Charity W. Law

Molecular Biology · 2014–2016

A researcher in the Science corpus: 3 papers; trusts 21 researchers and is trusted by 23 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 70 citations into it; #1,835 of 17,773 by trust here, #1,741 by citations
• Genetics — 20 citations into it
• Artificial Intelligence — 10 citations into it
• Cancer Research — 8 citations into it; #196 of 1,780 by trust here, #621 by citations
• Statistics and Probability — 8 citations into it
• Management Science and Operations Research — 5 citations into it

Whom Charity W. Law 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. Diabetes-Ling Wu
5. Yang Liao
6. Thomas J. Hardcastle
7. George W. Wright
8. Charlotte Soneson
9. Alicia Oshlack
10. Maqc Consortium

Who trusts Charity W. Law overall

1. Yang Liao
2. Michael I. Love
3. Gordon K. Smyth
4. Jeffrey T. Leek
5. Belinda Phipson
6. Malte D. Luecken
7. Yunshun Chen
8. Gabriel E. Hoffman
9. Tallulah S. Andrews
10. Christoph Hafemeister

Papers from Charity W. Law's perspective

Ordered by how strongly the researchers Charity W. Law 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. Controlling the false discovery rate: a practical and powerful approach to multiple testing
4. Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments (Statistical Applications in Genetics and Molecular Biology)
5. Small-sample estimation of negative binomial dispersion, with applications to SAGE data. (Biostatistics)
6. Understanding mechanisms underlying human gene expression variation with RNA sequencing (Nature)
7. Generalized Linear Models
8. A comparison of methods for differential expression analysis of RNA-seq data (BMC Bioinformatics)
9. baySeq: Empirical Bayesian methods for identifying differential expression in sequence count data (BMC Bioinformatics)
10. Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. (Nucleic Acids Research)

Their papers

• voom: precision weights unlock linear model analysis tools for RNA-seq read counts (2014) — 5,697 citations
• limma powers differential expression analyses for RNA-sequencing and microarray studies (2015) — 34,728 citations
• RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR (2016) — 738 citations