ArticleBioinformatics (Oxford, England)2018
Mitigating the adverse impact of batch effects in sample pattern detection.
Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Meta-Merging the Transcriptomes of Gastric Tumors Redefines the Connections among Molecular and Clinical Subtypes.Asian Pacific journal of cancer prevention : APJCP · 2026Article
- APPLICATION OF QUANTILE DISCRETIZATION AND BAYESIAN NETWORK ANALYSIS TO PUBLICLY AVAILABLE CYSTIC FIBROSIS DATA SETS.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2024Article
- The Sum of Two Halves May Be Different from the Whole-Effects of Splitting Sequencing Samples Across Lanes.Genes · 2022Article
- Identification and Validation of Candidate Gene Module Along With Immune Cells Infiltration Patterns in Atherosclerosis Progression to Plaque RuptureFrontiers in cardiovascular medicine · 2022Article
- Computational tumor stroma reaction evaluation led to novel prognosis-associated fibrosis and molecular signature discoveries in high-grade serous ovarian carcinoma.Frontiers in medicine · 2022Article
- Assessment of a computerized quantitative quality control tool for whole slide images of kidney biopsies.The Journal of pathology · 2021Article
- Comparative Transcriptomics across Nematode Life Cycles Reveal Gene Expression Conservation and Correlated Evolution in Adjacent Developmental Stages.Genome biology and evolution · 2020Article
- A transcriptional roadmap for 2C-like-to-pluripotent state transition.Science advances · 2020Article
- scBatch: batch-effect correction of RNA-seq data through sample distance matrix adjustment.Bioinformatics (Oxford, England) · 2020Article
- Cross-Regional View of Functional and Taxonomic Microbiota Composition in Obesity and Post-obesity Treatment Shows Country Specific Microbial Contribution.Frontiers in microbiology · 2019Article
- A new dynamic correlation algorithm reveals novel functional aspects in single cell and bulk RNA-seq data.PLoS computational biology · 2018Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Motivation: It is well known that batch effects exist in RNA-seq data and other profiling data. Although some methods do a good job adjusting for batch effects by modifying the data matrices, it is still difficult to remove the batch effects entirely. The remaining batch effect can cause artifacts in the detection of patterns in the data. Results: In this study, we consider the batch effect issue in the pattern detection among the samples, such as clustering, dimension reduction and construction of networks between subjects. Instead of adjusting the original data matrices, we design an adaptive method to directly adjust the dissimilarity matrix between samples. In simulation studies, the method achieved better results recovering true underlying clusters, compared to the leading batch effect adjustment method ComBat. In real data analysis, the method effectively corrected distance matrices and improved the performance of clustering algorithms. Availability and implementation: The R package is available at: https://github.com/tengfei-emory/QuantNorm. Supplementary information: Supplementary data are available at Bioinformatics online.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.