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SRIQ clustering : A...
SRIQ clustering : A fusion of Random Forest, QT clustering, and KNN concepts
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- Karlström, Jacob (författare)
- Lund University,Lunds universitet,Bröst/lungcancer,Sektion I,Institutionen för kliniska vetenskaper, Lund,Medicinska fakulteten,Breast/lungcancer,Section I,Department of Clinical Sciences, Lund,Faculty of Medicine
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- Aine, Mattias (författare)
- Lund University,Lunds universitet,LUCC: Lunds universitets cancercentrum,Övriga starka forskningsmiljöer,Bröst/lungcancer,Sektion I,Institutionen för kliniska vetenskaper, Lund,Medicinska fakulteten,LUCC: Lund University Cancer Centre,Other Strong Research Environments,Breast/lungcancer,Section I,Department of Clinical Sciences, Lund,Faculty of Medicine
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- Staaf, Johan (författare)
- Lund University,Lunds universitet,Forskningsgrupp Lungcancer,Forskargrupper vid Lunds universitet,LUCC: Lunds universitets cancercentrum,Övriga starka forskningsmiljöer,Bröst/lungcancer,Sektion I,Institutionen för kliniska vetenskaper, Lund,Medicinska fakulteten,Research Group Lung Cancer,Lund University Research Groups,LUCC: Lund University Cancer Centre,Other Strong Research Environments,Breast/lungcancer,Section I,Department of Clinical Sciences, Lund,Faculty of Medicine
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- Veerla, Srinivas (författare)
- Lund University,Lunds universitet,Forskningsgrupp Lungcancer,Forskargrupper vid Lunds universitet,Bröst- och ovarialcancer,LUCC: Lunds universitets cancercentrum,Övriga starka forskningsmiljöer,Bröst/lungcancer,Sektion I,Institutionen för kliniska vetenskaper, Lund,Medicinska fakulteten,Research Group Lung Cancer,Lund University Research Groups,Breast and Ovarian Cancer Genomics,LUCC: Lund University Cancer Centre,Other Strong Research Environments,Breast/lungcancer,Section I,Department of Clinical Sciences, Lund,Faculty of Medicine
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(creator_code:org_t)
- Elsevier BV, 2022
- 2022
- Engelska 13 s.
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Ingår i: Computational and Structural Biotechnology Journal. - : Elsevier BV. - 2001-0370. ; 20, s. 1567-1579
- Relaterad länk:
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http://dx.doi.org/10... (free)
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https://lup.lub.lu.s...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Gene expression profiling together with unsupervised analysis methods, typically clustering methods, has been used extensively in cancer research to unravel, e.g., new molecular subtypes that hold promise of disease refinement that may ultimately benefit patients. However, many of the commonly used methods require a prespecified number of clusters to extract and frequently require some type of feature pre-selection, e.g. variance filtering. This introduces subjectivity to the process of cluster discovery and the definition of putative novel tumor subtypes. Here, we introduce SRIQ, a novel unsupervised clustering method that could circumvent some of the issues in commonly used unsupervised analysis methods. SRIQ incorporates concepts from random forest machine learning as well as quality threshold- and k-nearest neighbor clustering. It is implemented as a Java and Python pipeline including data pre-processing, differential expression analysis, and pathway analysis. Using 434 lung adenocarcinomas profiled by RNA sequencing, we demonstrate the technical reproducibility of SRIQ and benchmark its performance compared to the commonly used consensus clustering method. Based on differential gene expression analysis and auxiliary molecular data we show that SRIQ can define new tumor subsets that appear biologically relevant and consistent compared and that these new subgroups seem to refine existing transcriptional subtypes that were defined using consensus clustering. Together, this provides support that SRIQ may be a useful new tool for unsupervised analysis of gene expression data from human malignancies.
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Cancer och onkologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Cancer and Oncology (hsv//eng)
Nyckelord
- Clustering
- Gene expression
- KNN
- Lung adenocarcinoma
- Molecular subtypes
- QT clustering
- Random Forest
Publikations- och innehållstyp
- art (ämneskategori)
- ref (ämneskategori)
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