ArticleScientific reports2016
CrossNorm: a novel normalization strategy for microarray data in cancers.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
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- Pairwise analysis of gene expression for oral squamous cell carcinoma via a large-scale transcriptome integration.Journal of cellular and molecular medicine · 2024Article
- Deep learning model to discriminate diverse infection types based on pairwise analysis of host gene expression.iScience · 2024Article
- Machine learning-based investigation of regulated cell death for predicting prognosis and immunotherapy response in glioma patients.Scientific reports · 2024Article
- MrGPS: an m6A-related gene pair signature to predict the prognosis and immunological impact of glioma patients.Briefings in bioinformatics · 2023Article
- Co-expression module analysis reveals high expression homogeneity for both coding and non-coding genes in sepsis.BMC genomics · 2023Article
- MicroRNAs as Targets for Cancer Diagnosis: Interests and Limitations.Advanced pharmaceutical bulletin · 2023Review
- bvnGPS: a generalizable diagnostic model for acute bacterial and viral infection using integrative host transcriptomics and pretrained neural networks.Bioinformatics (Oxford, England) · 2023Article
- Cross-platform normalization enables machine learning model training on microarray and RNA-seq data simultaneously.Communications biology · 2023Article
- Normalization of Large-Scale Transcriptome Data Using Heuristic Methods.Bioinformatics and biology insights · 2023Article
- GPGPS: a robust prognostic gene pair signature of glioma ensembling IDH mutation and 1p/19q co-deletion.Bioinformatics (Oxford, England) · 2023Article
- Platelets Purification Is a Crucial Step for Transcriptomic Analysis.International journal of molecular sciences · 2022Article
- Iron metabolism-related genes reveal predictive value of acute coronary syndrome.Frontiers in pharmacology · 2022Article
- Rapid Transient Transcriptional Adaptation to Hypergravity in Jurkat T Cells Revealed by Comparative Analysis of Microarray and RNA-Seq Data.International journal of molecular sciences · 2021Article
- The pregnane X receptor drives sexually dimorphic hepatic changes in lipid and xenobiotic metabolism in response to gut microbiota in mice.Microbiome · 2021Article
- Long non-coding RNA pairs to assist in diagnosing sepsis.BMC genomics · 2021Article
- Airway gene-expression classifiers for respiratory syncytial virus (RSV) disease severity in infants.BMC medical genomics · 2021Article
- Blood Circulating miRNA Pairs as a Robust Signature for Early Detection of Esophageal Cancer.Frontiers in oncology · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Normalization is essential to get rid of biases in microarray data for their accurate analysis. Existing normalization methods for microarray gene expression data commonly assume a similar global expression pattern among samples being studied. However, scenarios of global shifts in gene expressions are dominant in cancers, making the assumption invalid. To alleviate the problem, here we propose and develop a novel normalization strategy, Cross Normalization (CrossNorm), for microarray data with unbalanced transcript levels among samples. Conventional procedures, such as RMA and LOESS, arbitrarily flatten the difference between case and control groups leading to biased gene expression estimates. Noticeably, applying these methods under the strategy of CrossNorm, which makes use of the overall statistics of the original signals, the results showed significantly improved robustness and accuracy in estimating transcript level dynamics for a series of publicly available datasets, including titration experiment, simulated data, spike-in data and several real-life microarray datasets across various types of cancers. The results have important implications for the past and the future cancer studies based on microarray samples with non-negligible difference. Moreover, the strategy can also be applied to other sorts of high-throughput data as long as the experiments have global expression variations between conditions.
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