Yunshun Chen

Molecular Biology · 2012–2024

A researcher in the Science corpus: 5 papers; trusts 51 researchers and is trusted by 49 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 115 citations into it; #586 of 17,773 by trust here, #3,165 by citations
• Genetics — 24 citations into it
• Cancer Research — 18 citations into it; #153 of 1,780 by trust here, #565 by citations
• Statistics and Probability — 18 citations into it
• Management Science and Operations Research — 11 citations into it
• Artificial Intelligence — 8 citations into it
• Computational Biology & Bioinformatics — 7 citations into it
• Language and Linguistics — 5 citations into it

Whom Yunshun Chen trusts overall

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

1. Gordon K. Smyth
2. Mark D. Robinson
3. Davis J. McCarthy
4. Eric R. Ziegel
5. Aaron T. L. Lun
6. Alicia Oshlack
7. Belinda Phipson
8. Diabetes-Ling Wu
9. Kasper D. Hansen
10. Charlotte Soneson

Who trusts Yunshun Chen overall

1. Aaron T. L. Lun
2. Gordon K. Smyth
3. Yang Liao
4. Michael I. Love
5. Anqi Zhu
6. Charlotte Soneson
7. K. Van den Berge
8. Joseph G. Ibrahim
9. Xiaobei Zhou
10. Wei Shi

Papers from Yunshun Chen's perspective

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

1. A scaling normalization method for differential expression analysis of RNA-seq data (Genome Biology)
2. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data (Bioinform.)
3. Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments (Statistical Applications in Genetics and Molecular Biology)
4. Controlling the false discovery rate: a practical and powerful approach to multiple testing
5. Understanding mechanisms underlying human gene expression variation with RNA sequencing (Nature)
6. Small-sample estimation of negative binomial dispersion, with applications to SAGE data. (Biostatistics)
7. Generalized Linear Models
8. Moderated statistical tests for assessing differences in tag abundance (Bioinform.)
9. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome (Genome Biology)
10. Mapping and quantifying mammalian transcriptomes by RNA-Seq (Nature Methods)

Their papers

• voom: precision weights unlock linear model analysis tools for RNA-seq read counts (2014) — 5,697 citations
• Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation (2012) — 4,744 citations
• From reads to genes to pathways: differential expression analysis of RNA-Seq experiments using Rsubread and the edgeR quasi-likelihood pipeline (2016) — 1,215 citations
• It's DE-licious: A Recipe for Differential Expression Analyses of RNA-seq Experiments Using Quasi-Likelihood Methods in edgeR (2016) — 388 citations
• edgeR v4: powerful differential analysis of sequencing data with expanded functionality and improved support for small counts and larger datasets (2024) — 607 citations