ArticleGenome biology2022
Widespread redundancy in -omics profiles of cancer mutation states.
Article in Genome biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- CIBRA identifies genomic alterations with a system-wide impact on tumor biology.Bioinformatics (Oxford, England) · 2024Article
- Optimizer's dilemma: optimization strongly influences model selection in transcriptomic prediction.Bioinformatics advances · 2024Article
- Article
- Cross-platform normalization enables machine learning model training on microarray and RNA-seq data simultaneously.Communications biology · 2023Article
- wenda_gpu: fast domain adaptation for genomic data.Bioinformatics (Oxford, England) · 2022Article
- Widespread redundancy in -omics profiles of cancer mutation states.Genome biology · 2022Article
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Authors and funding
4 authors.
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Abstract
backgroundIn studies of cellular function in cancer, researchers are increasingly able to choose from many -omics assays as functional readouts. Choosing the correct readout for a given study can be difficult, and which layer of cellular function is most suitable to capture the relevant signal remains unclear.
resultsWe consider prediction of cancer mutation status (presence or absence) from functional -omics data as a representative problem that presents an opportunity to quantify and compare the ability of different -omics readouts to capture signals of dysregulation in cancer. From the TCGA Pan-Cancer Atlas that contains genetic alteration data, we focus on RNA sequencing, DNA methylation arrays, reverse phase protein arrays (RPPA), microRNA, and somatic mutational signatures as -omics readouts. Across a collection of genes recurrently mutated in cancer, RNA sequencing tends to be the most effective predictor of mutation state. We find that one or more other data types for many of the genes are approximately equally effective predictors. Performance is more variable between mutations than that between data types for the same mutation, and there is little difference between the top data types. We also find that combining data types into a single multi-omics model provides little or no improvement in predictive ability over the best individual data type.
conclusionsBased on our results, for the design of studies focused on the functional outcomes of cancer mutations, there are often multiple -omics types that can serve as effective readouts, although gene expression seems to be a reasonable default option.
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