Luke Zappia
Munich, Bavaria, Germany
Summary
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Rockstar🎓
Top SchoolLuke Zappia is a bioinformatician and data scientist with 11 years of experience applying machine learning to complex biological data, particularly single-cell RNA-seq, to drive target discovery and reproducible analysis workflows. He has a PhD in Bioinformatics and a strong track record of developing and optimizing core tools—contributing backend improvements to the widely used Seurat toolkit and authoring integration notebooks for theislab's sc-best-practices that showcase scVI/scANVI workflows. Comfortable bridging research and production, he focuses on high-quality software, memory and performance optimizations, and reproducible pipelines, and serves as a Bioconductor package reviewer and Carpentries instructor. Based in Munich, he blends deep academic insight from a postdoc at TUM with industry experience in immunotherapy target discovery, and brings the rare combination of hands-on algorithm development, open-source stewardship, and practical engineering for large single-cell datasets.
11 years of coding experience
7 years of employment as a software developer
The University of Melbourne
English, German
Stackoverflow
186reputation
13kreached
3answers
0questions
Github Skills (17)
notebook10
single-cell-genomics10
python10
r10
user-manual10
seq10
sc10
cell10
rna-seq10
data-integration10
bioinformatics9
data-analysis9
machine-learning8
cran6
ggplot6
Programming languages (17)
Github contributions (5)
theislab/single-cell-best-practices
Oct 2021 - Nov 2022
https://www.sc-best-practices.org
Role in this project:
Data Scientist
Data ScientistContributions:7 reviews, 31 commits, 1 PR in 1 year 1 month
Contributions summary:Luke added a comprehensive integration notebook focused on integrating single-cell RNA sequencing datasets, specifically using the scVI and scANVI methods. The notebook demonstrates the entire workflow, from data loading and pre-processing to variable gene selection, dimensionality reduction, and visualization. The user also includes sections on BBKNN and Seurat integration, providing a broad overview of integration techniques for single-cell data.
in-progresssingle-cellwork-in-progressrna-seq
satijalab/seurat
May 2018 - May 2018
R toolkit for single cell genomics
Role in this project:Back-end Developer
Back-end DeveloperContributions:8 commits, 1 PR, 15 comments in 1 day
Contributions summary:Luke primarily contributed to the Seurat R toolkit by implementing new functionalities and improving existing ones. They added features such as reading data from HDF5 files, variable gene selection options, and a function to rename cells. The user also worked on optimizing memory usage and fixing existing bugs, demonstrating an understanding of the internal workings of the toolkit. Their changes involved modifications to core processing and interaction components.
single-cell-genomicshuman-cell-atlassingle-cell-rna-seqcran
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