Gordon K. Smyth

232.9k citations
347 papers · 132.0k · 27 hit papers · h-index 89

Impact in

  • Cancer Research top 0.01%
    • Cancer-related molecular mechanisms research
    • MicroRNA in disease regulation
    • RNA modifications and cancer
    • Epigenetics and DNA Methylation
    • RNA Research and Splicing
    • Gene expression and cancer classification
    • Genomics and Chromatin Dynamics

Papers in

    • Gene expression and cancer classification 48
    • Epigenetics and DNA Methylation 31
    • Genomics and Chromatin Dynamics 28
    • Molecular Biology Techniques and Applications 23
    • Cancer Cells and Metastasis 21

Gordon K. Smyth

340 papers receiving 130.8k citations

Gordon K. Smyth's Hit Papers

edgeR v4: powerful differential analysis of sequencing data with expanded functionality and improved support for small counts and larger datasets 2025 · 160 citations
1600+5+11Years since publication5.0k10.0k15.0k20.0k25.0k

Peers

Gordon K. Smyth
Comparison fields: 5 of 228
  • Cancer Research 17.4k
  • Molecular Biology 65.1k
  • Immunology 16.6k
  • Aging 1.3k
  • Oncology 12.4k
Replace Wolfgang Huber with:
Wolfgang Huber Germany
Aviv Regev United States
Simon Anders Germany
Peer Bork Germany
Matthias Mann Germany
Minoru Kanehisa Japan
Steven L. Salzberg United States
Patrick O. Brown United States
Eric S. Lander United States
Christopher K. Glass United States
Gordon K. Smyth relative to Wolfgang Huber Germany Wolfgang Huber's profile →
Citations per field
00.5×1.5×2.1×
Wolfgang Huber · 1×
Citations per year

Countries citing papers authored by Gordon K. Smyth

Since Specialization
Citations

This map shows the geographic impact of Gordon K. Smyth'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 Gordon K. Smyth with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gordon K. Smyth more than expected).

Fields of papers citing papers by Gordon K. Smyth

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Gordon K. Smyth. 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 Gordon K. Smyth. The network helps show where Gordon K. Smyth may publish in the future.

Co-authors

The 25 scholars most cited alongside Gordon K. Smyth, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Gordon K. Smyth Line = papers co-authored together Gordon K. Smyth links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 347 papers — load more, or switch the sort, to bring in the rest.

#Work
1
edgeR : a Bioconductor package for differential expression analysis of digital gene expression data
Hit paper breakdown →
200928948
2
limma powers differential expression analyses for RNA-sequencing and microarray studies
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201524718
3
featureCounts: an efficient general purpose program for assigning sequence reads to genomic features
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201316256
4
Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments
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20049588
5
Gene ontology analysis for RNA-seq: accounting for selection bias
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20105372
6
limma: Linear Models for Microarray Data
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20054651
7
voom: precision weights unlock linear model analysis tools for RNA-seq read counts
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20143752
8
Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation
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20123500
9
The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote
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20131964
10
The R package Rsubread is easier, faster, cheaper and better for alignment and quantification of RNA sequencing reads
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20191647
11
Generation of a functional mammary gland from a single stem cell
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20061611
12
Normalization of cDNA microarray data
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20031530
13
ELDA: Extreme limiting dilution analysis for comparing depleted and enriched populations in stem cell and other assays
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20091476
14
Use of within-array replicate spots for assessing differential expression in microarray experiments
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20051145
15
Aberrant luminal progenitors as the candidate target population for basal tumor development in BRCA1 mutation carriers
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20091098
16
Count-based differential expression analysis of RNA sequencing data using R and Bioconductor
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2013861
17
Small-sample estimation of negative binomial dispersion, with applications to SAGE data
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2007788
18
A comparison of background correction methods for two-colour microarrays
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2007747
19
Randomized Quantile Residuals
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1996746
20
Moderated statistical tests for assessing differences in tag abundance
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2007609

About Gordon K. Smyth

Gordon K. Smyth is a scholar working on Molecular Biology, Oncology, Immunology, Cancer Research and Statistics and Probability, having authored 347 papers that have together received 132.0k indexed citations. Recurring topics across this work include Gene expression and cancer classification (48 papers), Epigenetics and DNA Methylation (31 papers), T-cell and B-cell Immunology (31 papers), Genomics and Chromatin Dynamics (28 papers), Immune Cell Function and Interaction (28 papers), Statistical Methods and Bayesian Inference (23 papers), Molecular Biology Techniques and Applications (23 papers) and Cancer Cells and Metastasis (21 papers). The work is most often cited by research in Cancer Research (17.4k citations), Molecular Biology (65.1k citations), Immunology (16.6k citations), Aging (1.3k citations) and Oncology (12.4k citations). Gordon K. Smyth has collaborated with scholars based in Australia, United States and Ireland. Frequent co-authors include Wei Shi, Davis J. McCarthy, Mark D. Robinson, Yang Liao, Charity W. Law, Yifang Hu, Di Wu, Matthew E. Ritchie, Belinda Phipson and Yunshun Chen. Their work appears in journals such as Nucleic Acids Research, Blood, Bioinformatics, Nature Communications and Cell Reports.

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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