fnc <- function(){ a <- 700 wrap_in_tibble2(mpg * a, cyl + 3) } a <- 0 fnc() wrap_in_tibble2(mpg * a, cyl + 3)
wrap_in_df <- function(...){ dots <- rlang::enquos(...) eval_in_mtcars(data.frame(!!! dots)) } wrap_in_tibble <- function(...){ dots <- rlang::enquos(...) eval_in_mtcars(tibble::tibble(!!! dots)) } eval_in_mtcars <- function(expr){ quo <- rlang::enquo(expr) rlang::eval_tidy(quo, data = mtcars[1:3,]) } wrap_in_df(mpg * 2, cyl + 3) #> X.mpg...2 X.cyl...3 #> 1 42.0 9 #> 2 42.0 9 #> 3 45.6 7 wrap_in_tibble(mpg * 2, cyl + 3) #> Error: object 'mpg' not found
Plot produced with exactplot
Plot produced with exactplot that contains Latex labels and has embedded fonts.
Screenshot of the glioblastoma figure
Example of the differential expression neighborhood inference
Subspace alignment schematic
Schematic of a simplified model with 2 genes where we fit 1D subspaces.
R and Python code to call LEMUR
Overview of the LEMUR steps: (1) subspace alignment, (2) differential expression, (3) DE neighborhoods, (4) pseudobulking.
Beeswarm plot of the double perturbation predictions. The Additive model outperforms GEARS, scGPT, and scFoundation by a large margin
Short video of myself in front of the cancer institute
Code screenshot of ``` ggplot(palmerpenguins::penguins, aes(x = interaction(island, species), y = bill_length_mm)) + geom_boxplot() + ggh4x::scale_x_manual(values = c(1:3, 4.5, 6)) + guides(x = gguidance::guide_axis_nested(drop_zero = FALSE)) ``` plus the outcome where the x-axis is grouped by species
An example plot of the new {{ggbezier}} package
Hex sticker for the fullRankMatrix R package
Screenshots from figure 4
Screenshot from figure 3
Screenshot of a simple example of LEMUR
Step-by-step workflow of LEMUR
Happy me with a fun PhD hat just after my defense