Journal ArticleDOI
Noise-robust soft clustering of gene expression time-course data
TLDR
To overcome the limitations of hard clustering, this work applied soft clustering which offers several advantages for researchers, including more noise robust and a priori pre-filtering of genes can be avoided.Abstract:
Clustering is an important tool in microarray data analysis. This unsupervised learning technique is commonly used to reveal structures hidden in large gene expression data sets. The vast majority of clustering algorithms applied so far produce hard partitions of the data, i.e. each gene is assigned exactly to one cluster. Hard clustering is favourable if clusters are well separated. However, this is generally not the case for microarray time-course data, where gene clusters frequently overlap. Additionally, hard clustering algorithms are often highly sensitive to noise. To overcome the limitations of hard clustering, we applied soft clustering which offers several advantages for researchers. First, it generates accessible internal cluster structures, i.e. it indicates how well corresponding clusters represent genes. This can be used for the more targeted search for regulatory elements. Second, the overall relation between clusters, and thus a global clustering structure, can be defined. Additionally, soft clustering is more noise robust and a priori pre-filtering of genes can be avoided. This prevents the exclusion of biologically relevant genes from the data analysis. Soft clustering was implemented here using the fuzzy c-means algorithm. Procedures to find optimal clustering parameters were developed. A software package for soft clustering has been developed based on the open-source statistical language R. The package called Mfuzz is freely available.read more
Citations
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Posted ContentDOI
Annelid functional genomics reveal the origins of bilaterian life cycles
TL;DR: In this article , the authors show that trunk development is deferred to pre-metamorphic stages in the feeding larva of O. fusiformis, but starts after gastrulation in the non-feeding larva with gradual metamorphosis of Capitella teleta and the direct developing embryo of Dimorphilus gyrociliatus.
Journal ArticleDOI
In vivo transcriptome analysis provides insights into host-dependent expression of virulence factors by Yersinia entomophaga MH96, during infection of Galleria mellonella.
Amber Rose Paulson,Amber Rose Paulson,Amber Rose Paulson,Maureen O'Callaghan,Xue-Xian Zhang,Paul B. Rainey,Paul B. Rainey,Paul B. Rainey,Mark R. H. Hurst +8 more
TL;DR: In this paper, the authors report the in vivo transcriptome of the entomopathogenic bacterium Yersinia entomophaga MH96, captured during initial, septicemic, and pre-cadaveric stages of intrahemocoelic infection in Galleria mellonella.
Journal ArticleDOI
An N-glycoproteomic site-mapping analysis reveals glycoprotein alterations in esophageal squamous cell carcinoma
Yi Qin Gao,L-F Shen,Tian-Jian Dong,Xin Yang,Heyang Cui,Ya-Xiong Guo,Yan-gang Ma,Pengzhou Kong,Xiaolong Cheng,Ling Zhang,Yongping Cui +10 more
TL;DR: In this article , the authors reported the proteomics and N-glycoproteomic site-mapping analysis of eight pairs of esophageal squamous cell carcinoma (ESCC) tissues and adjacent normal tissues.
Journal ArticleDOI
G protein controls stress readiness by modulating transcriptional and metabolic homeostasis in Arabidopsis thaliana and Marchantia polymorpha.
Ting Wu,S. Krishnamoorthi,Kulaporn Boonyaves,Isam Al-Darabsah,Richalynn Chiu Xian. Leong,Alan M. Jones,Kimitsune Ishizaki,Kang-Ling Liao,Daisuke Urano +8 more
TL;DR: In this paper , the authors compared the basal and salt stress-induced transcriptomic, metabolomic and phenotypic profiles in Gα, Gβ, and XLG-null mutants of two plant species, Arabidopsis thaliana and Marchantia polymorpha, and showed that G protein mediates the shift of transcriptional and metabolic homeostasis to stress readiness status.
Book ChapterDOI
The Human Transcriptome: Implications for Understanding, Diagnosing, and Treating Human Disease
TL;DR: While capturing transcript abundance on genome-wide level has become a routine task thanks to high-throughput technologies, analyzing the transcriptome continues to be a challenging task, since the RNA content of a biological entity is heterogeneous and can vary substantially.
References
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Journal ArticleDOI
Cluster analysis and display of genome-wide expression patterns
TL;DR: A system of cluster analysis for genome-wide expression data from DNA microarray hybridization is described that uses standard statistical algorithms to arrange genes according to similarity in pattern of gene expression, finding in the budding yeast Saccharomyces cerevisiae that clustering gene expression data groups together efficiently genes of known similar function.
Book
Pattern Recognition with Fuzzy Objective Function Algorithms
TL;DR: Books, as a source that may involve the facts, opinion, literature, religion, and many others are the great friends to join with, becomes what you need to get.
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