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Sorting Five Human Tumor Types Reveals Specific Biomarkers and Background Classification Genes
We applied two state-of-the-art, knowledge independent data-mining methods – Dynamic Quantum Clustering (DQC) and t-Distributed Stochastic Neighbor Embedding (t-SNE) – to data from The Cancer Genome Atlas (TCGA). We showed that the RNA expression patterns for a mixture of 2,016 samples from five tum...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5970138/ https://www.ncbi.nlm.nih.gov/pubmed/29802335 http://dx.doi.org/10.1038/s41598-018-26310-x |
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author | Roche, Kimberly E. Weinstein, Marvin Dunwoodie, Leland J. Poehlman, William L. Feltus, Frank A. |
author_facet | Roche, Kimberly E. Weinstein, Marvin Dunwoodie, Leland J. Poehlman, William L. Feltus, Frank A. |
author_sort | Roche, Kimberly E. |
collection | PubMed |
description | We applied two state-of-the-art, knowledge independent data-mining methods – Dynamic Quantum Clustering (DQC) and t-Distributed Stochastic Neighbor Embedding (t-SNE) – to data from The Cancer Genome Atlas (TCGA). We showed that the RNA expression patterns for a mixture of 2,016 samples from five tumor types can sort the tumors into groups enriched for relevant annotations including tumor type, gender, tumor stage, and ethnicity. DQC feature selection analysis discovered 48 core biomarker transcripts that clustered tumors by tumor type. When these transcripts were removed, the geometry of tumor relationships changed, but it was still possible to classify the tumors using the RNA expression profiles of the remaining transcripts. We continued to remove the top biomarkers for several iterations and performed cluster analysis. Even though the most informative transcripts were removed from the cluster analysis, the sorting ability of remaining transcripts remained strong after each iteration. Further, in some iterations we detected a repeating pattern of biological function that wasn’t detectable with the core biomarker transcripts present. This suggests the existence of a “background classification” potential in which the pattern of gene expression after continued removal of “biomarker” transcripts could still classify tumors in agreement with the tumor type. |
format | Online Article Text |
id | pubmed-5970138 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-59701382018-05-30 Sorting Five Human Tumor Types Reveals Specific Biomarkers and Background Classification Genes Roche, Kimberly E. Weinstein, Marvin Dunwoodie, Leland J. Poehlman, William L. Feltus, Frank A. Sci Rep Article We applied two state-of-the-art, knowledge independent data-mining methods – Dynamic Quantum Clustering (DQC) and t-Distributed Stochastic Neighbor Embedding (t-SNE) – to data from The Cancer Genome Atlas (TCGA). We showed that the RNA expression patterns for a mixture of 2,016 samples from five tumor types can sort the tumors into groups enriched for relevant annotations including tumor type, gender, tumor stage, and ethnicity. DQC feature selection analysis discovered 48 core biomarker transcripts that clustered tumors by tumor type. When these transcripts were removed, the geometry of tumor relationships changed, but it was still possible to classify the tumors using the RNA expression profiles of the remaining transcripts. We continued to remove the top biomarkers for several iterations and performed cluster analysis. Even though the most informative transcripts were removed from the cluster analysis, the sorting ability of remaining transcripts remained strong after each iteration. Further, in some iterations we detected a repeating pattern of biological function that wasn’t detectable with the core biomarker transcripts present. This suggests the existence of a “background classification” potential in which the pattern of gene expression after continued removal of “biomarker” transcripts could still classify tumors in agreement with the tumor type. Nature Publishing Group UK 2018-05-25 /pmc/articles/PMC5970138/ /pubmed/29802335 http://dx.doi.org/10.1038/s41598-018-26310-x Text en © The Author(s) 2018 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Roche, Kimberly E. Weinstein, Marvin Dunwoodie, Leland J. Poehlman, William L. Feltus, Frank A. Sorting Five Human Tumor Types Reveals Specific Biomarkers and Background Classification Genes |
title | Sorting Five Human Tumor Types Reveals Specific Biomarkers and Background Classification Genes |
title_full | Sorting Five Human Tumor Types Reveals Specific Biomarkers and Background Classification Genes |
title_fullStr | Sorting Five Human Tumor Types Reveals Specific Biomarkers and Background Classification Genes |
title_full_unstemmed | Sorting Five Human Tumor Types Reveals Specific Biomarkers and Background Classification Genes |
title_short | Sorting Five Human Tumor Types Reveals Specific Biomarkers and Background Classification Genes |
title_sort | sorting five human tumor types reveals specific biomarkers and background classification genes |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5970138/ https://www.ncbi.nlm.nih.gov/pubmed/29802335 http://dx.doi.org/10.1038/s41598-018-26310-x |
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