Consensus Clustering Using Weighted Association

2016 ◽  
Author(s):  
Aparajita Nanda ◽  
Arun K. Pujari
Keyword(s):  
Consensus Clustering

scholarly journals DNA Methylation Based Molecular Subtypes Predict Prognosis in Breast Cancer Patients

2021 ◽  
Vol 28 ◽  
pp. 107327482098851
Author(s):  
Zeng-Hong Wu ◽  
Yun Tang ◽  
Yan Zhou
Keyword(s):  
Breast Cancer ◽  
Dna Methylation ◽  
Molecular Subtypes ◽  
Methylation Status ◽  
The Cancer Genome Atlas ◽  
Training Dataset ◽  
Consensus Clustering ◽  
Breast Cancer Patients ◽  
Cancer Subtypes ◽  
Novel Biomarkers

Background: Epigenetic changes are tightly linked to tumorigenesis development and malignant transformation’ However, DNA methylation occurs earlier and is constant during tumorigenesis. It plays an important role in controlling gene expression in cancer cells. Methods: In this study, we determining the prognostic value of molecular subtypes based on DNA methylation status in breast cancer samples obtained from The Cancer Genome Atlas database (TCGA). Results: Seven clusters and 204 corresponding promoter genes were identified based on consensus clustering using 166 CpG sites that significantly influenced survival outcomes. The overall survival (OS) analysis showed a significant prognostic difference among the 7 groups (p<0.05). Finally, a prognostic model was used to estimate the results of patients on the testing set based on the classification findings of a training dataset DNA methylation subgroups. Conclusions: The model was found to be important in the identification of novel biomarkers and could be of help to patients with different breast cancer subtypes when predicting prognosis, clinical diagnosis and management.

scholarly journals N6-Methyladenosine RNA modification in cerebrospinal fluid as a novel potential diagnostic biomarker for progressive multiple sclerosis

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Fei Ye ◽  
Tianzhu Wang ◽  
Xiaoxin Wu ◽  
Jie Liang ◽  
Jiaoxing Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Sclerosis ◽  
Clustering Analysis ◽  
Roc Curves ◽  
Progressive Multiple Sclerosis ◽  
Rna Modification ◽  
Diagnostic Model ◽  
Consensus Clustering ◽  
Rna Methylation ◽  
M6a Rna Methylation

Abstract Background Progressive multiple sclerosis (PMS) is an uncommon and severe subtype of MS that worsens gradually and leads to irreversible disabilities in young adults. Currently, there are no applicable or reliable biomarkers to distinguish PMS from relapsing–remitting multiple sclerosis (RRMS). Previous studies have demonstrated that dysfunction of N6-methyladenosine (m6A) RNA modification is relevant to many neurological disorders. Thus, the aim of this study was to explore the diagnostic biomarkers for PMS based on m6A regulatory genes in the cerebrospinal fluid (CSF). Methods Gene expression matrices were downloaded from the ArrayExpress database. Then, we identified differentially expressed m6A regulatory genes between MS and non-MS patients. MS clusters were identified by consensus clustering analysis. Next, we analyzed the correlation between clusters and clinical characteristics. The random forest (RF) algorithm was applied to select key m6A-related genes. The support vector machine (SVM) was then used to construct a diagnostic gene signature. Receiver operating characteristic (ROC) curves were plotted to evaluate the accuracy of the diagnostic model. In addition, CSF samples from MS and non-MS patients were collected and used for external validation, as evaluated by an m6A RNA Methylation Quantification Kit and by real-time quantitative polymerase chain reaction. Results The 13 central m6A RNA methylation regulators were all upregulated in MS patients when compared with non-MS patients. Consensus clustering analysis identified two clusters, both of which were significantly associated with MS subtypes. Next, we divided 61 MS patients into a training set (n = 41) and a test set (n = 20). The RF algorithm identified eight feature genes, and the SVM method was successfully applied to construct a diagnostic model. ROC curves revealed good performance. Finally, the analysis of 11 CSF samples demonstrated that RRMS samples exhibited significantly higher levels of m6A RNA methylation and higher gene expression levels of m6A-related genes than PMS samples. Conclusions The dynamic modification of m6A RNA methylation is involved in the progression of MS and could potentially represent a novel CSF biomarker for diagnosing MS and distinguishing PMS from RRMS in the early stages of the disease.

scholarly journals Protein interactions and consensus clustering analysis uncover insights into herpesvirus virion structure and function relationships

PLoS Biology ◽  
2019 ◽  
Vol 17 (6) ◽  
pp. e3000316 ◽  
Author(s):  
Anna Hernández Durán ◽  
Todd M. Greco ◽  
Benjamin Vollmer ◽  
Ileana M. Cristea ◽  
Kay Grünewald ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Clustering Analysis ◽  
Structure And Function ◽  
Consensus Clustering ◽  
Virion Structure ◽  
And Function

Probabilistic consensus clustering using evidence accumulation

2013 ◽  
Vol 98 (1-2) ◽  
pp. 331-357 ◽  
Author(s):  
André Lourenço ◽  
Samuel Rota Bulò ◽  
Nicola Rebagliati ◽  
Ana L. N. Fred ◽  
Mário A. T. Figueiredo ◽  
...  
Keyword(s):  
Consensus Clustering ◽  
Evidence Accumulation

Prognosis-associated methylation subtypes in endometrial carcinoma patients and the role of magnetic nanoparticles in gene extraction

