Davide Risso

Molecular Biology · 2011–2019

A researcher in the Science corpus: 6 papers; trusts 22 researchers and is trusted by 36 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 183 citations into it; #1,179 of 17,773 by trust here, #3,296 by citations
• Cancer Research — 39 citations into it; #1,368 of 1,780 by trust here, #1,332 by citations
• Genetics — 14 citations into it

Whom Davide Risso trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Mark D. Robinson
2. Gordon K. Smyth
3. William S. Cleveland
4. Kasper D. Hansen
5. James H. Bullard
6. Davis J. McCarthy
7. Sandrine Dudoit
8. Rafael A. Irizarry
9. Terence P. Speed
10. Johann A. Gagnon-Bartsch

Who trusts Davide Risso overall

1. Romain Lopez
2. Malte D. Luecken
3. Michael I. Love
4. Ciaran Evans
5. Aaron T. L. Lun
6. Jeffrey T. Leek
7. K. Van den Berge
8. Valentine Svensson
9. Yunshun Chen
10. Simon Anders

Papers from Davide Risso's perspective

Ordered by how strongly the researchers Davide Risso trusts endorse each paper. Same corpus, a different order for every researcher.

1. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome (Genome Biology)
2. Mapping and quantifying mammalian transcriptomes by RNA-Seq (Nature Methods)
3. A scaling normalization method for differential expression analysis of RNA-seq data (Genome Biology)
4. Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments (Statistical Applications in Genetics and Molecular Biology)
5. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data (Bioinform.)
6. RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. (Genome Research)
7. RNA-Seq: a revolutionary tool for transcriptomics (Nature reviews genetics)
8. Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments (BMC Bioinformatics)
9. Using control genes to correct for unwanted variation in microarray data. (Biostatistics)
10. Normalization of RNA-seq data using factor analysis of control genes or samples (Nature Biotechnology)

Their papers

• Normalization of RNA-seq data using factor analysis of control genes or samples (2014) — 1,897 citations
• GC-Content Normalization for RNA-Seq Data (2011) — 801 citations
• A general and flexible method for signal extraction from single-cell RNA-seq data (2017) — 624 citations
• Normalizing single-cell RNA sequencing data: Challenges and opportunities (2017) — 401 citations
• Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics (2017) — 2,624 citations
• Orchestrating single-cell analysis with Bioconductor (2019) — 840 citations