Aaron T. L. Lun

Molecular Biology · 2015–2024

A researcher in the Science corpus: 11 papers; trusts 52 researchers and is trusted by 61 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 276 citations into it; #549 of 17,773 by trust here, #2,009 by citations
• Cancer Research — 60 citations into it; #127 of 1,780 by trust here, #517 by citations
• Genetics — 36 citations into it
• Immunology — 23 citations into it
• Artificial Intelligence — 18 citations into it
• Statistics and Probability — 12 citations into it
• Computer Vision and Pattern Recognition — 10 citations into it
• Representation & Self-Supervised Learning — 8 citations into it

Whom Aaron T. L. Lun trusts overall

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

1. Mark D. Robinson
2. Gordon K. Smyth
3. Davis J. McCarthy
4. Yunshun Chen
5. Simon Anders
6. Wolfgang Huber
7. Alicia Oshlack
8. Yang Liao
9. Davide Risso
10. John C. Marioni

Who trusts Aaron T. L. Lun overall

1. Yunshun Chen
2. Malte D. Luecken
3. Gordon K. Smyth
4. Christina Ernst
5. Matthew D. Young
6. Christoph Hafemeister
7. Rahul Satija
8. Belinda Phipson
9. Fabian J Theis
10. John C. Marioni

Papers from Aaron T. L. Lun's perspective

Ordered by how strongly the researchers Aaron T. L. Lun 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. Mapping and quantifying mammalian transcriptomes by RNA-Seq (Nature Methods)
4. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome (Genome Biology)
5. Normalization of RNA-seq data using factor analysis of control genes or samples (Nature Biotechnology)
6. Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments (Statistical Applications in Genetics and Molecular Biology)
7. Controlling the false discovery rate: a practical and powerful approach to multiple testing
8. Generalized Linear Models
9. Small-sample estimation of negative binomial dispersion, with applications to SAGE data. (Biostatistics)
10. HTSeq—a Python framework to work with high-throughput sequencing data (bioRxiv)

Their papers

• Pooling across cells to normalize single-cell RNA sequencing data with many zero counts (2016) — 1,087 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
• Batch effects in single-cell RNA sequencing data are corrected by matching mutual nearest neighbours (2018) — 2,133 citations
• csaw: a Bioconductor package for differential binding analysis of ChIP-seq data using sliding windows (2015) — 414 citations
• Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R (2017) — 1,604 citations
• It's DE-licious: A Recipe for Differential Expression Analyses of RNA-seq Experiments Using Quasi-Likelihood Methods in edgeR (2016) — 388 citations
• A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor (2016) — 1,565 citations
• EmptyDrops: distinguishing cells from empty droplets in droplet-based single-cell RNA sequencing data (2019) — 871 citations
• Orchestrating single-cell analysis with Bioconductor (2019) — 840 citations
• edgeR v4: powerful differential analysis of sequencing data with expanded functionality and improved support for small counts and larger datasets (2024) — 607 citations
• Doublet identification in single-cell sequencing data using scDblFinder (2021) — 1,087 citations