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Reposted by Constantin Ahlmann-Eltze

Junyan Lu @junyanlu.bsky.social · 1d

🚀 Excited to share our new preprint: msBayesImpute - A Versatile Framework for Addressing Missing Values in Biomedical Mass Spectrometry Proteomics Data
👉 Improves imputation accuracy, normalization, and differential expression detection
📝https://www.biorxiv.org/content/10.1101/2025.10.02.679746v1

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Reposted by Constantin Ahlmann-Eltze

James Bonfield @jbonfield.bsky.social · 23d

Heads up: ignore samtools dot org, similarly minimap2 dot com and likely others. It's owned by a known phishing site and while the binaries they offer look valid currently (but note they may be serving us different binaries to others), that could change.

Ie: it's not us (Samtools team)! Be warned

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Reposted by Constantin Ahlmann-Eltze

Louise Deconinck @louisedck.bsky.social · Aug 25

We're excited to share that our preprint on anndataR, a new package bringing Python's AnnData to R, is now available on bioRxiv 🎉

🔗 Read the paper: www.biorxiv.org/content/10.1...
💻 Check the package in action: anndatar.data-intuitive.com

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Reposted by Constantin Ahlmann-Eltze

W. Joel Schneider @w-joel-schneider.bsky.social · Aug 20

Now on CRAN, ggdiagram is a #ggplot2 extension that draws diagrams programmatically in #Rstats. Allows for precise control in how objects, labels, and equations are placed in relation to each other.
wjschne.github.io/ggdiagram/ar...

Trigonometric functions and a unit circle

A bivariate change model with structured residuals

A hierarchical model of cognitive abilities

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Constantin Ahlmann-Eltze @const-ae.bsky.social · Aug 10

Makes sense. I imagine this would simply be my primary use case, and I would prefer not having to refer to `x` twice. Something like:

replace_values <- function(x, ..., from=NULL, to=NULL, lookup=NULL){
if(! is.null(lookup)){
from <- names(lookup)
to <- lookup
}
...
}

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Constantin Ahlmann-Eltze @const-ae.bsky.social · Aug 10

This looks great! I think it would be neat if the functions also supported named look-up vectors in addition to the `to` and `from` arguments :)

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Reposted by Constantin Ahlmann-Eltze

Constantin Ahlmann-Eltze @const-ae.bsky.social · Aug 4

Our paper benchmarking foundation models for perturbation effect prediction is finally published 🎉🥳🎉

www.nature.com/articles/s41...

We show that none of the available* models outperform simple linear baselines. Since the original preprint, we added more methods, metrics, and prettier figures!

🧵

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.

Line plot of the true positive rate against the false discovery proportion showing that none of the methods is better at finding non additive interactions than simply predicting no change.

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Reposted by Constantin Ahlmann-Eltze

James Reading @jamesreading.bsky.social · Aug 5

🚨 PhD Position available in our lab 🚨 exploring the power of blood immune multi-omics to detect lung cancer years prior to clinical diagnosis in a unique cohort of >10,000 CT screened individuals.
✅ Wet & dry lab
✅ September 2025 enrolment
✅ UK tuition fees only

www.ucl.ac.uk/medical-scie...

Pre-Cancer Immunology

The Pre-Cancer Immunology Lab (James Reading Lab) is mapping pre-invasive T cell dynamics during carcinogenesis to detect and intercept cancer development.

www.ucl.ac.uk

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Reposted by Constantin Ahlmann-Eltze

Carl Zimmer @carlzimmer.com · Aug 4

I wrote about AI foundation models for biology last year: www.nytimes.com/2024/03/10/s... Benchmarking tests since then aren't finding that they're better than simpler models of how genes and cells work.

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Reposted by Constantin Ahlmann-Eltze

Nature Methods @natmethods.nature.com · Aug 4

An analysis shows that current deep learning models do not beat linear baselines in predicting gene perturbation effects, thus emphasizing the importance of further method development and evaluation. @const-ae.bsky.social @wkhuber.bsky.social @s-anders.bsky.social

www.nature.com/articles/s41...

Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines - Nature Methods

The analysis presented in this Brief Communication shows that, despite their complexity, current deep learning models do not outperform linear baselines in predicting gene perturbation effects, thus e...

www.nature.com

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Constantin Ahlmann-Eltze @const-ae.bsky.social · Aug 4

Haha, would also be a succinct summary of most of my academic work 😅

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Constantin Ahlmann-Eltze @const-ae.bsky.social · Aug 4

And lastly, a big shout-out to @wkhuber.bsky.social and @s-anders.bsky.social!

Link to pdf: rdcu.be/ey7x0

Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines

Nature Methods - The analysis presented in this Brief Communication shows that, despite their complexity, current deep learning models do not outperform linear baselines in predicting gene...

rdcu.be

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Constantin Ahlmann-Eltze @const-ae.bsky.social · Aug 4

- Li et al. doi.org/10.1101/2024.12.23.630036
- Chen Li et al. doi.org/10.1101/2024.12.20.629581
- Wong et al. doi.org/10.1093/bioinformatics/btaf317

And probably many more that I am missing here.

