Differentially Expressed Genes Extracted by the Tensor Robust Principal Component Analysis (TRPCA) Method

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Yue Hu ◽  
Jin-Xing Liu ◽  
Ying-Lian Gao ◽  
Sheng-Jun Li ◽  
Juan Wang

In the big data era, sequencing technology has produced a large number of biological sequencing data. Different views of the cancer genome data provide sufficient complementary information to explore genetic activity. The identification of differentially expressed genes from multiview cancer gene data is of great importance in cancer diagnosis and treatment. In this paper, we propose a novel method for identifying differentially expressed genes based on tensor robust principal component analysis (TRPCA), which extends the matrix method to the processing of multiway data. To identify differentially expressed genes, the plan is carried out as follows. First, multiview data containing cancer gene expression data from different sources are prepared. Second, the original tensor is decomposed into a sum of a low-rank tensor and a sparse tensor using TRPCA. Third, the differentially expressed genes are considered to be sparse perturbed signals and then identified based on the sparse tensor. Fourth, the differentially expressed genes are evaluated using Gene Ontology and Gene Cards tools. The validity of the TRPCA method was tested using two sets of multiview data. The experimental results showed that our method is superior to the representative methods in efficiency and accuracy aspects.

BMC Surgery ◽  
2021 ◽  
Vol 21 (1) ◽  
Hai-jun Zhu ◽  
Meng Fan ◽  
Wei Gao

Abstract Background The skin is the largest organ of the body and has multiple functions. Wounds remain a significant healthcare problem due to the large number of traumatic and pathophysiological conditions patients suffer. Methods Gene expression profiles of 37 biopsies collected from patients undergoing split-thickness skin grafts at five different time points were downloaded from two datasets (GSE28914 and GSE50425) in the Gene Expression Omnibus (GEO) database. Principal component analysis (PCA) was applied to classify samples into different phases. Subsequently, differentially expressed genes (DEGs) analysis, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway functional enrichment analyses were performed, and protein–protein interaction (PPI) networks created for each phase. Furthermore, based on the results of the PPI, hub genes in each phase were identified by molecular complex detection combined with the ClueGO algorithm. Results Using principal component analysis, the collected samples were divided into four phases, namely intact phase, acute wound phase, inflammatory and proliferation phase, and remodeling phase. Intact samples were used as control group. In the acute wound phase, a total of 1 upregulated and 100 downregulated DEGs were identified. Tyrosinase (TYR), tyrosinase Related Protein 1 (TYRP1) and dopachrome tautomerase (DCT) were considered as hub genes and enriched in tyrosine metabolism which dominate the process of melanogenesis. In the inflammatory and proliferation phase, a total of 85 upregulated and 164 downregulated DEGs were identified. CHEK1, CCNB1 and CDK1 were considered as hub genes and enriched in cell cycle and P53 signaling pathway. In the remodeling phase, a total of 121 upregulated and 49 downregulated DEGs were identified. COL4A1, COL4A2, and COL6A1 were considered as hub genes and enriched in protein digestion and absorption, and ECM-receptor interaction. Conclusion This comprehensive bioinformatic re-analysis of GEO data provides new insights into the molecular pathogenesis of wound healing and the potential identification of therapeutic targets for the treatment of wounds.

2020 ◽  
Vol 319 (5) ◽  
pp. F809-F821
Sehoon Park ◽  
Seung Hee Yang ◽  
Chang Wook Jeong ◽  
Kyung Chul Moon ◽  
Dong Ki Kim ◽  

Few studies have examined gene expression changes occurring in the glomeruli of IgA nephropathy (IgAN) using a sensitive transcriptomic profiling method such as RNA sequencing (RNA-Seq). We collected glomeruli from biopsy specimens from patients with IgAN with relatively preserved kidney function (estimated glomerular filtration rate ≥ 60 mL·min−1·1.73 m−2 and urine protein-to-creatinine ratio < 3 g/g) and from normal kidney cortexes by hand microdissection and performed RNA-Seq. Differentially expressed genes were identified, and gene ontology term annotation and pathway analysis were performed. Immunohistochemical labeling and primary mesangial cell cultures were performed to confirm the findings of RNA-Seq analysis. Fourteen patients with IgAN and ten controls were included in this study. Glomerulus-specific genes were highly abundant. Principal component analysis showed clear separation between the IgAN and control groups. There were 2,497 differentially expressed genes, of which 1,380 were upregulated and 1,117 were downregulated (false discovery rate < 0.01). The enriched gene ontology terms included motility/migration, protein/vesicle transport, and immune system, and kinase binding was the molecular function overrepresented in IgAN. B cell signaling, chemokine signal transduction, and Fcγ receptor-mediated phagocytosis were the canonical pathways overrepresented. In vitro experiments confirmed that spleen tyrosine kinase (SYK), reported as upregulated in the IgAN transcriptome, was also upregulated in glomeruli from an independent set of patients with IgAN and that treatment with patient-derived IgA1 increased the expression of SYK in mesangial cells. In conclusion, transcriptomic profiling of the IgAN glomerulus provides insights in the intraglomerular pathophysiology of IgAN before it reaches profound kidney dysfunction. SYK may have a pathogenetic role in IgAN.

2020 ◽  
Vol 48 (2) ◽  
pp. 214-233
J. de Sousa ◽  
K. Hron ◽  
K. Fačevicová ◽  
P. Filzmoser

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