Author

Dennis J. Montoya

University of California, Los AngelesORCID 0000-0002-0802-3309Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

Click to filter author papers

Bioinformatics· 2 Genetics· 1

Signature Assays, Organisms & Methods

Click to filter · or open tag

Organism:human· 3 Approach:statistical model· 3 Assay:bulk RNA-seq· 2 Research Focus:deconvolution· 2 Approach:benchmark study· 1 Research Focus:benchmarking· 1 Research Focus:cell type annotation· 1 Research Focus:differential expression· 1

Frequent Co-Authors

Matteo A. PellegriniiD· 3 shared Brian B. NadeliD· 2 shared Feiyang MaiD· 2 shared Serghei ManguliD· 2 shared Aldons Jake LusisiD· 1 shared Alice MoutoniD· 1 shared Arturo Ottavio RinaldiiD· 1 shared Barbara E. StrangeriD· 1 shared

Activity by Year

2018-2021 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 27 mutual co-author links

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

3 shared papersBioinformatics

Matteo A. PellegriniORCID

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Systematic evaluation of transcriptomics-based deconvolution methods and references using thousands of clinical samples

Brian B. Nadel, Meritxell Oliva, Benjamin L. Shou, Keith Mitchell, Feiyang Ma, Dennis J. Montoya, Alice Mouton, Sarah Kim-Hellmuth, Barbara E. Stranger, Matteo A. Pellegrini, Serghei Mangul

This study evaluates ten deconvolution tools on 4,000 clinical and synthetic samples, finding EPIC performs best on Framingham data and HPCA references are generally accurate.

Briefings in Bioinformatics · Jun 25, 2021Peer-reviewedBioinformatics

The Gene Expression Deconvolution Interactive Tool (GEDIT): Accurate Cell Type Quantification from Gene Expression Data

Brian B. Nadel, David Lopez, Dennis J. Montoya, Feiyang Ma, Hannah B. Waddel, Misha M. Khan, Serghei Mangul, Matteo A. Pellegrini

GEDIT estimates cell type abundances from gene expression data, outperforming existing tools across multiple platforms and tissue types.

GigaScience · Jan 29, 2021Peer-reviewedBioinformatics

Epigenome-wide association in adipose tissue from the METSIM cohort

Luz D. Orozco, Colin P. Farrell, Christopher J. Hale, Liudmilla Rubbi, Arturo Ottavio Rinaldi, Mete Civelek, Calvin Pan, Larry Lam, Dennis J. Montoya, Chantle R. Edillor, Marcus Michael Seldin, Michael Boehnke, Karen L. Mohlke, Steven E. Jacobsen, Johanna Kuusisto, , Matteo A. Pellegrini

The paper identifies DNA methylation associations with metabolic syndrome traits in adipose tissue and develops a biomarker for type II diabetes risk.

Human Molecular Genetics · Mar 16, 2018Peer-reviewedGenetics