I am a postdoc at 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 cancerogenesis.

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 the analysis of 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, please get in touch, as I am happy to consult on projects.

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

How to refer to columns when programming with dplyr.

Download statistics of my R packages.

Remove the stroke around points to map the size argument accurately.

Find the standard deviation of $g(X)$

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

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

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

Fit Gamma-Poisson Generalized Linear Models Reliably.

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

*
#### ggupset

#### lemur

#### pyLemur

#### transformGamPoi

#### einsum

#### glmGamPoi

#### sparseMatrixStats

#### proDA

#### ggupset

#### mixdir

#### tidygenomics

#### ggsignif

Bezier curves for ggplot2.

R package for “Latent Embedding Multivariate Regression”

Python package for “Latent Embedding Multivariate Regression”

Variance stabilizing transformation for Gamma Poisson distributed data

Einstein Summation for Arrays in R

Fit Gamma-Poisson Generalized Linear Models Reliably

Implementation of the `matrixStats`

API for sparse matrices

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

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

R package for clustering high dimensional categorical data

Tidy Verbs for Dealing with Genomic Data Frames

A `ggplot2`

extension to add significance brackets