From Proteins and Molecules to Cats and Dogs: Visualization as Scalable Minimum Spanning Trees
- 1Department of Chemistry, Biochemistry and Pharmaceutical Sciences, University of Bern, Freiestrasse 3, Bern, Switzerland, 3012
- 2Bioinformatics Group, Wageningen University, Droevendaalsesteeg, Wageningen, Netherlands, 6708 PB
Abstract
Dimensionality reduction and data visualization are essential for analyzing highdimensional datasets, yet traditional projection methods obscure local structural relationships. We present TMAP2, a scalable rewrite of TMAP that maps highdimensional data as a minimum spanning tree. TMAP2 accepts diverse input types, including continuous embeddings, sparse fingerprints, and precomputed graphs, and applies to single-cell, image, chemistry, or protein-language-model data. Every line on the map is therefore a real nearest-neighbor relationship, and exactly one path joins any two points. The number of edges along that path, its hop count, works as a coordinate along a progression: it recovers a pancreatic cell’s developmental age (Spearman 0.90), tracks the collection date of a SARS-CoV-2 spike sequence built from raw sequence alone (Spearman 0.86), and traces a smooth morph from a Persian cat to a Yorkshire terrier that crosses species at a single edge. On 3.6 million CAZyme sequences the same tree separates taxonomic domains and, within the family GH43, sorts sequences by catalytic activity, revealing an anchor enzyme annotated as one activity sitting among a cluster of another. TMAP2 extends the original TMAP from fingerprints to any embedding, at up to one million points with 95–99% recall.
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BibTeX
@misc{sepulveda2026from,
title = {{From Proteins and Molecules to Cats and Dogs: Visualization as Scalable Minimum Spanning Trees}},
author = {Alejandro Flores Sepúlveda and Maarten Boneschansker and Daniel Probst and Jean-Louis Reymond},
howpublished = {ChemRxiv},
year = {2026},
month = sep,
doi = {10.26434/chemrxiv.15008307/v1},
url = {https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008307/v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - From Proteins and Molecules to Cats and Dogs: Visualization as Scalable Minimum Spanning Trees
AU - Alejandro Flores Sepúlveda
AU - Maarten Boneschansker
AU - Daniel Probst
AU - Jean-Louis Reymond
PY - 2026
DA - 2026/09/04
PB - ChemRxiv
DO - 10.26434/chemrxiv.15008307/v1
UR - https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008307/v1
AB - Dimensionality reduction and data visualization are essential for analyzing highdimensional datasets, yet traditional projection methods obscure local structural relationships. We present TMAP2, a scalable rewrite of TMAP that maps highdimensional data as a minimum spanning tree. TMAP2 accepts diverse input types, including continuous embeddings, sparse fingerprints, and precomputed graphs, and applies to single-cell, image, chemistry, or protein-language-model data. Every line on the map is therefore a real nearest-neighbor relationship, and exactly one path joins any two points. The number of edges along that path, its hop count, works as a coordinate along a progression: it recovers a pancreatic cell’s developmental age (Spearman 0.90), tracks the collection date of a SARS-CoV-2 spike sequence built from raw sequence alone (Spearman 0.86), and traces a smooth morph from a Persian cat to a Yorkshire terrier that crosses species at a single edge. On 3.6 million CAZyme sequences the same tree separates taxonomic domains and, within the family GH43, sorts sequences by catalytic activity, revealing an anchor enzyme annotated as one activity sitting among a cluster of another. TMAP2 extends the original TMAP from fingerprints to any embedding, at up to one million points with 95–99% recall.
ER -