Albert Dominguez Mantes

PhD Student

École Polytechnique Fédérale de Lausanne (EPFL)

Hey there! I’m a PhD student in the Weigert group and the La Manno group at EPFL in Switzerland. I hold a BSc in Data Science and Engineering by the Universitat Politècnica de Catalunya (UPC), and I’m currently enroled in the Computational Biology PhD program at EPFL.

I’m developing computational methods for the analysis of fluorescence microscopy images. One of my main focus is on developing methods that are easily adoptable for other researchers. As such, I’m big on open source and best software engineering practices.

Previously, I worked on ML-based methods for population genetics in the Bustamante lab at Stanford University, in collaboration with the Image Processing group at Universitat Politècnica de Catalunya (UPC).

Outside of academia, I spent two great years working as a Data Scientist at a startup, Abi Global Health, where I developed and deployed NLP models for usage in a conversational setting. I was also involved in maintaining the main product’s backend as well as general analytics.

Please reach out if you’re interested in my work or just want to chat about any of these topics!

selected publications

Neural ADMIXTURE for rapid genomic clustering

Albert Dominguez Mantes , Daniel Mas Montserrat , Carlos D. Bustamante , and 2 more authors

Nature Computational Science, 2023

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Characterizing the genetic structure of large cohorts has become increasingly important as genetic studies extend to massive, increasingly diverse biobanks. Popular methods decompose individual genomes into fractional cluster assignments with each cluster representing a vector of DNA variant frequencies. However, with rapidly increasing biobank sizes, these methods have become computationally intractable. Here we present Neural ADMIXTURE, a neural network autoencoder that follows the same modeling assumptions as the current standard algorithm, ADMIXTURE, while reducing the compute time by orders of magnitude surpassing even the fastest alternatives. One month of continuous compute using ADMIXTURE can be reduced to just hours with Neural ADMIXTURE. A multi-head approach allows Neural ADMIXTURE to offer even further acceleration by computing multiple cluster numbers in a single run. Furthermore, the models can be stored, allowing cluster assignment to be performed on new data in linear time without needing to share the training samples.
Spotiflow: accurate and efficient spot detection for imaging-based spatial transcriptomics with stereographic flow regression

Albert Dominguez Mantes , Antonio Herrera , Irina Khven , and 7 more authors

bioRxiv, 2024

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The identification of spot-like structures in large and noisy microscopy images is an important task in many life science techniques, and it is essential to their quantitative performance. For example, imaging-based spatial transcriptomics (iST) methods rely critically on the accurate detection of millions of transcripts in low signal-to-noise ratio (SNR) images. While recent developments in computer vision have revolutionized many bioimage tasks, currently adopted spot detection approaches for iST still rely on classical signal processing methods that are fragile and require manually tuning. In this work we introduce Spotiflow, a deep-learning method that casts the spot-detection problem as a multiscale stereographic flow regression problem that yields subpixel-accurate localizations. Spotiflow is robust to different noise conditions and generalizes across different chemistries while being up to an order-of-magnitude more time and memory efficient than commonly used methods. We show the efficacy of Spotiflow by comprehensive quantitative comparisons against other methods on a variety of datasets and demonstrate the impact of its increased accuracy on the biological conclusions drawn from iST and live imaging experiments. Spotiflow is available as an easy-to-use Python library as well as a napari plugin at http://www.github.com/weigertlab/spotiflow.