Clustering Analysis of SAGE Transcription Profiles Using a Poisson Approach

2008 ◽  
pp. 185-198 ◽  
Author(s):  
Haiyan Huang ◽  
Li Cai ◽  
Wing H. Wong
Keyword(s):  
Clustering Analysis ◽  
Transcription Profiles

scholarly journals DNA Microarray Comparisons of Plant Factor- and Nitrogen Deprivation-Induced Hormogonia Reveal Decision-Making Transcriptional Regulation Patterns in Nostoc punctiforme

2008 ◽  
Vol 190 (22) ◽  
pp. 7382-7391 ◽  
Author(s):  
Elsie L. Campbell ◽  
Harry Christman ◽  
John C. Meeks
Keyword(s):  
Dna Microarray ◽  
Clustering Analysis ◽  
Time Course ◽  
Global Changes ◽  
Nitrogen Deprivation ◽  
Nostoc Punctiforme ◽  
Growth State ◽  
Transcription Profiles ◽  
Negative Factors ◽  
Gliding Mechanism

ABSTRACT Hormogonia are nongrowing filaments, motile by means of a gliding mechanism, that are produced by certain cyanobacteria. Their differentiation is induced by positive and negative factors for growth, such as deprivation of combined nitrogen (nitrogen stress induction [NSI]). In Nostoc punctiforme, they are also induced by the exudate (hormogonium-inducing factor [HIF]) of a symbiotic plant partner. Time course (0.5 to 24 h) transcription profiles were determined by DNA microarray assays for hormogonia of N. punctiforme following induction by HIF and NSI. Clustering analysis revealed both common and distinct transcriptional patterns for the two methods of induction. By 24 h, a common set of 1,328 genes was identified. This 24-h common set of genes arose by the transition of 474 genes from an 819-member common set of genes at 1 h after induction; 405 and 51 genes unique to the HIF and NSI groups at 1 h, respectively; and 398 genes differentially transcribed at later time points. The NSI hormogonia showed a transcriptional checkpoint at 12 h following induction in which up- and downregulated genes were transiently down- or upregulated, respectively. The transient changes in these 1,043 genes appeared to reflect a switch back to a vegetative growth state. Such a checkpoint was not seen in HIF hormogonia. Genes uniquely upregulated in HIF hormogonia included those encoding proteins hypothesized to synthesize a metabolite repressor of hormogonium differentiation. Approximately 34 to 42% of the 6,893 printed genes were differentially transcribed during hormogonium differentiation; about half of those genes were upregulated, and 1,034 genes responded within 0.5 h after induction. These collective results indicate extensive and rapid global changes in the transcription of specific genes during the differentiation of these specialized filaments.

2249-PUB: Clustering Analysis as a Tool to Define Endotypes in Type 1 Diabetes (T1D)

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 2249-PUB
Author(s):  
ALEJANDRO F. SILLER ◽  
XIANGJUN GU ◽  
MUSTAFA TOSUR ◽  
MARCELA ASTUDILLO ◽  
ASHOK BALASUBRAMANYAM ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Clustering Analysis

A Level Inference Method for Aggregated Information of Objects Based on Clustering Analysis

2012 ◽  
Vol 34 (6) ◽  
pp. 1432-1437 ◽  
Author(s):  
Li-feng Cao ◽  
Xing-yuan Chen ◽  
Xue-hui Du ◽  
Chun-tao Xia
Keyword(s):  
Clustering Analysis ◽  
Inference Method

1p19q Gene Transcription Profiles Closely Correlated to Malignancy and Prognosis of 1p/19q Non-Codeletion Gliomas

2018 ◽  
Author(s):  
Rui-Chao Chai ◽  
Ke-Nan Zhang ◽  
Fan Wu ◽  
Yu-Qing Liu ◽  
Zheng Zhao ◽  
...  
Keyword(s):  
Gene Transcription ◽  
Transcription Profiles

Clustering Count-based RNA Methylation Data Using a Nonparametric Generative Model

2018 ◽  
Vol 14 (1) ◽  
pp. 11-23 ◽  
Author(s):  
Lin Zhang ◽  
Yanling He ◽  
Huaizhi Wang ◽  
Hui Liu ◽  
Yufei Huang ◽  
...  
Keyword(s):  
Clustering Analysis ◽  
Methylation Level ◽  
Optimal Number ◽  
Generative Model ◽  
Methylation Data ◽  
Sequencing Data ◽  
Number Of Clusters ◽  
Rna Methylation ◽  
Clustering Effect ◽  
Optimal Number Of Clusters

Background: RNA methylome has been discovered as an important layer of gene regulation and can be profiled directly with count-based measurements from high-throughput sequencing data. Although the detailed regulatory circuit of the epitranscriptome remains uncharted, clustering effect in methylation status among different RNA methylation sites can be identified from transcriptome-wide RNA methylation profiles and may reflect the epitranscriptomic regulation. Count-based RNA methylation sequencing data has unique features, such as low reads coverage, which calls for novel clustering approaches. <P><P> Objective: Besides the low reads coverage, it is also necessary to keep the integer property to approach clustering analysis of count-based RNA methylation sequencing data. <P><P> Method: We proposed a nonparametric generative model together with its Gibbs sampling solution for clustering analysis. The proposed approach implements a beta-binomial mixture model to capture the clustering effect in methylation level with the original count-based measurements rather than an estimated continuous methylation level. Besides, it adopts a nonparametric Dirichlet process to automatically determine an optimal number of clusters so as to avoid the common model selection problem in clustering analysis. <P><P> Results: When tested on the simulated system, the method demonstrated improved clustering performance over hierarchical clustering, K-means, MClust, NMF and EMclust. It also revealed on real dataset two novel RNA N6-methyladenosine (m6A) co-methylation patterns that may be induced directly by METTL14 and WTAP, which are two known regulatory components of the RNA m6A methyltransferase complex. <P><P> Conclusion: Our proposed DPBBM method not only properly handles the count-based measurements of RNA methylation data from sites of very low reads coverage, but also learns an optimal number of clusters adaptively from the data analyzed. <P><P> Availability: The source code and documents of DPBBM R package are freely available through the Comprehensive R Archive Network (CRAN): https://cran.r-project.org/web/packages/DPBBM/.

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