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Transcriptional regulation analysis of Alzheimer 's disease based on FastNCA algorithm


Qianni Sun*, Wei Kong, Xiaoyang Mou and Shuaiqun Wang  


Understanding the relationship between genetic variation and gene expression is a central issue in genetics. Although many studies have identified genetic variations associated with gene expression, it is unclear how they perturb the underlying regulatory network of gene expression. Exploring how genetic variations perturb potential transcriptional regulation networks can help to paint a more complete picture of the complex landscape of transcription regulation. A method extending network component analysis (NCA) indicates that the genetic variations in the form of single nucleotide polymorphisms (SNPs) perturb transcription factors (TFs) expression activity and their regulatory strengths on target genes (TGs) to induce differential gene expression. Traditional analysis of transcriptional regulation networks is generally based on microarray data, ignoring the limitations of the microarray itself. However, RNA-seq has a greater dynamic range, lower background noise, and it can detect and quantify previously unknown transcripts and subtypes. For this situation, in order to compensate for higher background noise and inaccurate measurement, we applied fast network component analysis (FastNCA) to analysis the expression activities of TFs and their regulatory strengths on TGs using microarray data and RNA-seq, and constructed the transcriptional regulatory networks of differentially expressed genes in Alzheimer’s disease (AD). Multi-data fusion analysis was used to analyze the different TGs regulated by the same TFs in the different data. The study shows that the pathways of different TGs regulated by the same TFs in different data are all closely related to AD. Multi-data fusion analysis can form a certain complement to some extent and get more comprehensive results in the process of exploring the pathogenesis of AD.


Alzheimer’s disease (AD), Transcription factors (TFs), Target genes (TGs), Fast network component analysis (FastNCA), limma-voom, Single nucleotide polymorphisms (SNPs)


Shanghai Maritime University - College of Information Engineering Shanghai, Shanghai Maritime University - College of Information Engineering Shanghai, Rowan University and Guava Medicine - Department of Biochemistry Glassboro, Shanghai Maritime University - College of Information Engineering Shanghai

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