GEOquery: a bridge between the Gene Expression Omnibus (GEO) and BioConductor

Davis, Sean; Meltzer, Paul S.

doi:10.1093/bioinformatics/btm254

Cited by 2,359 publications

(1,734 citation statements)

References 3 publications

Supporting

Mentioning

1,731

Contrasting

Unclassified

Order By: Relevance

“…Like other packages [15,16], it performs the usual formatting operations. It also offers new functionalities for sorting lists of datasets according to information keywords.…”

Section: Discussionmentioning

confidence: 99%

“…Several R/Bioconductor packages can be used to download and format the data [15,16]. We have chosen to encode in R our own functions.…”

Section: Methodsmentioning

confidence: 99%

“…Such a matrix is deduced from raw datasets, available on the GEO repository, though standard treatments: annotation and reduction [14]. Several R packages [15,16] that perform these operations and output data matrices such as considered here, are available. We have encoded our own functions.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Large Scale Statistical Analysis of GEO Datasets

Konstantina¹

2014

Gene Technology

View full text Add to dashboard Cite

The problem addressed here is that of simultaneous treatment of several gene expression datasets, possibly collected under different experimental conditions and/or platforms. Using robust statistics, a large scale statistical analysis has been conducted over 20 datasets downloaded from the Gene Expression Omnibus repository. The differences between datasets are compared to the variability inside a given dataset. Evidence that meaningful biological information can be extracted by merging different sources is provided.

show abstract

“…Like other packages [15,16], it performs the usual formatting operations. It also offers new functionalities for sorting lists of datasets according to information keywords.…”

Section: Discussionmentioning

confidence: 99%

“…Several R/Bioconductor packages can be used to download and format the data [15,16]. We have chosen to encode in R our own functions.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Large Scale Statistical Analysis of GEO Datasets

Konstantina¹

2014

Gene Technology

View full text Add to dashboard Cite

show abstract

“…For identifying differentially expressed genes, the samples were subjected to GEO2R analysis (Smyth, 2004) using GEOquery (Davis and Meltzer, 2007) and limma R (Smyth, no date) package. As a result, the most highly differentially expressed 250 genes have been identified.…”

Section: Geo2r Analysismentioning

confidence: 99%

Identification of Key Proteins Associated with Myocardial Infarction using Bioinformatics and Systems Biology

Ahammad

2018

Preprint

View full text Add to dashboard Cite

Myocardial infarction, more commonly known as heart attack, is a huge health problem around the world. It is a result of inadequate blood supply to certain parts of the heart and death of heart muscle cells in that region. Although it has been around for a long time, newer and newer ways of probing myocardial infarction is being followed. Bioinformatics and Systems Biology are relatively recent fields to try to give new insights into myocardial infarction. Following the footsteps of others, this in silico study has tried to chime in on the investigation into myocardial infarction. The study began with the gene expression omnibus dataset uploaded to the NCBI from a whole-genome gene expression analysis carried out at Mayo Clinic in Rochester, Minnesota. From the dataset, differentially expressed genes following first-time myocardial infarction were identified and classified into up-regulated and downregulated ones. Gene Set Enrichment Analysis (GSEA) was carried out on the up-regulated genes which were statistically significant and corresponded with the NCBI generated annotations. Protein-Protein Interaction Network for these genes was constructed. GSEA revealed 5 transcription factors, 5 microRNAs and 3 pathways significantly associated with them. From the Protein-Protein Interaction Network, 6 key proteins (hub nodes) have been identified. These 6 proteins may open a new window of opportunity for the discovery/design of new drugs for mitigating the damage caused by myocardial infarction.

show abstract

“…[10][11][12][13] The primary results were based on dataset GSE50834. 10 This data consists of 44 Illumina HumanHT-12 V4.0 expression beadchip with 23 samples HIV infected, 21 samples of TB-HIV co-infected.…”

Section: Data Acquisitionmentioning

confidence: 99%

Differential co-expression network analysis for identification of functional modules in HIV infection and tubercolosis-HIV co-infection

et al. 2016

View full text Add to dashboard Cite

Co-infection with tuberculosis (TB) is the preeminent cause of demise in human immunodeficiency virus (HIV) infected individuals. However, diagnosis of TB, particularly in the presence of an HIV co-infection, can be limiting owing to the high inaccuracy associated with conventional diagnostic strategies. Here we determine dysregulated pathways in TB-HIV co-infection and HIV infection utilizing coexpression networks. Primarily, we utilized preservation statistics to identify gene modules that exhibit a weak conservation of network topology within HIV infected and TB-HIV co-infected networks. Raw data was downloaded from Gene Expression Omnibus (GSE50834) and duly pre-processed. Co-expression networks for each condition (HIV infected and TB-HIV co-infected) were constructed independently. Preservation of HIV infected network edges was evaluated with respect to TB-HIV co-infected and vice versa using weighted correlation network analysis. Two out of the 22 modules were identified as exhibiting weak preservation in both conditions. Functional enrichment analysis identified that weakly preserved modules were pertinent to the condition under study. For instance, weakly preserved TBHIV co-infected module T1 enriched for genes associated with mitochondrion exhibited the highest fraction of gene interaction pairs exclusive to TB-HIV co-infection. Concisely, we illustrated the application of using preservation statistics to detect modules functionally linked with dysregulated pathways in disease, as exemplified by the mitochondrion module T1. Our analyses discovered gene clusters that are non-randomly linked with the disease. Highly specific gene pairs pointed to interactions between known markers of disease and favoured identification of possible markers that are likely to be associated with the disease.

show abstract

GEOquery: a bridge between the Gene Expression Omnibus (GEO) and BioConductor

Abstract: GEOquery is available as part of the BioConductor project.

Cited by 2,359 publications

References 3 publications

Large Scale Statistical Analysis of GEO Datasets

Large Scale Statistical Analysis of GEO Datasets

Identification of Key Proteins Associated with Myocardial Infarction using Bioinformatics and Systems Biology

Differential co-expression network analysis for identification of functional modules in HIV infection and tubercolosis-HIV co-infection

Contact Info

Product

Resources

About