ArticleBMC medical genomics2019
Deconvolution of transcriptomes and miRNomes by independent component analysis provides insights into biological processes and clinical outcomes of melanoma patients.
Article in BMC medical genomics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Omics in Cutaneous Melanoma.Asian Pacific journal of cancer prevention : APJCP · 2025Review
- iModulonMiner and PyModulon: Software for unsupervised mining of gene expression compendia.PLoS computational biology · 2024Article
- Early-life influenza A (H1N1) infection independently programs brain connectivity, HPA AXIS and tissue-specific gene expression profiles.Scientific reports · 2024Article
- DECOMICS, a shiny application for unsupervised cell type deconvolution and biological interpretation of bulk omic data.Bioinformatics advances · 2024Article
- consICA: an R package for robust reference-free deconvolution of multi-omics data.Bioinformatics advances · 2024Article
- Extracellular Vesicle Secretion by Leukemia Cells In Vivo Promotes CLL Progression by Hampering Antitumor T-cell Responses.Blood cancer discovery · 2023Article
- Multi-omics data integration and modeling unravels new mechanisms for pancreatic cancer and improves prognostic prediction.NPJ precision oncology · 2022Article
- In silico Approach for Validating and Unveiling New Applications for Prognostic Biomarkers of Endometrial Cancer.Cancers · 2021Article
- DECONbench: a benchmarking platform dedicated to deconvolution methods for tumor heterogeneity quantification.BMC bioinformatics · 2021Article
- A DNA Repair and Cell Cycle Gene Expression Signature in Pediatric High-Grade Gliomas: Prognostic and Therapeutic Value.Cancers · 2021Article
- Integrative approaches for analysis of mRNA and microRNA high-throughput data.Computational and structural biotechnology journal · 2021Review
- Meta-Analysis of Esophageal Cancer Transcriptomes Using Independent Component Analysis.Frontiers in genetics · 2021Article
- Patient-derived organoids and orthotopic xenografts of primary and recurrent gliomas represent relevant patient avatars for precision oncology.Acta neuropathologica · 2020Article
- Reference-free deconvolution, visualization and interpretation of complex DNA methylation data using DecompPipeline, MeDeCom and FactorViz.Nature protocols · 2020Article
- Factorial study of the RNA-seq computational workflow identifies biases as technical gene signatures.NAR genomics and bioinformatics · 2020Article
- Metabolic Traits in Cutaneous Melanoma.Frontiers in oncology · 2020Review
- Independent Component Analysis for Unraveling the Complexity of Cancer Omics Datasets.International journal of molecular sciences · 2019Review
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9 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundThe amount of publicly available cancer-related "omics" data is constantly growing and can potentially be used to gain insights into the tumour biology of new cancer patients, their diagnosis and suitable treatment options. However, the integration of different datasets is not straightforward and requires specialized approaches to deal with heterogeneity at technical and biological levels.
methodsHere we present a method that can overcome technical biases, predict clinically relevant outcomes and identify tumour-related biological processes in patients using previously collected large discovery datasets. The approach is based on independent component analysis (ICA) - an unsupervised method of signal deconvolution. We developed parallel consensus ICA that robustly decomposes transcriptomics datasets into expression profiles with minimal mutual dependency.
resultsBy applying the method to a small cohort of primary melanoma and control samples combined with a large discovery melanoma dataset, we demonstrate that our method distinguishes cell-type specific signals from technical biases and allows to predict clinically relevant patient characteristics. We showed the potential of the method to predict cancer subtypes and estimate the activity of key tumour-related processes such as immune response, angiogenesis and cell proliferation. ICA-based risk score was proposed and its connection to patient survival was validated with an independent cohort of patients. Additionally, through integration of components identified for mRNA and miRNA data, the proposed method helped deducing biological functions of miRNAs, which would otherwise not be possible.
conclusionsWe present a method that can be used to map new transcriptomic data from cancer patient samples onto large discovery datasets. The method corrects technical biases, helps characterizing activity of biological processes or cell types in the new samples and provides the prognosis of patient survival.
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