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Sebastian I Zúñiga Norman

dblp:437/1003 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2026
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › gene regulation › gene regulatory network
gene regulatory network analysis
1.012026
REACTOR: REgulon Activity analysis and Comparison Tool for single-cell transcriptOmics Research · Bioinform. 2026
Bioinformatics and computational biology › single-cell analysis
single-cell RNA sequencing
1.012026
REACTOR: REgulon Activity analysis and Comparison Tool for single-cell transcriptOmics Research · Bioinform. 2026
Bioinformatics and computational biology
transcriptomics
1.012026
REACTOR: REgulon Activity analysis and Comparison Tool for single-cell transcriptOmics Research · Bioinform. 2026

Methods — techniques the papers use, named apart from their topics

statistical test · 1.0
YearPublicationVenuePosition
2026 REACTOR: REgulon Activity analysis and Comparison Tool for single-cell transcriptOmics Research
abstract
SUMMARY: We introduce REACTOR, a computational tool designed to detect differential activity of transcriptional regulators and their target genes (regulons) in single-cell RNA-sequencing data. It expands the currently available framework for regulon analysis by introducing a robust statistical test to detect differential regulon activity between conditions, such as disease versus control, with multiple replicates. By contrasting different conditions, REACTOR enables identification of key condition- and cell type-specific regulons. To demonstrate the use of REACTOR, we illustrate its performance in a publicly available COVID-19 dataset. AVAILABILITY: REACTOR R-package together with an implementation vignette are available at https://www.github.com/elolab/REACTOR.
Markus Lindén, Sebastian I Zúñiga Norman, Tommi Välikangas, Sini Junttila, Tomi Suomi, Kalle T. Rytkönen, Laura Elo
Bioinform.2