ArticleBioinformatics (Oxford, England)2020
Efficient weighted univariate clustering maps outstanding dysregulated genomic zones in human cancers.
Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
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- STREAK: A supervised cell surface receptor abundance estimation strategy for single cell RNA-sequencing data using feature selection and thresholded gene set scoring.PLoS computational biology · 2023Article
- Optimal Sparse Regression Trees.Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence · 2023Article
- SPECK: an unsupervised learning approach for cell surface receptor abundance estimation for single-cell RNA-sequencing data.Bioinformatics advances · 2023Article
- Detecting genetic epistasis by differential departure from independence.Molecular genetics and genomics : MGG · 2022Article
- Surface boulder banding indicates Martian debris-covered glaciers formed over multiple glaciations.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Olfaction-Related Gene Expression in the Antennae of Female Mosquitoes From CommonFrontiers in physiology · 2021Article
- Identification of new driver and passenger mutations within APOBEC-induced hotspot mutations in bladder cancer.Genome medicine · 2020Article
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2 authors.
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Abstract
motivationChromosomal patterning of gene expression in cancer can arise from aneuploidy, genome disorganization or abnormal DNA methylation. To map such patterns, we introduce a weighted univariate clustering algorithm to guarantee linear runtime, optimality and reproducibility.
resultsWe present the chromosome clustering method, establish its optimality and runtime and evaluate its performance. It uses dynamic programming enhanced with an algorithm to reduce search-space in-place to decrease runtime overhead. Using the method, we delineated outstanding genomic zones in 17 human cancer types. We identified strong continuity in dysregulation polarity-dominance by either up- or downregulated genes in a zone-along chromosomes in all cancer types. Significantly polarized dysregulation zones specific to cancer types are found, offering potential diagnostic biomarkers. Unreported previously, a total of 109 loci with conserved dysregulation polarity across cancer types give insights into pan-cancer mechanisms. Efficient chromosomal clustering opens a window to characterize molecular patterns in cancer genome and beyond. AVAILABILITY AND IMPLEMENTATION: Weighted univariate clustering algorithms are implemented within the R package 'Ckmeans.1d.dp' (4.0.0 or above), freely available at https://cran.r-project.org/package=Ckmeans.1d.dp. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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