scholarly journals A non-Gaussian factor analysis approach to transcription Network Component Analysis

2012 ◽  
Author(s):  
Shikui Tu ◽  
Dingsheng Luo ◽  
Runsheng Chen ◽  
Lei Xu
Keyword(s):  
Factor Analysis ◽  
Component Analysis ◽  
Analysis Approach ◽  
Network Component Analysis ◽  
Transcription Network ◽  
Non Gaussian ◽  
Network Component

scholarly journals Primary components in shaping koperasi masjid’s participation level: Employing a principle component analysis approach

2018 ◽  
Vol 31 (3) ◽  
pp. 318
Author(s):  
Fitriani Fitriani ◽  
Sudiyo Sudiyo ◽  
Dayang Berliana ◽  
Evi Yuniarti
Keyword(s):  
Factor Analysis ◽  
Strong Correlation ◽  
Principal Component ◽  
Component Analysis ◽  
Primary Factor ◽  
Analysis Approach ◽  
Second Primary ◽  
Study Approach ◽  
Participation Level

Koperasi masjid present themselves as a part of muamalah through Sharia economic facilitation. This study was conducted to analyse the cooperative members’ participation level in relation to the implementation of a cooperative business program. A survey using a case study approach was conducted at koperasi Masjid Hafshotul Iman, Masjid Taqwa and Masjid Darul HikmahThe survey employed a purposive sampling method with the participation of the cooperative’s management (chairman, secretary, and treasurer) and the masjid’s members. Ten respondents were purposively chosen from each mosque and a total 30 respondents were acquired. The distributed questionnaire was a closed one, measured with a Likert scale. The results were analysed using factor analysis and the Principal Component Analysis approach (PCA). Based on the results of the factor analysis, the members’ participation level in cooperative service can be classified into three primary factors. Some of the variables displayed a strong correlation with Factor 1, namely motivation, management performance, RAT performance, and supervisor performance. The second primary factor was formed by activeness in participating in cooperative activities. Lastly, the third primary factor encompassed three variables with a strong correlation, namely age, education, and membership duration.

scholarly journals Transcriptome network component analysis with limited microarray data

2006 ◽  
Vol 22 (15) ◽  
pp. 1886-1894 ◽  
Author(s):  
S. J. Galbraith ◽  
L. M. Tran ◽  
J. C. Liao
Keyword(s):  
Microarray Data ◽  
Component Analysis ◽  
Network Component Analysis ◽  
Network Component

scholarly journals Environmental gene regulatory influence networks in rice (Oryza sativa):response to water deficit, high temperature and agricultural environments

2016 ◽  
Author(s):  
Olivia Wilkins ◽  
Christoph Hafemeister ◽  
Anne Plessis ◽  
Meisha-Marika Holloway-Phillips ◽  
Gina M. Pham ◽  
...  
Keyword(s):  
Water Deficit ◽  
Target Genes ◽  
Component Analysis ◽  
Field Conditions ◽  
Network Component Analysis ◽  
Regulatory Influence ◽  
Putative Target ◽  
Gene Regulatory ◽  
Influence Networks ◽  
Network Component

ABSTRACTEnvironmental Gene Regulatory Influence Networks (EGRINs) coordinate the timing and rate of gene expression in response to environmental and developmental signals. EGRINs encompass many layers of regulation, which culminate in changes in the level of accumulated transcripts. Here we infer EGRINs for the response of five tropical Asian rice cultivars to high temperatures, water deficit, and agricultural field conditions, by systematically integrating time series transcriptome data (720 RNA-seq libraries), patterns of nucleosome-free chromatin (18 ATAC-seq libraries), and the occurrence of known cis-regulatory elements. First, we identify 5,447 putative target genes for 445 transcription factors (TFs) by connecting TFs with genes with known cis-regulatory motifs in nucleosome-free chromatin regions proximal to transcriptional start sites (TSS) of genes. We then use network component analysis to estimate the regulatory activity for these TFs from the expression of these putative target genes. Finally, we inferred an EGRIN using the estimated TFA as the regulator. The EGRIN included regulatory interactions between 4,052 target genes regulated by 113 TFs. We resolved distinct regulatory roles for members of a large TF family, including a putative regulatory connection between abiotic stress and the circadian clock, as well as specific regulatory functions for TFs in the drought response. TFA estimation using network component analysis is an effective way of incorporating multiple genome-scale measurements into network inference and that supplementing data from controlled experimental conditions with data from outdoor field conditions increases the resolution for EGRIN inference.

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