Jeffrey T. Leek

Molecular Biology · 2005–2020

A researcher in the Science corpus: 14 papers; trusts 77 researchers and is trusted by 54 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 162 citations into it; #1,148 of 17,773 by trust here, #2,214 by citations
• Genetics — 48 citations into it; #405 of 6,854 by trust here, #2,063 by citations
• Statistics and Probability — 28 citations into it; #6,304 of 12,932 by trust here, #5,294 by citations
• Artificial Intelligence — 25 citations into it
• Cancer Research — 18 citations into it; #238 of 1,780 by trust here, #1,289 by citations
• Management Science and Operations Research — 11 citations into it

Whom Jeffrey T. Leek trusts overall

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

1. John D. Storey
2. Yoav Benjamini
3. Mark D. Robinson
4. Simon Anders
5. J. Shaffer
6. Alkes L Price
7. Johann A. Gagnon-Bartsch
8. Bradley Efron
9. James H. Bullard
10. Gordon K. Smyth

Who trusts Jeffrey T. Leek overall

1. Yuqing Zhang
2. Laleh Haghverdi
3. Johann A. Gagnon-Bartsch
4. Jean-Philippe Fortin
5. Art B. Owen
6. Gabriel Erion-Barner
7. Oliver Stegle
8. Gao Wang
9. Kévin Caye
10. Greg Finak

Papers from Jeffrey T. Leek's perspective

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

1. Controlling the false discovery rate: a practical and powerful approach to multiple testing
2. Statistical significance for genomewide studies (Proceedings of the National Academy of Sciences of the United States of America)
3. Mapping and quantifying mammalian transcriptomes by RNA-Seq (Nature Methods)
4. THE CONTROL OF THE FALSE DISCOVERY RATE IN MULTIPLE TESTING UNDER DEPENDENCY
5. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome (Genome Biology)
6. A scaling normalization method for differential expression analysis of RNA-seq data (Genome Biology)
7. RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. (Genome Research)
8. Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments (Statistical Applications in Genetics and Molecular Biology)
9. Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. (Nucleic Acids Research)
10. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data (Bioinform.)

Their papers

• Tackling the widespread and critical impact of batch effects in high-throughput data (2010) — 2,064 citations
• Cloud-scale RNA-sequencing differential expression analysis with Myrna (2010) — 346 citations
• A general framework for multiple testing dependence (2008) — 393 citations
• Significance analysis of time course microarray experiments. (2005) — 628 citations
• Capturing Heterogeneity in Gene Expression Studies by Surrogate Variable Analysis (2007) — 631 citations
• Temporal dynamics and genetic control of transcription in the human prefrontal cortex (2011) — 699 citations
• Reproducible RNA-seq analysis using recount2 (2017) — 368 citations
• Bump hunting to identify differentially methylated regions in epigenetic epidemiology studies. (2012) — 650 citations
• Developmental regulation of human cortex transcription and its clinical relevance at base resolution (2014) — 1,328 citations
• Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie, and Ballgown (2016) — 5,669 citations
• Transparency and reproducibility in artificial intelligence (2020) — 429 citations
• Quality score compression improves genotyping accuracy (2015) — 563 citations
• svaseq: removing batch effects and other unwanted noise from sequencing data (2014) — 513 citations
• Surrogate variable analysis (2007) — 88 citations