Author

Victor E. Nieto-Caballero

Princeton UniversityORCID 0000-0002-5217-0330Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Approach:statistical model· 2 Research Focus:benchmarking· 1 Research Focus:cell type annotation· 1 Assay:ChIP-seq· 1 Approach:experimental assay· 1 Research Focus:gene regulatory network inference· 1 Organism:mouse· 1 Research Focus:spatial domain detection· 1

Frequent Co-Authors

Augustin RuiziD· 1 shared Benjamin J. RaphaeliD· 1 shared Boudewijn P. F. LelieveldtiD· 1 shared Brian LongiD· 1 shared Chang LiiD· 1 shared Christopher CardenasiD· 1 shared Delissa McMilleniD· 1 shared Diego A. Camacho-Hernández· 1 shared

Activity by Year

2021-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 36 mutual co-author links

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1 shared paperNeuroscience

Augustin RuizORCID

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2 papers

Recent·Most cited

NeurosciencebioRxivPreprintNov 22, 2025

Spatial patterning of transcriptional and regulatory programs in the primate subcortex

Shu Dan, Meghan A. Turner, Michael DeBerardine…·Fenna M. Krienen

The paper presents a spatially resolved transcriptomic and gene regulatory atlas of primate subcortical regions, revealing spatially tuned transcriptional and regulatory programs across cell types.

2 citations

mousespatial transcriptomics

𝕏 PostBluesky

BioinformaticsApplied SciencesPeer-reviewedJun 28, 2021

Partition Quantitative Assessment (PQA): A quantitative methodology to assess the embedded noise in clustered omics and systems biology data

Diego A. Camacho-Hernández, Victor E. Nieto-Caballero, José E. León-Burguete, Julio Augusto Freyre-González

The paper introduces a method to quantify noise in clustered biological data using autocorrelation.

DOI 10.3390/app11135999

statistical modelbenchmarking

𝕏 PostBluesky