Jeffrey T. Leek

30.5k total citations · 5 hit papers
82 papers, 16.1k citations indexed

About

Jeffrey T. Leek is a scholar working on Molecular Biology, Artificial Intelligence and Genetics. According to data from OpenAlex, Jeffrey T. Leek has authored 82 papers receiving a total of 16.1k indexed citations (citations by other indexed papers that have themselves been cited), including 51 papers in Molecular Biology, 12 papers in Artificial Intelligence and 12 papers in Genetics. Recurrent topics in Jeffrey T. Leek's work include Gene expression and cancer classification (32 papers), Bioinformatics and Genomic Networks (16 papers) and Molecular Biology Techniques and Applications (12 papers). Jeffrey T. Leek is often cited by papers focused on Gene expression and cancer classification (32 papers), Bioinformatics and Genomic Networks (16 papers) and Molecular Biology Techniques and Applications (12 papers). Jeffrey T. Leek collaborates with scholars based in United States, United Kingdom and Mexico. Jeffrey T. Leek's co-authors include John D. Storey, Andrew E. Jaffe, Steven L. Salzberg, W. Evan Johnson, Daehwan Kim, Geo Pertea, Mihaela Pertea, Hilary S. Parker, Ben Langmead and Rafael A. Irizarry and has published in prestigious journals such as Nature, Science and Cell.

In The Last Decade

Jeffrey T. Leek

81 papers receiving 15.9k citations

Hit Papers

align trajectories sort by overperformance

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown

2016 4.4k citations Mihaela Pertea, Daehwan Kim et al. Nature Protocols profile →
The sva package for removing batch effects and other unwanted variation in high-throughput experiments

2012 3.6k citations Jeffrey T. Leek, W. Evan Johnson et al. Bioinformatics profile →
Tackling the widespread and critical impact of batch effects in high-throughput data

2010 1.4k citations Jeffrey T. Leek, Héctor Corrada Bravo et al. profile →
Capturing Heterogeneity in Gene Expression Studies by Surrogate Variable Analysis

2007 1.2k citations Jeffrey T. Leek, John D. Storey profile →
Bump hunting to identify differentially methylated regions in epigenetic epidemiology studies

2012 457 citations Andrew E. Jaffe, Peter Murakami et al. International Journal of Epidemiology profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Jeffrey T. Leek United States	39	10.0k	2.5k	2.5k	1.7k	1.4k	82	16.1k
Sandrine Dudoit United States	50	11.4k 1.1×	1.6k 0.6×	2.2k 0.9×	790 0.5×	1.4k 1.0×	99	17.4k
Gilbert Chu United States	43	14.0k 1.4×	2.3k 0.9×	3.0k 1.2×	1.4k 0.8×	1.5k 1.1×	87	19.8k
Olga G. Troyanskaya United States	54	11.3k 1.1×	2.1k 0.8×	1.3k 0.5×	538 0.3×	802 0.6×	153	15.8k
John D. Storey United States	40	15.2k 1.5×	5.5k 2.2×	2.3k 0.9×	2.0k 1.2×	1.7k 1.2×	83	26.0k
W. Evan Johnson United States	35	8.9k 0.9×	1.8k 0.7×	2.5k 1.0×	425 0.2×	1.6k 1.1×	126	14.6k
Jean Yang Australia	52	8.2k 0.8×	1.2k 0.5×	1.6k 0.6×	661 0.4×	1.6k 1.1×	257	13.8k
Satoru Miyano Japan	58	10.5k 1.0×	1.3k 0.5×	2.7k 1.1×	850 0.5×	1.1k 0.8×	523	15.5k
Hongyu Zhao United States	77	11.6k 1.2×	6.4k 2.5×	1.5k 0.6×	3.2k 1.9×	1.4k 1.0×	746	25.1k
Michael Q. Zhang United States	68	18.2k 1.8×	2.4k 0.9×	2.9k 1.2×	1.7k 1.0×	1.3k 0.9×	226	21.6k
Benjamin M. Bolstad United States	8	7.7k 0.8×	1.3k 0.5×	2.0k 0.8×	886 0.5×	1.2k 0.8×	10	11.5k

Countries citing papers authored by Jeffrey T. Leek

Since Specialization

Citations

This map shows the geographic impact of Jeffrey T. Leek's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Jeffrey T. Leek with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jeffrey T. Leek more than expected).

Fields of papers citing papers by Jeffrey T. Leek

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jeffrey T. Leek. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Jeffrey T. Leek. The network helps show where Jeffrey T. Leek may publish in the future.

