Batch effect removal methods for microarray gene expression data integration: a survey

Lazar, C., Meganck, S., Taminau, J., Steenhoff, D., Coletta, A., Molter, C., Weiss-Solis, D. Y., Duque, R. +2 more·2013·Briefings in Bioinformatics

Cited by (334)

Items 301–334 of 334

  1. The significance of molecular heterogeneity in breast cancer batch correction and dataset integration
    Nicholas Moir, Dominic Pearce, Simon P. Langdon, T. Ian Simpson · 2024 · medRxiv
  2. sampleclusteR: A lightweight R package for automated clustering of transcriptomics samples using metadata
    Brandon Coke, Mahesan Niranjan, Rob M. Ewing · 2025 · bioRxiv (Cold Spring Harbor Laboratory)
  3. Processing primary gene expression data: Normalization, harmonization and data quality control
    Nicolas M. Borisov, Maxim Sorokin, Anton A. Buzdin · 2025 · Elsevier eBooks
  4. Comment on “KDM6A, ARID1A and SETD2 loss-of-function mutations as prognostic biomarkers and their association with sites of metastatic progression in locally-advanced, recurrent or metastatic adenoid cystic carcinoma”
    Yan Kuang, Na Liu, Rongguang Kuang · 2025 · Oral Oncology
  5. Introducing feature identification and refinement engine (FIRE) for identifying consistent and informative gene signature
    Shivam Kumar, Devvrat Pandey, Samrat Chatterjee · 2025 · Computational Biology and Chemistry
  6. High-Fidelity Transcriptome Reconstruction of Degraded RNA-Seq Samples Using Denoising Diffusion Models
    Ke Xiao, Jinlei Sun, Yunqing Liu, Chen Li, Hengchuan Guo, Yiying Wang, Yajun Pang, Zhiyu Liu · 2025 · Biology
  7. HGAlign: Biologically preserving batch correction and classification for metabolomics via heterogeneous graph alignment
    Yang Gao, Haoyun Yu, Chunman Zuo · 2025 · Computational Biology and Chemistry
  8. Additional file 1 of Single-center versus multi-center data sets for molecular prognostic modeling: a simulation study
    Daniel Samaga, Roman Hornung, Herbert Braselmann, Julia Heß, Horst F. Zitzelsberger, Belka, Claus, Boulesteix, Anne-Laure, Unger, Kristian · 2020 · Figshare
  9. Additional file 3 of An 8-gene machine learning model improves clinical prediction of severe dengue progression
    Yiran E. Liu, Sirle Saul, Aditya Rao, Makeda Robinson, Agudelo Rojas, Olga Lucia, Ana María Sanz, Michelle Verghese, Daniel C. Solis, Mamdouh Sibai, Chun Huang et al. · 2022 · Figshare
  10. Additional file 1 of Repeatability of radiomics studies in colorectal cancer: a systematic review
    Liu, Ying, Xiaoqin Wei, Feng, Xu, Liu, Yan, Feng, Guiling, Du, Yong · 2023 · Figshare
  11. Additional file 1 of Joint deep learning for batch effect removal and classification toward MALDI MS based metabolomics
    Jingyang Niu, Yang, Jing, Yuyu Guo, Kun Qian, Wang, Qian · 2022 · Figshare
  12. Additional file 2 of Repeatability of radiomics studies in colorectal cancer: a systematic review
    Liu, Ying, Xiaoqin Wei, Feng, Xu, Yan Liu, Feng, Guiling, Du, Yong · 2023 · Figshare
  13. Additional file 2 of An 8-gene machine learning model improves clinical prediction of severe dengue progression
    Yiran E. Liu, Sirle Saul, Aditya Rao, Makeda Robinson, Agudelo Rojas, Olga Lucia, Ana María Sanz, Michelle Verghese, Daniel C. Solis, Mamdouh Sibai, Chun Huang et al. · 2022 · Figshare
  14. Additional file 2 of Propensity scores as a novel method to guide sample allocation and minimize batch effects during the design of high throughput experiments
    Patrick M. Carry, Vigers, Tim, Vanderlinden, Lauren A., Keeter, Carson, Fran Dong, Teresa Buckner, Litkowski, Elizabeth, Yang, Ivana, Jill M. Norris, Katerina J. Kechris · 2023 · Figshare
  15. Additional file 5 of Embedding covariate adjustments in tree-based automated machine learning for biomedical big data analyses
    Elisabetta Manduchi, Fu, Weixuan, Romano, Joseph D., Ruberto, Stefano, Jason H. Moore · 2020 · Figshare
  16. Additional file 2 of Correcting batch effects in large-scale multiomics studies using a reference-material-based ratio method
    Yu, Ying, Zhang, Naixin, Yuanbang Mai, Luyao Ren, Chen, Qiaochu, Zehui Cao, Chen, Qingwang, Liu, Yaqing, Wanwan Hou, Yang, Jingcheng et al. · 2023 · Figshare
  17. Additional file 1 of An 8-gene machine learning model improves clinical prediction of severe dengue progression
    Yiran E. Liu, Sirle Saul, Aditya Rao, Makeda Robinson, Agudelo Rojas, Olga Lucia, Ana María Sanz, Michelle Verghese, Daniel C. Solis, Mamdouh Sibai, Chun Huang et al. · 2022 · Figshare
