| Using control genes to correct for unwanted variation in microarray data JA Gagnon-Bartsch, TP Speed Biostatistics 13 (3), 539-552, 2012 | 522 | 2012 |
| Signatures of tumour immunity distinguish Asian and non-Asian gastric adenocarcinomas SJ Lin, JA Gagnon-Bartsch, IB Tan, S Earle, L Ruff, K Pettinger, B Ylstra, ... Gut 64 (11), 1721-1731, 2015 | 248 | 2015 |
| Statistical methods for handling unwanted variation in metabolomics data AMD Livera, M Sysi-Aho, L Jacob, JA Gagnon-Bartsch, S Castillo, ... Analytical chemistry 87 (7), 3606-3615, 2015 | 204 | 2015 |
| Skeletal muscle and plasma lipidomic signatures of insulin resistance and overweight/obesity in humans KT Tonks, ACF Coster, MJ Christopher, R Chaudhuri, A Xu, ... Obesity 24 (4), 908-916, 2016 | 180 | 2016 |
| scMerge leverages factor analysis, stable expression, and pseudoreplication to merge multiple single-cell RNA-seq datasets Y Lin, S Ghazanfar, KYX Wang, JA Gagnon-Bartsch, KK Lo, X Su, ZG Han, ... Proceedings of the National Academy of Sciences 116 (20), 9775-9784, 2019 | 164 | 2019 |
| Removing unwanted variation from high dimensional data with negative controls JA Gagnon-Bartsch, L Jacob, TP Speed Berkeley: Tech Reports from Dep Stat Univ California, 1-112, 2013 | 122 | 2013 |
| Dtangle: accurate and robust cell type deconvolution GJ Hunt, S Freytag, M Bahlo, JA Gagnon-Bartsch Bioinformatics 35 (12), 2093-2099, 2019 | 114 | 2019 |
| Correcting gene expression data when neither the unwanted variation nor the factor of interest are observed L Jacob, JA Gagnon-Bartsch, TP Speed Biostatistics 17 (1), 16-28, 2016 | 112 | 2016 |
| A new normalization for Nanostring nCounter gene expression data R Molania, JA Gagnon-Bartsch, A Dobrovic, TP Speed Nucleic acids research 47 (12), 6073-6083, 2019 | 106 | 2019 |
| Comprehensive evaluation of deconvolution methods for human brain gene expression GJ Sutton, D Poppe, RK Simmons, K Walsh, U Nawaz, R Lister, ... Nature Communications 13 (1), 1358, 2022 | 93 | 2022 |
| Removing unwanted variation in a differential methylation analysis of Illumina HumanMethylation450 array data J Maksimovic, JA Gagnon-Bartsch, TP Speed, A Oshlack Nucleic acids research 43 (16), e106-e106, 2015 | 88 | 2015 |
| Vesicular monoamine and glutamate transporters select distinct synaptic vesicle recycling pathways B Onoa, H Li, LAB Elias, RH Edwards Biophysical Journal 98 (3), 501a, 2010 | 66 | 2010 |
| Systematic noise degrades gene co-expression signals but can be corrected S Freytag, J Gagnon-Bartsch, TP Speed, M Bahlo BMC bioinformatics 16, 1-17, 2015 | 60 | 2015 |
| Social media as an alternative to surveys of opinions about the economy FG Conrad, JA Gagnon-Bartsch, RA Ferg, MF Schober, J Pasek, E Hou Social Science Computer Review 39 (4), 489-508, 2021 | 56 | 2021 |
| The LOOP estimator: Adjusting for covariates in randomized experiments E Wu, JA Gagnon-Bartsch Evaluation review 42 (4), 458-488, 2018 | 54 | 2018 |
| The classification permutation test J Gagnon-Bartsch, Y Shem-Tov The Annals of Applied Statistics 13 (3), 1464-1483, 2019 | 50* | 2019 |
| Removing unwanted variation from large-scale RNA sequencing data with PRPS R Molania, M Foroutan, JA Gagnon-Bartsch, LC Gandolfo, A Jain, A Sinha, ... Nature Biotechnology 41 (1), 82-95, 2023 | 49 | 2023 |
| Precise unbiased estimation in randomized experiments using auxiliary observational data JA Gagnon-Bartsch, AC Sales, E Wu, AF Botelho, JA Erickson, ... Journal of Causal Inference 11 (1), 20220011, 2023 | 22 | 2023 |
| The role of scale in the estimation of cell-type proportions GJ Hunt, JA Gagnon-Bartsch | 12 | 2021 |
| Stably expressed genes in single-cell RNA sequencing JM Deeke, JA Gagnon-Bartsch Journal of Bioinformatics and Computational Biology 18 (01), 2040004, 2020 | 12 | 2020 |