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Constantin Ahlmann-Eltze @const-ae.bsky.social · Aug 4

- @kasparmartens.bsky.social et al. openreview.net/forum?id=eb3ndUlkt4
- Gaudelet et al. doi.org/10.48550/arXiv.2404.16907
- @aaronwtr.bsky.social et al. openreview.net/forum?id=t04D9bkKUq
- Bendidi et al. doi.org/10.48550/arXiv.2410.13956
- Wu et al. doi.org/10.48550/arXiv.2408.10609

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Constantin Ahlmann-Eltze @const-ae.bsky.social · Aug 4

I also encourage everyone to checkout the parallel efforts by groups around the world who came to similar conclusions:
- @ekernf01.bsky.social et al. doi.org/10.1101/2023.07.28.551039
- Csendes et al. doi.org/10.1186/s12864-025-11600-2
- @kasia.codes et al. doi.org/10.1186/s13059-025-03574-x

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Constantin Ahlmann-Eltze @const-ae.bsky.social · Aug 4

*We benchmarked scGPT, scFoundation, GEARS, CPA (which claim predictive ability), and scBERT, Geneformer, and UCE (which do not claim this ability). I can't comment on methods released in the last 5 months. For a summary of recent developments, see @ekernf01.bsky.social's post

A recap of virtual cell releases circa June 2025

In October 2024, I twote that “something is deeply wrong” with what we now call virtual cell models. A lot has happened since then: modelers are advancing new architectures and mining new sources of i...

ekernf01.github.io

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Constantin Ahlmann-Eltze @const-ae.bsky.social · Aug 4

Our paper benchmarking foundation models for perturbation effect prediction is finally published 🎉🥳🎉

www.nature.com/articles/s41...

We show that none of the available* models outperform simple linear baselines. Since the original preprint, we added more methods, metrics, and prettier figures!

🧵

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Reposted by Constantin Ahlmann-Eltze

Eric Kernfeld @ekernf01.bsky.social · Jul 27

In October 2024, I twote that "something is deeply wrong" with what we now call virtual cell models. A lot has happened since then. How am I updating? New blog post: ekernf01.github.io/virtual-cell...

A recap of virtual cell releases circa June 2025

In October 2024, I twote that “something is deeply wrong” with what we now call virtual cell models. A lot has happened since then: modelers are advancing new architectures and mining new sources of i...

ekernf01.github.io

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Constantin Ahlmann-Eltze @const-ae.bsky.social · Jul 3

That's amazing. Congratulations 🎉

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Reposted by Constantin Ahlmann-Eltze

Helena Lucia Crowell @helucro.bsky.social · Jun 27

Going from methods dev to full on cancer bio has been tough. All the more excited to see this out at last—

www.biorxiv.org/content/10.1...

@pascual-reguant.bsky.social
@brukerspatial.bsky.social
@hoheyn.bsky.social

Tracing colorectal malignancy transformation from cell to tissue scale

The transformation of normal intestinal epithelium into colorectal cancer (CRC) involves coordinated changes across molecular, cellular, and architectural scales; yet, how these layers integrate remai...

www.biorxiv.org

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Reposted by Constantin Ahlmann-Eltze

We Are R-Ladies @weare.rladies.org · May 8

With {tidylog}, you see friendly messages like:

summarise: now 3 rows and 3 columns, ungrouped

filter: removed 2 rows (67%), one row remaining

✨ It’s like having a gentle co-pilot, helping you track what each step is doing — super useful for debugging and learning!

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Reposted by Constantin Ahlmann-Eltze

Matthias Meyer-Bender @matthiasmeybe.bsky.social · May 5

New preprint out!

We introduce 𝐬𝐩𝐚𝐭𝐢𝐚𝐥𝐩𝐫𝐨𝐭𝐞𝐨𝐦𝐢𝐜𝐬, a Python package for end-to-end processing and analysis of highly multiplexed immunofluorescence imaging data.

Built on xarray and dask, with seamless integration into the scverse ecosystem.
www.biorxiv.org/content/10.1...

Spatialproteomics orchestrates workflows to analyze highly multiplexed images. It segments cells, processes images, quantifies proteins, predicts cell types, and provides neighborhood analysis methods, all while integrating into the scverse ecosystem.

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Reposted by Constantin Ahlmann-Eltze

T cell Vaccines Lab (Swadling UCL) 🧬👩🏽‍🔬 @tcellvaccineucl.bsky.social · Apr 10

(2/3)

Constantin Ahlmann-Eltze showcased impressive work on assembling an atlas of T cells in precancerous samples. They developed the R packages treelabel and Shinytreelabel, which showed GITR+ Tregs are enriched in several precancerous samples, suggesting avenues for future treatments

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Constantin Ahlmann-Eltze @const-ae.bsky.social · Mar 21

A friend of mine recommends the Bahnstadt Edeka Fleischtheke. (Vegetarian myself, so no first-hand experience 😅)

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Reposted by Constantin Ahlmann-Eltze

Lorenz Adlung Lab @adlunglab.com · Mar 19

🦎 Differential expression analysis relies on cluster identities, unless you perform a regression along continuous representation in latent space. LEMUR from @const-ae.bsky.social & @wkhuber.bsky.social does that neatly for scRNA-seq data!
🔗 doi.org/10.1038/s415... 🧪 @naturegenet.bsky.social

Analysis of multi-condition single-cell data with latent embedding multivariate regression

Nature Genetics - Latent embedding multivariate regression models multi-condition single-cell RNA-seq using a continuous latent space, enabling data integration, per-cell gene expression prediction...

doi.org

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