ArticleBioinformatics (Oxford, England)2019
Batch-normalization of cerebellar and medulloblastoma gene expression datasets utilizing empirically defined negative control genes.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
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Who cites it
44 citing papers in PubMed.
- The SHH/GLI target Netrin-1 supports self-renewal in medulloblastoma tumor cells.Cell communication and signaling : CCS · 2026Article
- Fibroblast growth factor receptor substrate 2 interactome mapping reveals novel candidate interactors associated with migration and invasion.Cell communication and signaling : CCS · 2026Article
- AhR-Siglec-15 axis regulates lysosomal Ca2+ release for sonic hedgehog medulloblastoma growth via TRPML1.Protein & cell · 2026Article
- RaMBat: Accurate identification of medulloblastoma subtypes from diverse data sources with severe batch effects.Molecular oncology · 2026Article
- A transcription regulator atlas identifies TOX3 as an Atoh1 coactivator in cerebellar development and tumorigenesis.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Fluid shear stress activates a targetable mechano-metastatic cascade to promote medulloblastoma metastasis.Nature biomedical engineering · 2026Article
- Identification of Raptor and GLI1 as USP37 substrates highlight its context-specific function in medulloblastoma cells.Oncogene · 2026Article
- EphA2 and phosphoantigen-mediated selective killing of medulloblastoma by γδT cells preserves neuronal and stem cell integrity.Oncoimmunology · 2025Article
- The transcription factor LHX2 mediates and enhances oncogenic BMP signaling in medulloblastoma.Cell death and differentiation · 2025Article
- How far are we from the era of big data in transcriptomics? Lessons from the bacterial data in GEO.Briefings in bioinformatics · 2025Review
- MYC promotes group 3 medulloblastoma cell proliferation and alleviates ROS-induced cell death by upregulating transketolase.Acta neuropathologica communications · 2025Article
- Transcription of Clock Genes in Medulloblastoma.Cancers · 2025Article
- In-depth inference of transcriptional regulatory networks reveals NPM1 as a therapeutic ribosomal regulator in MYC-amplified medulloblastoma.NPJ precision oncology · 2025Article
- METTL3-mediated activation of Sonic Hedgehog signaling promotes breast cancer progression.Frontiers in cell and developmental biology · 2025Article
- Potential Biomarkers in Myocardial Fibrosis: A Bioinformatic Analysis.Arquivos brasileiros de cardiologia · 2024Article
- High-throughput neural stem cell-based drug screening identifies S6K1 inhibition as a selective vulnerability in sonic hedgehog-medulloblastoma.Neuro-oncology · 2024Article
- Survival-Related Genes on Chromosomes 6 and 17 in Medulloblastoma.International journal of molecular sciences · 2024Article
- scRank infers drug-responsive cell types from untreated scRNA-seq data using a target-perturbed gene regulatory network.Cell reports. Medicine · 2024Article
- EYA2 tyrosine phosphatase inhibition reduces MYC and prevents medulloblastoma progression.Neuro-oncology · 2023Article
- DUBing Primary Tumors of the Central Nervous System: Regulatory Roles of Deubiquitinases.Biomolecules · 2023Review
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Authors and funding
7 authors.
Funding
Abstract
motivationMedulloblastoma (MB) is a brain cancer predominantly arising in children. Roughly 70% of patients are cured today, but survivors often suffer from severe sequelae. MB has been extensively studied by molecular profiling, but often in small and scattered cohorts. To improve cure rates and reduce treatment side effects, accurate integration of such data to increase analytical power will be important, if not essential.
resultsWe have integrated 23 transcription datasets, spanning 1350 MB and 291 normal brain samples. To remove batch effects, we combined the Removal of Unwanted Variation (RUV) method with a novel pipeline for determining empirical negative control genes and a panel of metrics to evaluate normalization performance. The documented approach enabled the removal of a majority of batch effects, producing a large-scale, integrative dataset of MB and cerebellar expression data. The proposed strategy will be broadly applicable for accurate integration of data and incorporation of normal reference samples for studies of various diseases. We hope that the integrated dataset will improve current research in the field of MB by allowing more large-scale gene expression analyses. AVAILABILITY AND IMPLEMENTATION: The RUV-normalized expression data is available through the Gene Expression Omnibus (GEO; https://www.ncbi.nlm.nih.gov/geo/) and can be accessed via the GSE series number GSE124814. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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