ImaGEO: integrative gene expression meta-analysis from GEO database

Toro-Domínguez, Daniel; Martorell-Marugán, Jordi; López-Domínguez, Raúl; García-Moreno, Adrián; González‐Rumayor, Víctor; Alarcón‐Riquelme, Marta E.; Carmona-Sáez, Pedro

doi:10.1093/bioinformatics/bty721

Cited by 121 publications

(79 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In our study, the gene expression profiling datasets GSE53819 and GSE12452, and gene methylation profiling datasets GSE52068 were obtained from the gene expression omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/) [16,17]. Totally 18NPC and 18 normal specimens were obtained in GSE53819 while 31NPC and 10 normal samples were obtained in GSE12452.…”

Section: Microarray Datamentioning

confidence: 99%

DNA methylation biomarkers for nasopharyngeal carcinoma

et al. 2020

View full text Add to dashboard Cite

Background Aberrant methylation of DNA plays an important role in the pathogenesis of nasopharyngeal carcinoma (NPC). In the current study, we aimed to integrate three cohorts profile datasets to identify abnormally methylated-differentially expressed genes and pathways associated with NPC. Methods Data of gene expression microarrays (GSE53819, GSE412452) and gene methylation microarrays (GSE52068) obtained from the GEO database. Aberrantly methylated differentially expressed genes (DEGs) were obtained by GEO2R. The David database was utilized to perform enrichment and functional analysis regarding selected genes. To create a protein-protein interaction (PPI), STRING and Cytoscape software were utilized. The MCODE was used for module analysis of the PPI network. Results In total, 181 hypomethylation-high expression genes were identified, which were enriched in the biological mechanisms involved in the differentiation of endodermal cell, mitotic nuclear division, mitotic cell cycle process, chromosome segregation and cell cycle phase transition, etc. Pathway enrichment showed ECM-receptor interaction, PI3K-Akt signaling pathway, Focal adhesion, Protein digestion and absorption and Amoebiasis, etc. The top 3 hub genes of PPI network were FANCI, POSTN, and IFIH1. Additionally, 210 hypermethylation-low expression genes were identified, and our data revealed enrichment in biological processes including axoneme assembly, micro tubular formation, assembly of axonemal dynein

show abstract

Section: Microarray Datamentioning

confidence: 99%

DNA methylation biomarkers for nasopharyngeal carcinoma

et al. 2020

View full text Add to dashboard Cite

show abstract

“…The GSE21138 dataset included 29 healthy control samples and 30 samples from SCZ patients [21]. The web-based application ImaGEO was used to perform the meta-analysis (http://bioinfo.genyo.es/imageo/) [22]. ImaGEO performs gene expression meta-analysis of GEO microarray datasets by using functions contained in the MetaDE R package [22].…”

Section: Dataset Selection and Analysismentioning

confidence: 99%

“…GSE18312 included data from 13 SCZ patients and 8 healthy controls [24], while GSE27383 included PBMCs samples from 43 SCZ subjects and 29 healthy people [25]. The meta-analysis of the two datasets was performed as indicated in the "Statistical Analysis" section, using the ImaGEO software [22].…”

Section: Of 16mentioning

confidence: 99%

Meta-Analysis of Transcriptomic Data of Dorsolateral Prefrontal Cortex and of Peripheral Blood Mononuclear Cells Identifies Altered Pathways in Schizophrenia

Petralia

Saraceno

Pennisi

et al. 2020

Genes

View full text Add to dashboard Cite

Schizophrenia (SCZ) is a psychiatric disorder characterized by both positive and negative symptoms, including cognitive dysfunction, decline in motivation, delusion and hallucinations. Antipsychotic agents are currently the standard of care treatment for SCZ. However, only about one-third of SCZ patients respond to antipsychotic medications. In the current study, we have performed a meta-analysis of publicly available whole-genome expression datasets on Brodmann area 46 of the brain dorsolateral prefrontal cortex in order to prioritize potential pathways underlying SCZ pathology. Moreover, we have evaluated whether the differentially expressed genes in SCZ belong to specific subsets of cell types. Finally, a cross-tissue comparison at both the gene and functional level was performed by analyzing the transcriptomic pattern of peripheral blood mononuclear cells of SCZ patients. Our study identified a robust disease-specific set of dysfunctional biological pathways characterizing SCZ patients that could in the future be exploited as potential therapeutic targets.

