ArticleBioinformatics (Oxford, England)2018
Feature specific quantile normalization enables cross-platform classification of molecular subtypes using gene expression data.
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 46 papers, 3 of them syntheses that pooled it.
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Who cites it
46 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- A time-resolved meta-analysis of consensus gene expression profiles during human T-cell activation.Genome biology · 2023Pooled it
- A genomic meta-analysis of clinical variables and their association with intrinsic molecular subsets in systemic sclerosis.Rheumatology (Oxford, England) · 2022Pooled it
- Multivariate meta-analysis reveals global transcriptomic signatures underlying distinct human naive-like pluripotent states.PloS one · 2021Pooled it
- Machine-learning classification identifies patients with early systemic sclerosis as abatacept responders via CD28 pathway modulation.JCI insight · 2022Trial
- BOLD-100 in Combination with FOLFOX Is a Broadly Effective Therapeutic Strategy for Gastric Cancer.International journal of molecular sciences · 2026Article
- Establishment and characterization of a novel human ampullary carcinoma cell line derived from a Chinese patient.BMC cancer · 2026Article
- Transplatformer: translating toxicogenomic profiles between generations of platforms.BMC bioinformatics · 2026Article
- A 107 Gene Nanostring Assay Effectively Translates the Cancer Genome Atlas, and Tumour Microenvironment Gastric Cancer Molecular Classification to a Patient-Derived Organoid Model.Genes, chromosomes & cancer · 2025Article
- WMRCA + : a weighted majority rule-based clustering method for cancer subtype prediction using metabolic gene sets.Hereditas · 2025Article
- Metabolic differentiation of brushtail possum populations resistant and susceptible to plant toxins revealed via differential gene expression.Journal of comparative physiology. B, Biochemical, systemic, and environmental physiology · 2025Article
- Article
- BC-predict: mining of signal biomarkers and production of models for early-stage breast cancer subtyping and prognosis.Frontiers in bioinformatics · 2025Article
- Normalization and Selecting Non-Differentially Expressed Genes Improve Machine Learning Modelling of Cross-Platform Transcriptomic Data.Transactions on artificial intelligence · 2025Article
- Molecular Subtypes of High-Grade Serous Ovarian Cancer across Racial Groups and Gene Expression Platforms.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2024Article
- A comparison of RNA-Seq data preprocessing pipelines for transcriptomic predictions across independent studies.BMC bioinformatics · 2024Article
- Feature-specific quantile normalization and feature-specific mean-variance normalization deliver robust bi-directional classification and feature selection performance between microarray and RNAseq data.BMC bioinformatics · 2024Article
- Comparison of the effectiveness of different normalization methods for metagenomic cross-study phenotype prediction under heterogeneity.Scientific reports · 2024Article
- Evaluation of normalization methods for predicting quantitative phenotypes in metagenomic data analysis.Frontiers in genetics · 2024Article
- Learning Individual Survival Models from PanCancer Whole Transcriptome Data.Clinical cancer research : an official journal of the American Association for Cancer Research · 2023Article
- A robust immune-related gene pairs signature for predicting the overall survival of esophageal cancer.BMC genomics · 2023Article
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3 authors.
Funding
Abstract
Motivation: Molecular subtypes of cancers and autoimmune disease, defined by transcriptomic profiling, have provided insight into disease pathogenesis, molecular heterogeneity and therapeutic responses. However, technical biases inherent to different gene expression profiling platforms present a unique problem when analyzing data generated from different studies. Currently, there is a lack of effective methods designed to eliminate platform-based bias. We present a method to normalize and classify RNA-seq data using machine learning classifiers trained on DNA microarray data and molecular subtypes in two datasets: breast invasive carcinoma (BRCA) and colorectal cancer (CRC). Results: Multiple analyses show that feature specific quantile normalization (FSQN) successfully removes platform-based bias from RNA-seq data, regardless of feature scaling or machine learning algorithm. We achieve up to 98% accuracy for BRCA data and 97% accuracy for CRC data in assigning molecular subtypes to RNA-seq data normalized using FSQN and a support vector machine trained exclusively on DNA microarray data. We find that maximum accuracy was achieved when normalizing RNA-seq datasets that contain at least 25 samples. FSQN allows comparison of RNA-seq data to existing DNA microarray datasets. Using these techniques, we can successfully leverage information from existing gene expression data in new analyses despite different platforms used for gene expression profiling. Availability and implementation: FSQN has been submitted as an R package to CRAN. All code used for this study is available on Github (https://github.com/jenniferfranks/FSQN). Contact: michael.l.whitfield@dartmouth.edu. Supplementary information: Supplementary data are available at Bioinformatics online.
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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.