Catalina A. Vallejos
Molecular Biology · 2017–2020
A researcher in the Science corpus: 2 papers; trusts 3 researchers and is trusted by 2 researchers here, as inferred from the citation graph.
Subfields
- Molecular Biology — 140 citations into it; #13,318 of 17,773 by trust here, #9,476 by citations
- Cancer Research — 38 citations into it; #1,367 of 1,780 by trust here, #1,332 by citations
Whom Catalina A. Vallejos trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Mark D. Robinson
- Dominic Grün
- Aaron T. L. Lun
- James H. Bullard
- Ali Mortazavi
- Laleh Haghverdi
- Xiaojie Qiu
- Allon M. Klein
- Alicia Oshlack
- Alexander van Oudenaarden
Who trusts Catalina A. Vallejos overall
Papers from Catalina A. Vallejos's perspective
Ordered by how strongly the researchers Catalina A. Vallejos trusts endorse each paper. Same corpus, a different order for every researcher.
- Mapping and quantifying mammalian transcriptomes by RNA-Seq (Nature Methods)
- Accounting for technical noise in single-cell RNA-seq experiments (Nature Methods)
- A scaling normalization method for differential expression analysis of RNA-seq data (Genome Biology)
- Normalization of RNA-seq data using factor analysis of control genes or samples (Nature Biotechnology)
- MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data (Genome Biology)
- Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments (BMC Bioinformatics)
- Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex (Nature Biotechnology)
- svaseq: removing batch effects and other unwanted noise from sequencing data (bioRxiv)
- Splatter: simulation of single-cell RNA sequencing data (Genome Biology)
- mRNA-Seq whole-transcriptome analysis of a single cell (Nature Methods)
Their papers
- Normalizing single-cell RNA sequencing data: Challenges and opportunities (2017) — 401 citations
- Eleven grand challenges in single-cell data science (2020) — 1,198 citations