ComBat-seq: batch effect adjustment for RNA-seq count data Y Zhang, G Parmigiani, WE Johnson NAR Genomics and Bioinformatics 2 (3), 2020 | 791 | 2020 |
Alternative empirical Bayes models for adjusting for batch effects in genomic studies Y Zhang, DF Jenkins, S Manimaran, WE Johnson BMC Bioinformatics 19 (1), 262, 2018 | 70 | 2018 |
Pathway activity profiling of growth factor receptor network and stemness pathways differentiates metaplastic breast cancer histological subtypes JA McQuerry, DF Jenkins, SE Yost, Y Zhang, D Schmolze, WE Johnson, ... BMC cancer 19, 1-14, 2019 | 22 | 2019 |
The impact of different sources of heterogeneity on loss of accuracy from genomic prediction models Y Zhang, C Bernau, G Parmigiani, L Waldron Biostatistics, 2018 | 19 | 2018 |
Robustifying genomic classifiers to batch effects via ensemble learning Y Zhang, WE Johnson, G Parmigiani Bioinformatics, 2020 | 17 | 2020 |
Overcoming the impacts of two-step batch effect correction on gene expression estimation and inference T Li, Y Zhang, P Patil, WE Johnson Biostatistics 24 (3), 635-652, 2023 | 16 | 2023 |
Detection of multiple perturbations in multi‐omics biological networks PJ Griffin, Y Zhang, WE Johnson, ED Kolaczyk Biometrics, 2018 | 5 | 2018 |
Statistical and computational methods for addressing heterogeneity in genomic data Y Zhang Boston University, 2020 | | 2020 |
Package ‘simulatorZ’ Y Zhang, C Bernau, L Waldron, MY Zhang, I supports ExpressionSet, ... | | 2014 |