Johann Gagnon-Bartsch
Johann Gagnon-Bartsch
Associate Professor of Statistics, University of Michigan
Потвърден имейл адрес: umich.edu - Начална страница
Using control genes to correct for unwanted variation in microarray data
JA Gagnon-Bartsch, TP Speed
Biostatistics 13 (3), 539-552, 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
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
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
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
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
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
Dtangle: accurate and robust cell type deconvolution
GJ Hunt, S Freytag, M Bahlo, JA Gagnon-Bartsch
Bioinformatics 35 (12), 2093-2099, 2019
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
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
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
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
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
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
The classification permutation test
J Gagnon-Bartsch, Y Shem-Tov
The Annals of Applied Statistics 13 (3), 1464-1483, 2019
The LOOP estimator: Adjusting for covariates in randomized experiments
E Wu, JA Gagnon-Bartsch
Evaluation review 42 (4), 458-488, 2018
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
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
Stably expressed genes in single-cell RNA sequencing
JM Deeke, JA Gagnon-Bartsch
Journal of Bioinformatics and Computational Biology 18 (01), 2040004, 2020
Automatic transformation and integration to improve visualization and discovery of latent effects in imaging data
GJ Hunt, MA Dane, JE Korkola, LM Heiser, JA Gagnon-Bartsch
Journal of Computational and Graphical Statistics 29 (4), 929-941, 2020
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