Clustering Algorithms: Their Application to Gene Expression Data
Jelili Oyelade,Itunuoluwa Isewon,Funke Oladipupo,Olufemi Aromolaran,Efosa Uwoghiren,Faridah Ameh,Moses Achas,Ezekiel Adebiyi +7 more
TLDR
This review examines the various clustering algorithms applicable to the gene expression data in order to discover and provide useful knowledge of the appropriate clustering technique that will guarantee stability and high degree of accuracy in its analysis procedure.Abstract:
Gene expression data hide vital information required to understand the biological process that takes place in a particular organism in relation to its environment. Deciphering the hidden patterns in gene expression data proffers a prodigious preference to strengthen the understanding of functional genomics. The complexity of biological networks and the volume of genes present increase the challenges of comprehending and interpretation of the resulting mass of data, which consists of millions of measurements; these data also inhibit vagueness, imprecision, and noise. Therefore, the use of clustering techniques is a first step toward addressing these challenges, which is essential in the data mining process to reveal natural structures and identify interesting patterns in the underlying data. The clustering of gene expression data has been proven to be useful in making known the natural structure inherent in gene expression data, understanding gene functions, cellular processes, and subtypes of cells, mining useful information from noisy data, and understanding gene regulation. The other benefit of clustering gene expression data is the identification of homology, which is very important in vaccine design. This review examines the various clustering algorithms applicable to the gene expression data in order to discover and provide useful knowledge of the appropriate clustering technique that will guarantee stability and high degree of accuracy in its analysis procedure.read more
Citations
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Journal ArticleDOI
SGAClust: Semi-supervised Graph Attraction Clustering of gene expression data
Koyel Mandal,Rosy Sarmah +1 more
TL;DR: A graph-theoretic clustering algorithm called GAClust is proposed which groups co-expressed genes into the same cluster while also detecting noise genes and it has been found that SGAClust outperforms the unsupervised algorithms.
Dissertation
Aplicación y comparativa de cuatro modelos de clustering para datos GTEx
TL;DR: TFM will implement all clustering models, search optimum clusters numbers statistical criteria-based as BIC, AIC and elbow method and, at last, the final partitions generated by those methods are visualized are compared via external validation metrics (measures).
Journal ArticleDOI
Mapping the Complex Transcriptional Landscape of the Phytopathogenic Bacterium Dickeya dadantii
TL;DR: In this paper , a comprehensive and annotated transcriptomic map of D. dadantii was obtained by a computational method combining five independent transcriptomic data sets: (i) paired-end RNA sequencing (RNA-seq) data for a precise reconstruction of the RNA landscape; (ii) DNA microarray data providing transcriptional responses to a broad variety of environmental conditions; (iii) long-read Nanopore native RNA-seq data for iso-form-level transcriptome validation and determination of transcription termination sites; (iv) differential RNA sequencing sequencing (dRNAseq) for the precise mapping of transcription start sites.
Archetypal solution spaces for clustering gene expression datasets in identification of cancer subtypes
TL;DR: In this paper , the authors used energy landscape theory to determine the organization of the solution space for a variety of gene expression datasets using the $K$-means clustering algorithm.
Posted ContentDOI
Multiple latent clusterisation model for the inference of RNA life-cycle kinetic rates from sequencing data
TL;DR: A widespread choral regulation of the three rates is uncovered in the murine fibroblasts to the activation of proto-oncogene MYC, which was not previously observed in this biological system.
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