Co-authorship network of co-authors of Jeffrey T. Leek

This figure shows the co-authorship network connecting the top 25 collaborators of Jeffrey T. Leek. A scholar is included among the top collaborators of Jeffrey T. Leek based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Jeffrey T. Leek. Jeffrey T. Leek is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Majumder, Mary A., et al.. (2024). Large-scale genotype prediction from RNA sequence data necessitates a new ethical and policy framework. Nature Genetics. 56(8). 1537–1540. 1 indexed citations

Kammers, Kai, Margaret A. Taub, Benjamin A.T. Rodriguez, et al.. (2020). Transcriptional profile of platelets and iPSC-derived megakaryocytes from whole-genome and RNA sequencing. Blood. 137(7). 959–968. 13 indexed citations

McCormick, Tyler H., et al.. (2020). Methods for correcting inference based on outcomes predicted by machine learning. Proceedings of the National Academy of Sciences. 117(48). 30266–30275. 32 indexed citations

Ellis, Shannon, Leonardo Collado‐Torres, Andrew E. Jaffe, & Jeffrey T. Leek. (2018). Improving the value of public RNA-seq expression data by phenotype prediction. Nucleic Acids Research. 46(9). e54–e54. 23 indexed citations

Boca, Simina M. & Jeffrey T. Leek. (2018). A direct approach to estimating false discovery rates conditional on covariates. PeerJ. 6. e6035–e6035. 53 indexed citations

Jaffe, Andrew E., Richard E. Straub, Joo Heon Shin, et al.. (2018). Developmental and genetic regulation of the human cortex transcriptome illuminate schizophrenia pathogenesis. Nature Neuroscience. 21(8). 1117–1125. 211 indexed citations

Jaffe, Andrew E., Ran Tao, Alexis L. Norris, et al.. (2017). qSVA framework for RNA quality correction in differential expression analysis. Proceedings of the National Academy of Sciences. 114(27). 7130–7135. 59 indexed citations

Nellore, Abhinav, Christopher Wilks, Kasper D. Hansen, Jeffrey T. Leek, & Ben Langmead. (2016). Rail-dbGaP: analyzing dbGaP-protected data in the cloud with Amazon Elastic MapReduce. Bioinformatics. 32(16). 2551–2553. 3 indexed citations

Nellore, Abhinav, Leonardo Collado‐Torres, Andrew E. Jaffe, et al.. (2016). Rail-RNA: scalable analysis of RNA-seq splicing and coverage. Bioinformatics. 33(24). 4033–4040. 38 indexed citations

10.

Manimaran, Solaiappan, Kwame Okrah, Jeffrey T. Leek, et al.. (2016). BatchQC: interactive software for evaluating sample and batch effects in genomic data. Bioinformatics. 32(24). 3836–3838. 45 indexed citations

11.

Pertea, Mihaela, Daehwan Kim, Geo Pertea, Jeffrey T. Leek, & Steven L. Salzberg. (2016). Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown. Nature Protocols. 11(9). 1650–1667. 4416 indexed citations breakdown →

12.

Patil, Prasad, et al.. (2015). Test set bias affects reproducibility of gene signatures. Bioinformatics. 31(14). 2318–2323. 74 indexed citations

13.

Jager, Leah R. & Jeffrey T. Leek. (2013). An estimate of the science-wise false discovery rate and application to the top medical literature. Biostatistics. 15(1). 1–12. 109 indexed citations

14.

Parker, Hilary S. & Jeffrey T. Leek. (2012). The practical effect of batch on genomic prediction. Statistical Applications in Genetics and Molecular Biology. 11(3). Article 10–Article 10. 20 indexed citations

15.

Jaffe, Andrew E., Peter Murakami, Hwa-Jin Lee, et al.. (2012). Bump hunting to identify differentially methylated regions in epigenetic epidemiology studies. International Journal of Epidemiology. 41(1). 200–209. 457 indexed citations breakdown →

16.

Leek, Jeffrey T. & John D. Storey. (2011). The Joint Null Criterion for Multiple Hypothesis Tests. Statistical Applications in Genetics and Molecular Biology. 10(1). 16 indexed citations

17.

Frazee, Alyssa C., Ben Langmead, & Jeffrey T. Leek. (2011). ReCount: A multi-experiment resource of analysis-ready RNA-seq gene count datasets. BMC Bioinformatics. 12(1). 449–449. 103 indexed citations

18.

Leek, Jeffrey T. & John D. Storey. (2008). A general framework for multiple testing dependence. Proceedings of the National Academy of Sciences. 105(48). 18718–18723. 237 indexed citations

19.

Storey, John D., James Y. Dai, & Jeffrey T. Leek. (2006). The optimal discovery procedure for large-scale significance testing, with applications to comparative microarray experiments. Biostatistics. 8(2). 414–432. 106 indexed citations

20.

Craig, Jamie E., et al.. (2002). Deletion of the OPA1 gene in a family with dominant optic atrophy: evidence that haploinsufficiency is the cause of disease. Journal of Medical Genetics. 39. 47–48. 4 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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