ArticleCommunications biology2024
Procrustes is a machine-learning approach that removes cross-platform batch effects from clinical RNA sequencing data.
Article in Communications biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 12 citations in OpenAlex.
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- WBT-DC pipeline: a cross-cohort and cross-platform disease classification pipeline based on whole-blood transcriptomics.Journal of translational medicine · 2026Article
- Biomarker identification of triple negative breast cancer subtypes using machine learning.NPJ systems biology and applications · 2026Article
- Lineage infidelity in FH-deficient RCC with secondary somatic alterations: a case report and implications for diagnosis and treatment.Therapeutic advances in medical oncology · 2026Article
- Explainable machine learning-based identification of transcriptomic biomarkers in CD1c+ dendritic cells for non-infectious uveitis: an integrative analysis of bulk RNA-seq data.Frontiers in bioinformatics · 2026Article
- Identification of therapeutic targets for renal medullary carcinoma via integrated genomic and transcriptomic profiling.Cell reports. Medicine · 2025Article
- Integrative RNA-seq analysis reveals immune-related hub genesInternational journal of reproductive biomedicine · 2025Article
- Artificial intelligence in bioinformatics: a survey.Briefings in bioinformatics · 2025Review
- RNA sequencing and immunohistochemistry jointly improve tumor biomarker interpretation.Scientific reports · 2025Article
- Clinical and analytical validation of a combined RNA and DNA exome assay across a large tumor cohort.Communications medicine · 2025Article
- A novel case of glial transdifferentiation in renal medullary carcinoma brain metastasis.Acta neuropathologica communications · 2025Article
- Highly effective batch effect correction method for RNA-seq count data.Computational and structural biotechnology journal · 2025Article
- Procrustes is a machine-learning approach that removes cross-platform batch effects from clinical RNA sequencing data.Communications biology · 2024Article
Corrections and comments
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Authors and funding
25 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the increased use of gene expression profiling for personalized oncology, optimized RNA sequencing (RNA-seq) protocols and algorithms are necessary to provide comparable expression measurements between exome capture (EC)-based and poly-A RNA-seq. Here, we developed and optimized an EC-based protocol for processing formalin-fixed, paraffin-embedded samples and a machine-learning algorithm, Procrustes, to overcome batch effects across RNA-seq data obtained using different sample preparation protocols like EC-based or poly-A RNA-seq protocols. Applying Procrustes to samples processed using EC and poly-A RNA-seq protocols showed the expression of 61% of genes (N = 20,062) to correlate across both protocols (concordance correlation coefficient > 0.8, versus 26% before transformation by Procrustes), including 84% of cancer-specific and cancer microenvironment-related genes (versus 36% before applying Procrustes; N = 1,438). Benchmarking analyses also showed Procrustes to outperform other batch correction methods. Finally, we showed that Procrustes can project RNA-seq data for a single sample to a larger cohort of RNA-seq data. Future application of Procrustes will enable direct gene expression analysis for single tumor samples to support gene expression-based treatment decisions.
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