  18. Additional file 4 of Embedding covariate adjustments in tree-based automated machine learning for biomedical big data analyses
    Elisabetta Manduchi, Fu, Weixuan, Romano, Joseph D., Stefano Ruberto, Jason H. Moore · 2020 · Figshare
  19. Additional file 1 of Correcting batch effects in large-scale multiomics studies using a reference-material-based ratio method
    Yu, Ying, Zhang, Naixin, Yuanbang Mai, Luyao Ren, Chen, Qiaochu, Zehui Cao, Chen, Qingwang, Liu, Yaqing, Wanwan Hou, Yang, Jingcheng et al. · 2023 · Figshare
  20. Correcting batch effects in fermentation processes using empirical Bayesian approach
    Kaiqiang Lou, Shunyi Zhao, Xiaoli Luan, Fei Liu · 2026 · Journal of Process Control
  21. Benchmarking Preprocessing and Integration Methods in Single-Cell Genomics
    Ali Anaissi, Seid Miad Zandavi, Weidong Huang, Junaid Akram, Basem Suleiman, Ali Braytee, Jie Hua · 2026 · Communications in computer and information science
  22. The Integration of Bioinformatics with Experimental Validation Has Uncovered Shared Molecular Characteristics Between Sarcopenia and Osteoarthritis
    Deyu Wang, Wei Wei, Fumin Xue, Hongfei Liu, Xiangran Cui, Yongju Yang, Xuefeng Guan · 2026 · Calcified Tissue International
  23. Enhancing Predictive Modeling of Breast Cancer Drug Response Via Multi-Dataset Fusion and Comparative Imputation Methods
    Lina AlRifai, Mostafa Z. Ali, Qasem S. Abu Al-Haija, Talal Z. Ali, Belal Al-Husein · 2026 · IEEE Conference on Computer Applications (ICCA)
  24. Development and comparison of evaluation metrics for batch correction reveals performance differences
    Aleksi Laiho, M P E Laitinen, Liisa Holm, Petri Törönen · 2026 · Bioinformatics Advances
  25. USADAE: a deep learning approach to disentangle hidden covariates in RNA-seq data
    Xu Chen, Luoyuan Guo, Yaosheng Chen, Delin Mo, Xiaohong Liu · 2026 · Briefings in Bioinformatics
  26. Dissecting the expression pattern during embryonic development and unveiling sustained ZGA genes with oncogenic relevance
    Han Xing, Yongjian Zhao · 2026 · BMC Genomics
  27. A novel batch effect correction framework for robust integration of high-variance data via a global-information virtual reference batch
    Yuqian Liu, Lan Du, Jingxuan Jiang, Xin Tong, Junfei Xu, Jiayin Wang, Xin Lai · 2026 · Frontiers in Microbiology
  28. EPHX2 Expression and Its Association with Prognosis, Metabolic Regulation, and Metastasis-Related Pathways in Lung Adenocarcinoma
    Şebnem Yıldırımcan Kadıçeşme · 2026 · Genes
  29. A multi-layer framework for computational cancer epigenomics
    Manyata Srivastava, Pratik Kumar, Ankita Chouhan, Komal Maan, Shishir Singh, Monisha Banerjee, Atar Singh Kushwah · 2026 · Academia molecular biology and genomics.
  30. Effects of recombinant brazzein consumption as a sugar alternative on human gut microbiota, biochemical status, and psychological well-being
    Philip Sokolov, Vladimir A. Veselovsky, Anatoly A. Kotlobay, Aleksei Fomenko, Aleksandra A. Strokach, Dmitry Andreev, Polina Y. Zoruk, Mikhail Devkota, Evgenii Olekhnovich, Sergei Tsaregorodtsev et al. · 2026 · Food Production Processing and Nutrition
  31. Characterization of a β-Glucosidase from Fragaria × ananassa: Substrate Specificity, Catalytic Efficiency, and Structural Insights
    Ibrahim Rabeeah, Alberto Zavarise, Alessandro Manghi, Viktoria Gruber-Schmidt, Matthias Hackl, Renate Paltram, Manfred Gössinger, Luca Cattani, Karl Stich, Hester Sheehan et al. · 2026 · Foods
  32. Integration of Multi-Omics in AI-Based Models for Predictive Oncology
    Arnaud Nativel, Sarah Dandou, Florian Favier, Candice Lesage, Alain Mangé, Julien Aligon, Peter J. P. Coopman, Romain M. Larive · 2026 · Genes
  33. 2,234 attempts to harmonize a multi-platform B-cell lymphoma bulk transcriptomic dataset: ComBat is superior by cross-batch prediction quality
    Daniil M. Nikitin, Arsen Arakelyan · 2026 · bioRxiv (Cold Spring Harbor Laboratory)
  34. Multi-omics, multi-tissue analysis reveal role of extracellular matrix remodeling and sphingolipid homeostasis in edematous malnutrition (kwashiorkor)
    Fabienne Nackers, Kirrily de Polnay, Ruben Almey, Simon Daled, Azahir Dan Djibo, Sarah De Saeger, Dieter L. D. Deforce, Maarten Dhaenens, Yannick Gansemans, Griet Glorieux et al. · 2026 · Biomarker Research