2021 ◽  
Vol 11 (8) ◽  
pp. 1288-1298
Author(s):  
Liang Wang ◽  
Fengxia Xue
Keyword(s):  
Endometrial Cancer ◽  
Magnetic Nanoparticles ◽  
Endometrial Carcinoma ◽  
High Throughput Sequencing ◽  
Risk Groups ◽  
Vital Role ◽  
Low Risk ◽  
The Cancer Genome Atlas ◽  
Consensus Clustering ◽  
Tumor Subtypes

Endometrial cancer is one of the most common gynecological malignancies, and DNA methylation plays a vital role in its occurrence and development. In this study, we collected the relevant data on endometrial cancer from the Cancer Genome Atlas database and UCSC website. By screening and processing the data, we obtained 410 samples and 16,381 methylation sites. Endometrial carcinoma can be divided into seven molecular subtypes using consensus clustering method. Based on the analysis of the differences among subtypes, the methylation degree of different sites was obtained, and the prognosis model of methylation sites was established. Based on the median value of the train group, the train and test groups were divided into high and low-risk groups. The survival between the high and low-risk groups was different. It also showed that this model can predict the survival of patients, with better accuracy. In conclusion, the tumor subtypes based on methylation sites can provide a better guidance for treatment, relapse, and prognosis of endometrial cancer. In this study, magnetic nanoparticles can be used to extract genomic DNA and total RNA due to their paramagnetism and biocompatibility, then transcriptome high-throughput sequencing was performed. It may serve as potential cancer immune biomarker targets for developing future oncological treatments.

Development and Validation of a Novel M6A-related LncRNA Prognostic Signature Associated with Immune Microenvironment for Ovarian Serous Cystadenocarcinoma

2021 ◽  
Author(s):  
Rui Geng ◽  
Tian Chen ◽  
Zihang Zhong ◽  
Senmiao Ni ◽  
Jianling Bai ◽  
...  
Keyword(s):  
Immune Modulation ◽  
Cox Regression ◽  
Great Influence ◽  
Clinical Samples ◽  
Consensus Clustering ◽  
Prognostic Signature ◽  
Cancer Stemness ◽  
Gynecological Malignancy ◽  
Pan Cancer ◽  
Geo Database

Abstract Background: OV is the most lethal gynecological malignancy. M6A and lncRNAs have great influence on OV development and patients' immunotherapy response. Here, we decided to establish a reliable signature in the light of mRLs. Method: The lncRNAs associated with m6A in OV were analyzed and obtained by co-expression analysis in the light of TCGA-OV database. Univariate, LASSO and multivariate Cox regression analyses were employed to establish the model in the light of the mRLs. K-M analysis, PCA, GSEA, and nomogram based on the TCGA-OV and GEO database were conducted to prove the predictive value and independence of the model. The underlying relationship between the model and TME and cancer stemness properties were further investigated through immune features comparison, consensus clustering analysis, and Pan-cancer analysis.Results: A prognostic signature comprising four mRLs: WAC-AS1, LINC00997, DNM3OS, and FOXN3-AS1, was constructed and verified for OV according to TCGA and GEO database. The expressions of the four mRLs were confirmed by qRT-PCR in clinical samples. Applying this signature, people can identify patients more effectively. All the sample were assigned into two clusters, and the clusters had different overall survival, clinical features, and tumor microenvironment. Finally, Pan-cancer analysis further demonstrated the four mRLs significantly related to immune infiltration, TME and cancer stemness properties in various cancer types. Conclusion: This study provided an accurate prognostic signature for patients with OV and elucidated the potential mechanism of the mRLs in immune modulation and treatment response, giving new insights into identifying new therapeutic targets.

Hypernatremia subgroups among hospitalized patients by machine learning consensus clustering with different patient survival

2021 ◽  
Author(s):  
Charat Thongprayoon ◽  
Michael A. Mao ◽  
Mira T. Keddis ◽  
Andrea G. Kattah ◽  
Grace Y. Chong ◽  
...  
Keyword(s):  
Machine Learning ◽  
Patient Survival ◽  
Hospitalized Patients ◽  
Consensus Clustering

Consensus Clustering for Binning Metagenome Sequences

2017 ◽  
pp. 273-284
Author(s):  
Isis Bonet ◽  
Adriana Escobar ◽  
Andrea Mesa-Múnera ◽  
Juan Fernando Alzate
Keyword(s):  
Consensus Clustering

ENTROPY-BASED CLUSTER VALIDATION AND ESTIMATION OF THE NUMBER OF CLUSTERS IN GENE EXPRESSION DATA

2012 ◽  
Vol 10 (05) ◽  
pp. 1250011
Author(s):  
NATALIA NOVOSELOVA ◽  
IGOR TOM
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Clustering Algorithm ◽  
Selection Procedure ◽  
Biological Knowledge ◽  
Consensus Clustering ◽  
Expression Data ◽  
Cluster Validation ◽  
Number Of Clusters ◽  
Validity Measure

Many external and internal validity measures have been proposed in order to estimate the number of clusters in gene expression data but as a rule they do not consider the analysis of the stability of the groupings produced by a clustering algorithm. Based on the approach assessing the predictive power or stability of a partitioning, we propose the new measure of cluster validation and the selection procedure to determine the suitable number of clusters. The validity measure is based on the estimation of the "clearness" of the consensus matrix, which is the result of a resampling clustering scheme or consensus clustering. According to the proposed selection procedure the stable clustering result is determined with the reference to the validity measure for the null hypothesis encoding for the absence of clusters. The final number of clusters is selected by analyzing the distance between the validity plots for initial and permutated data sets. We applied the selection procedure to estimate the clustering results on several datasets. As a result the proposed procedure produced an accurate and robust estimate of the number of clusters, which are in agreement with the biological knowledge and gold standards of cluster quality.

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