ShinyMDE: Shiny tool for microarray meta-analysis for differentially expressed gene detection

2016 ◽  
Author(s):  
H. L. Shashirekha ◽  
Agaz Hussain Wani
Keyword(s):  
Meta Analysis ◽  
Differentially Expressed Gene ◽  
Differentially Expressed ◽  
Gene Detection

scholarly journals scCODE: an R package for personalized differentially expressed gene detection on single-cell RNA-sequencing data

2021 ◽  
Author(s):  
jiawei Zou ◽  
miaochen Wang ◽  
zhen Zhang ◽  
zheqi Liu ◽  
xiaobin Zhang ◽  
...  
Keyword(s):  
Single Cell ◽  
Differentially Expressed Gene ◽  
R Package ◽  
Differentially Expressed ◽  
Rna Seq ◽  
Sequencing Data ◽  
Gene Detection ◽  
Gene Filtering ◽  
Consensus Optimization ◽  
Detection Strategies

Differential expression (DE) gene detection in single-cell RNA-seq (scRNA-seq) data is a key step to understand the biological question investigated. We find that DE methods together with gene filtering have profound impact on DE gene identification, and different datasets will benefit from personalized DE gene detection strategies. Existing tools don't take gene filtering into consideration, and couldn't evaluate DE performance on real datasets without prior knowledge of true results. Based on two new metrics, we propose scCODE (single cell Consensus Optimization of Differentially Expressed gene detection), an R package to automatically optimize DE gene detection for each experimental scRNA-seq dataset.

Decoding Psoriasis: Integrated Bioinformatics Approach to Understand Hub Genes and Involved Pathways

2020 ◽  
Vol 26 (29) ◽  
pp. 3619-3630
Author(s):  
Saumya Choudhary ◽  
Dibyabhaba Pradhan ◽  
Noor S. Khan ◽  
Harpreet Singh ◽  
George Thomas ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differentially Expressed Gene ◽  
Therapeutic Targets ◽  
Interaction Network ◽  
Gene Expression Omnibus ◽  
Differentially Expressed ◽  
Keratinocyte Differentiation ◽  
Hub Genes ◽  
Lesional Skin ◽  
Ncbi Gene Expression Omnibus

Background: Psoriasis is a chronic immune mediated skin disorder with global prevalence of 0.2- 11.4%. Despite rare mortality, the severity of the disease could be understood by the accompanying comorbidities, that has even led to psychological problems among several patients. The cause and the disease mechanism still remain elusive. Objective: To identify potential therapeutic targets and affecting pathways for better insight of the disease pathogenesis. Method: The gene expression profile GSE13355 and GSE14905 were retrieved from NCBI, Gene Expression Omnibus database. The GEO profiles were integrated and the DEGs of lesional and non-lesional psoriasis skin were identified using the affy package in R software. The Kyoto Encyclopaedia of Genes and Genomes pathways of the DEGs were analyzed using clusterProfiler. Cytoscape, V3.7.1 was utilized to construct protein interaction network and analyze the interactome map of candidate proteins encoded in DEGs. Functionally relevant clusters were detected through Cytohubba and MCODE. Results: A total of 1013 genes were differentially expressed in lesional skin of which 557 were upregulated and 456 were downregulated. Seven dysregulated genes were extracted in non-lesional skin. The disease gene network of these DEGs revealed 75 newly identified differentially expressed gene that might have a role in development and progression of the disease. GO analysis revealed keratinocyte differentiation and positive regulation of cytokine production to be the most enriched biological process and molecular function. Cytokines -cytokine receptor was the most enriched pathways. Among 1013 identified DEGs in lesional group, 36 DEGs were found to have altered genetic signature including IL1B and STAT3 which are also reported as hub genes. CCNB1, CCNA2, CDK1, IL1B, CXCL8, MKI 67, ESR1, UBE2C, STAT1 and STAT3 were top 10 hub gene. Conclusion: The hub genes, genomic altered DEGs and other newly identified differentially dysregulated genes would improve our understanding of psoriasis pathogenesis, moreover, the hub genes could be explored as potential therapeutic targets for psoriasis.

Identification of the Differentially Expressed Genes Involved in the Synergistic Neurotoxicity of an HIV Protease Inhibitor and Methamphetamine

2019 ◽  
Vol 17 (4) ◽  
pp. 290-303
Author(s):  
Sangsang Li ◽  
Yanfei Li ◽  
Bingpeng Deng ◽  
Jie Yan ◽  
Yong Wang
Keyword(s):  
Protease Inhibitor ◽  
Growth Inhibition ◽  
Quantitative Polymerase Chain Reaction ◽  
Differentially Expressed Gene ◽  
Antiretroviral Drugs ◽  
Differentially Expressed ◽  
Hiv Protease ◽  
Hiv Protease Inhibitor ◽  
Dopaminergic Cells ◽  
Immunodeficiency Syndrome

Background: The abuse of psychostimulants such as methamphetamine (METH) is common in human immunodeficiency virus (HIV)-infected individuals. Acquired immunodeficiency syndrome (AIDS) patients taking METH and antiretroviral drugs could suffer severe neurologic damage and cognitive impairment. Objective: To reveal the underlying neuropathologic mechanisms of an HIV protease inhibitor (PI) combined with METH, growth-inhibition tests of dopaminergic cells and RNA sequencing were performed. Methods: A combination of METH and PI caused more growth inhibition of dopaminergic cells than METH alone or a PI alone. Furthermore, we identified differentially expressed gene (DEG) patterns in the METH vs. untreated cells (1161 genes), PI vs. untreated cells (16 genes), METH-PI vs. PI (3959 genes), and METH-PI vs. METH groups (14 genes). Results: The DEGs in the METH-PI co-treatment group were verified in the brains of a mouse model using quantitative polymerase chain reaction and were involved mostly in the regulatory functions of cell proliferation and inflammation. Conclusion: Such identification of key regulatory genes could facilitate the study of their neuroprotective potential in the users of METH and PIs.

