Constantin Ahlmann-Eltze

Postdoc

UCL London

Biography

I am a postdoc at the Cancer Institute at UCL in the group of James Reading. I did my PhD with Wolfgang Huber at EMBL Heidelberg. Before joining UCL, I did a bridging postdoc with Simon Anders at Heidelberg University.

Currently, I am working on novel ways to analyze the role of T cells in early carcinogenesis.

Previously, I have worked on:

Statistical methods to simplify working with single-cell data,
Benchmarks of single-cell methods,
Differential abundance analysis of mass spectrometry data,
Clustering of high-dimensional categorical data.

For a full record of my publications, see Google Scholar.

In addition, I develop statistical methods and tools for analyzing cutting-edge biological data. I maintain ten R packages, which are published on CRAN and Bioconductor and total more than 100,000 downloads per month. In 2023, I received the Bioconductor Community Award in recognition of my contributions to the project.

If you are working in a company and would like help with some analysis or one of my packages, don’t hesitate to get in touch, as I am happy to consult on projects.

Recent Posts

LEMUR simplified

A simplified implementation of the LEMUR algorithm.

Jan 5, 2025 9 min read

D3 (and other javascript) in R

Leave abandoned wrapper packages behinds and do it yourself

Dec 2, 2024 5 min read

Latex to Word conversion with pandoc

Converting arbitrary Latex documents to Word documents without errors is probably impossible. However, pandoc can get you surprisingly …

Last updated on Aug 2, 2024 4 min read

tidyselect and tidyeval cheat sheet

How to refer to columns when programming with dplyr.

Oct 23, 2023 3 min read

My R package downloads

Download statistics of my R packages.

Sep 10, 2023 1 min read

Recent Publications

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

A new method to analyze multi-condition single-cell data without clustering.

Constantin Ahlmann-Eltze, Wolfgang Huber (2025). Nat. Genet.

PDF Project DOI R package Python package Reproducibility Github

Deep learning-based predictions of gene perturbation effects do not yet outperform simple linear methods

Comparison of foundation models for genetic perturbation prediction against linear models.

Constantin Ahlmann-Eltze, Wolfgang Huber, Simon Anders (2024). bioR

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PDF DOI Reproducibility Github

Comparison of transformations for single-cell RNA-seq data

Theoretical and empirical analysis of transformation methods for single-cell data.

Constantin Ahlmann-Eltze, Wolfgang Huber (2023). Nat. Methods

PDF Project DOI Reproducibility R package Github Shiny App

glmGamPoi: fitting Gamma-Poisson generalized linear models on single cell count data

Fit Gamma-Poisson Generalized Linear Models Reliably.

Constantin Ahlmann-Eltze, Wolfgang Huber (2020). Bioinformatics

PDF Project DOI Reproducibility R package Github

proDA: Probabilistic Dropout Analysis for Identifying Differentially Abundant Proteins in Label-Free Mass Spectrometry

Decide which proteins are differentially abundant in label-free mass spectormetry without imputation.

Constantin Ahlmann-Eltze, Simon Anders (2019). bioR

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PDF Project DOI Reproducibility R package Github

See all publications

Software

ggupset

Bezier curves for ggplot2.

lemur

R package for “Latent Embedding Multivariate Regression”

pyLemur

Python package for “Latent Embedding Multivariate Regression”

transformGamPoi

Variance stabilizing transformation for Gamma Poisson distributed data

einsum

Einstein Summation for Arrays in R

glmGamPoi

Fit Gamma-Poisson Generalized Linear Models Reliably

sparseMatrixStats

Implementation of the matrixStats API for sparse matrices

proDA

R package for “Protein Differential Abundance Analysis for Label-Free Mass Spectrometry Data”

ggupset

Plot a combination matrix instead of the standard x-axis and create UpSet plots with ggplot2.

mixdir

R package for clustering high dimensional categorical data

tidygenomics

Tidy Verbs for Dealing with Genomic Data Frames

ggsignif

A ggplot2 extension to add significance brackets