show abstract

“…Although the steps of data normalization and integration are not a part of MOG, because the data that is input to MOG is typically from aggregate studies, we present several considerations. Two predominate frameworks for analysis of multiple expression studies are: 1) analyze each study independently and then combine results from independent studies (metaanalysis) (5,9,10,27,(40)(41)(42)(43), or 2) combine data from multiple studies together to create a "pooled" dataset and analyze the pooled data (5,26,27). In meta-analysis, if individual studies show statistically-significant results, then it is likely that the final result combined from the studies will also be significant (5).…”

Section: B3 Challenges Of Integrating Data From Multiple Studiesmentioning

confidence: 99%

MetaOmGraph: a workbench for interactive exploratory data analysis of large expression datasets

Singh

Hur

Dorman

et al. 2019

Preprint

View full text Add to dashboard Cite

The diverse and growing omics data in public domains provide researchers with a tremendous opportunity to extract hidden knowledge. However, the challenge of providing domain experts with easy access to these big data has resulted in the vast majority of archived data remaining unused. Here, we present MetaOmGraph (MOG), a free, open-source, standalone software for exploratory data analysis of massive datasets by scientific researchers. Using MOG, a researcher can interactively visualize and statistically analyze the data, in the context of its metadata. Researchers can interactively hone-in on groups of experiments or genes based on attributes such as expression values, statistical results, metadata terms, and ontology annotations. MOG's statistical tools include coexpression, differential expression, and differential correlation analysis, with permutation test-based options for significance assessments. Multithreading and indexing enable efficient data analysis on a personal computer, with no need for writing code. Data can be visualized as line charts, box plots, scatter plots, and volcano plots. A researcher can create new MOG projects from any data or analyze an existing one. An R-wrapper lets a researcher select and send smaller data subsets to R for additional analyses. A researcher can save MOG projects with a history of the exploratory progress and later reopen or share them. We illustrate MOG by case studies of large curated datasets from human cancer RNA-Seq, in which we assembled a list of novel putative biomarker genes in different tumors, and microarray and metabolomics from A. thaliana.Singh et al. | bioRχiv | July 11, 2019 | 1-18 in complex biological networks (6,8,(24)(25)(26)(27)(28). Here, we present MetaOmGraph (MOG), a software written in Java, to interactively explore and visualize large datasets. MOG overcomes the challenges posed by the size and complexity of big datasets by efficient handling of the data files, using a combination of data indexing and buffering schemes. Further, by incorporating metadata, MOG adds extra dimensions to the analyses and provides flexibility in data exploration. Researchers can explore their own data on their local machines. At any stage of the analysis, a researcher can save her/his progress. Saved MOG projects can be shared, reused, and included in publications. MOG is user-centered software, designed for all types of data, but specialized for expression data. MATERIALS AND METHODSA. Overview. MOG is a standalone program that can run on any operating system capable of running Java (Linux, Mac and Windows). Access to MOG easy. MOG runs on the researcher's computer and thus the researcher does not need to rely on internet accessibility, and is not slowed down by the latency of transfer of big data. Furthermore, the data in a researcher's project is secure, remaining on the researcher's computer, particularly important for confidential data such as human RNA-Seq. MOG's modular structure has been carefully designed to enable developers to easily implement new s...

show abstract

ImaGEO: integrative gene expression meta-analysis from GEO database

Abstract: Online-only Supplementary data available at the journal's web site.

Cited by 121 publications

References 8 publications

DNA methylation biomarkers for nasopharyngeal carcinoma

DNA methylation biomarkers for nasopharyngeal carcinoma

Meta-Analysis of Transcriptomic Data of Dorsolateral Prefrontal Cortex and of Peripheral Blood Mononuclear Cells Identifies Altered Pathways in Schizophrenia

MetaOmGraph: a workbench for interactive exploratory data analysis of large expression datasets

Contact Info

Product

Resources

About