Clustering analysis of microarray gene expression data by splitting algorithm

Wang, Ruye; Scharenbroich, Lucas; Hart, Christopher E.; Wold, B; Mjolsness, Eric

doi:10.1016/s0743-7315(03)00085-6

Cited by 8 publications

(3 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To verify the results, the colony density was digitized by image processing and the growth score value (GSV) of a deletion strain was calculated as a ratio of colony density of treated over untreated normalized with the wt treated over untreated ratio (S1). The GSV’s were cluster-analyzed (Wang et al 2003) at a genomic scale to confirm sensitive and resistant strains (S1-S3). As a result, 300 fission yeast mutants are identified BHA sensitive, 15 strains are BHT sensitive, and 117 strains are BPA sensitive (Figure 1A, S10).…”

Section: Resultsmentioning

confidence: 99%

Comparative Genomic Screen in Two Yeasts Reveals Conserved Pathways in the Response Network to Phenol Stress

Alhoch

Chen

Chan

et al. 2019

G3 Genes|Genomes|Genetics

Self Cite

View full text Add to dashboard Cite

Living organisms encounter various perturbations, and response mechanisms to such perturbations are vital for species survival. Defective stress responses are implicated in many human diseases including cancer and neurodegenerative disorders. Phenol derivatives, naturally occurring and synthetic, display beneficial as well as detrimental effects. The phenol derivatives in this study, butylated hydroxyanisole (BHA), butylated hydroxytoluene (BHT), and bisphenol A (BPA), are widely used as food preservatives and industrial chemicals. Conflicting results have been reported regarding their biological activity and correlation with disease development; understanding the molecular basis of phenol action is a key step for addressing issues relevant to human health. This work presents the first comparative genomic analysis of the genetic networks for phenol stress response in an evolutionary context of two divergent yeasts, Schizosaccharomyces pombe and Saccharomyces cerevisiae . Genomic screening of deletion strain libraries of the two yeasts identified genes required for cellular response to phenol stress, which are enriched in human orthologs. Functional analysis of these genes uncovered the major signaling pathways involved. The results provide a global view of the biological events constituting the defense process, including cell cycle arrest, DNA repair, phenol detoxification by V-ATPases, reactive oxygen species alleviation, and endoplasmic reticulum stress relief through ergosterol and the unfolded protein response, revealing novel roles for these cellular pathways.

show abstract

Section: Resultsmentioning

confidence: 99%

Comparative Genomic Screen in Two Yeasts Reveals Conserved Pathways in the Response Network to Phenol Stress

Alhoch

Chen

Chan

et al. 2019

G3 Genes|Genomes|Genetics

Self Cite

View full text Add to dashboard Cite

show abstract

“…6,17 Hierarchical clustering techniques are extensively used in microarray data analysis, which combines all data points into a single set which are placed adjacent to each other in the feature space. 11 At present, hierarchical clustering is the most often used method for grouping data. 13,14 The main objective of hierarchical clustering is to obtain a best cluster that will signify a set of patterns in the background of a given distance metric.…”

Section: Introductionmentioning

confidence: 99%

A Fast Quad-Tree Based Two Dimensional Hierarchical Clustering

Rajadurai

Sankaranarayanan

2012

Bioinform Biol Insights

View full text Add to dashboard Cite

Recently, microarray technologies have become a robust technique in the area of genomics. An important step in the analysis of gene expression data is the identification of groups of genes disclosing analogous expression patterns. Cluster analysis partitions a given dataset into groups based on specified features. Euclidean distance is a widely used similarity measure for gene expression data that considers the amount of changes in gene expression. However, the huge number of genes and the intricacy of biological networks have highly increased the challenges of comprehending and interpreting the resulting group of data, increasing processing time. The proposed technique focuses on a QT based fast 2-dimensional hierarchical clustering algorithm to perform clustering. The construction of the closest pair data structure is an each level is an important time factor, which determines the processing time of clustering. The proposed model reduces the processing time and improves analysis of gene expression data.

show abstract

“…Clustering techniques produce a rich taxonomy of results by defining groups of genes that act more or less similarly across a number of experimental conditions. The diverse approaches to clustering genes by expression levels include k-means [ 1 ], self-organizing maps [ 2 ], hierarchical algorithms [ 3 , 4 ] and probabilistic models [ 5 ]. Some approaches permit clustering of the conditions as well [ 6 - 8 ].…”

Section: Introductionmentioning

confidence: 99%

Mining gene expression data by interpreting principal components

et al. 2006

View full text Add to dashboard Cite

Background: There are many methods for analyzing microarray data that group together genes having similar patterns of expression over all conditions tested. However, in many instances the biologically important goal is to identify relatively small sets of genes that share coherent expression across only some conditions, rather than all or most conditions as required in traditional clustering; e.g. genes that are highly up-regulated and/or down-regulated similarly across only a subset of conditions. Equally important is the need to learn which conditions are the decisive ones in forming such gene sets of interest, and how they relate to diverse conditional covariates, such as disease diagnosis or prognosis.

show abstract

Clustering analysis of microarray gene expression data by splitting algorithm

Cited by 8 publications

References 9 publications

Comparative Genomic Screen in Two Yeasts Reveals Conserved Pathways in the Response Network to Phenol Stress

Comparative Genomic Screen in Two Yeasts Reveals Conserved Pathways in the Response Network to Phenol Stress

A Fast Quad-Tree Based Two Dimensional Hierarchical Clustering

Mining gene expression data by interpreting principal components

Contact Info

Product

Resources

About