Gene Set Correlation Analysis and Visualization Using Gene Expression Data

2020 ◽  
Vol 15 ◽  
Author(s):  
Chen-An Tsai ◽  
James J. Chen
Keyword(s):  
Gene Expression ◽  
Correlation Analysis ◽  
Gene Expression Data ◽  
Differentially Expressed Gene ◽  
Differentially Expressed ◽  
Superior Performance ◽  
Expression Data ◽  
Gene Set ◽  
Gene Sets ◽  
Set Correlation

Background: Gene set enrichment analyses (GSEA) provide a useful and powerful approach to identify differentially expressed gene sets with prior biological knowledge. Several GSEA algorithms have been proposed to perform enrichment analyses on groups of genes. However, many of these algorithms have focused on identification of differentially expressed gene sets in a given phenotype. Objective: In this paper, we propose a gene set analytic framework, Gene Set Correlation Analysis (GSCoA), that simultaneously measures within and between gene sets variation to identify sets of genes enriched for differential expression and highly co-related pathways. Methods: We apply co-inertia analysis to the comparisons of cross-gene sets in gene expression data to measure the costructure of expression profiles in pairs of gene sets. Co-inertia analysis (CIA) is one multivariate method to identify trends or co-relationships in multiple datasets, which contain the same samples. The objective of CIA is to seek ordinations (dimension reduction diagrams) of two gene sets such that the square covariance between the projections of the gene sets on successive axes is maximized. Simulation studies illustrate that CIA offers superior performance in identifying corelationships between gene sets in all simulation settings when compared to correlation-based gene set methods. Result and Conclusion: We also combine between-gene set CIA and GSEA to discover the relationships between gene sets significantly associated with phenotypes. In addition, we provide a graphical technique for visualizing and simultaneously exploring the associations of between and within gene sets and their interaction and network. We then demonstrate integration of within and between gene sets variation using CIA and GSEA, applied to the p53 gene expression data using the c2 curated gene sets. Ultimately, the GSCoA approach provides an attractive tool for identification and visualization of novel associations between pairs of gene sets by integrating co-relationships between gene sets into gene set analysis.

scholarly journals Expression of DDX11 and DNM1L at the 12p11 Locus Modulates Systemic Lupus Erythematosus Susceptibility

2021 ◽  
Vol 22 (14) ◽  
pp. 7624
Author(s):  
Mohammad Saeed ◽  
Alejandro Ibáñez-Costa ◽  
Alejandra María Patiño-Trives ◽  
Laura Muñoz-Barrera ◽  
Eduardo Collantes Estévez ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lupus Erythematosus ◽  
Genome Stability ◽  
Meta Analysis ◽  
Susceptibility Gene ◽  
Significant Snps ◽  
Differentially Expressed ◽  
Systemic Lupus Erythematosus Susceptibility ◽  
Functional Pathways ◽  
Functional Analyses

Objectives: This study employed genetic and functional analyses using OASIS meta-analysis of multiple existing GWAS and gene-expression datasets to identify novel SLE genes. Methods: Four hundred and ten genes were mapped using SNIPPER to 30 SLE GWAS loci and investigated for expression in three SLE GEO-datasets and the Cordoba GSE50395-dataset. Blood eQTL for significant SNPs in SLE loci and STRING for functional pathways of differentially expressed genes were used. Confirmatory qPCR on SLE monocytes was performed. The entire 12p11 locus was investigated for genetic association using two additional GWAS. Expression of 150 genes at this locus was assessed. Based on this significance, qPCRs for DNM1L and KRAS were performed. Results: Fifty genes were differentially expressed in at least two SLE GEO-datasets, with all probes directionally aligned. DDX11, an RNA helicase involved in genome stability, was downregulated in both GEO and Cordoba datasets. The most significant SNP, rs3741869 in OASIS locus 12p11.21, containing DDX11, was a cis-eQTL regulating DDX11 expression. DDX11 was found repressed. The entire 12p11 locus showed three association peaks. Gene expression in GEO datasets identified DNM1L and KRAS, besides DDX11. Confirmatory qPCR validated DNM1L as an SLE susceptibility gene. DDX11, DNM1L and KRAS interact with each other and multiple known SLE genes including STAT1/STAT4 and major components of IFN-dependent gene expression, and are responsible for signal transduction of cytokines, hormones, and growth-factors, deregulation of which is involved in SLE-development. Conclusion: A genomic convergence approach with OASIS analysis of multiple GWAS and expression datasets identified DDX11 and DNM1L as novel SLE-genes, the expression of which is altered in monocytes from SLE patients. This study lays the foundation for understanding the pathogenic involvement of DDX11 and DNM1L in SLE by identifying them using a systems-biology approach, while the 12p11 locus harboring these genes was previously missed by four independent GWAS.

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