ArticleBMC bioinformatics2019
Shambhala: a platform-agnostic data harmonizer for gene expression data.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- PXN Unlocks the Power of Public Gene Expression Data Through Cross-Technology Integration.bioRxiv : the preprint server for biology · 2026Article
- Depiction of neuroendocrine features associated with immunotherapy response using a novel one-class predictor in lung adenocarcinoma.Discover oncology · 2023Article
- Uniformly shaped harmonization combines human transcriptomic data from different platforms while retaining their biological properties and differential gene expression patterns.Frontiers in molecular biosciences · 2023Article
- Large-scale assessment of pros and cons of autopsy-derived or tumor-matched tissues as the norms for gene expression analysis in cancers.Computational and structural biotechnology journal · 2023Article
- Personalized targeted therapy prescription in colorectal cancer using algorithmic analysis of RNA sequencing data.BMC cancer · 2022Article
- Transcriptomic Harmonization as the Way for Suppressing Cross-Platform Bias and Batch Effect.Biomedicines · 2022Review
- ENIGMA HALFpipe: Interactive, reproducible, and efficient analysis for resting-state and task-based fMRI data.Human brain mapping · 2022Review
- Transcriptomic Portraits and Molecular Pathway Activation Features of Adult Spinal Intramedullary Astrocytomas.Frontiers in oncology · 2022Article
- Article
- Bioinformatics Analysis Reveals Crosstalk Among Platelets, Immune Cells, and the Glomerulus That May Play an Important Role in the Development of Diabetic Nephropathy.Frontiers in medicine · 2021Article
- Medical genomics at the Systems Biology and Bioinformatics (SBB-2019) school.BMC medical genomics · 2020Article
- Cancer gene expression profiles associated with clinical outcomes to chemotherapy treatments.BMC medical genomics · 2020Article
- RNA Sequencing in Comparison to Immunohistochemistry for Measuring Cancer Biomarkers in Breast Cancer and Lung Cancer Specimens.Biomedicines · 2020Article
- RNA sequencing profiles and diagnostic signatures linked with response to ramucirumab in gastric cancer.Cold Spring Harbor molecular case studies · 2020Article
- Flexible Data Trimming Improves Performance of Global Machine Learning Methods in Omics-Based Personalized Oncology.International journal of molecular sciences · 2020Article
- New Paradigm of Machine Learning (ML) in Personalized Oncology: Data Trimming for Squeezing More Biomarkers From Clinical Datasets.Frontiers in oncology · 2019Article
- Gene Expression Value Prediction Based on XGBoost Algorithm.Frontiers in genetics · 2019Article
- High-Throughput Mutation Data Now Complement Transcriptomic Profiling: Advances in Molecular Pathway Activation Analysis Approach in Cancer Biology.Cancer informatics · 2019Article
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8 authors.
Funding
Abstract
backgroundHarmonization techniques make different gene expression profiles and their sets compatible and ready for comparisons. Here we present a new bioinformatic tool termed Shambhala for harmonization of multiple human gene expression datasets obtained using different experimental methods and platforms of microarray hybridization and RNA sequencing.
resultsUnlike previously published methods enabling good quality data harmonization for only two datasets, Shambhala allows conversion of multiple datasets into the universal form suitable for further comparisons. Shambhala harmonization is based on the calibration of gene expression profiles using the auxiliary standardization dataset. Each profile is transformed to make it similar to the output of microarray hybridization platform Affymetrix Human Gene. This platform was chosen because it has the biggest number of human gene expression profiles deposited in public databases. We evaluated Shambhala ability to retain biologically important features after harmonization. The same four biological samples taken in multiple replicates were profiled independently using three and four different experimental platforms, respectively, then Shambhala-harmonized and investigated by hierarchical clustering.
conclusionOur results showed that unlike other frequently used methods: quantile normalization and DESeq/DESeq2 normalization, Shambhala harmonization was the only method supporting sample-specific and platform-independent biologically meaningful clustering for the data obtained from multiple experimental platforms.
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