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Title: Identifying Glioblastoma Gene Networks Based on Hypergeometric Test Analysis      
dateReleased:
10-27-2015
description:
Patient specific therapy is emerging as an important possibility for many cancer patients. However, to identify such therapies it is essential to determine the genomic and transcriptional alterations present in one tumor relative to control samples. This presents a challenge since use of a single sample precludes many standard statistical analysis techniques. We reasoned that one means of addressing this issue is by comparing transcriptional changes in one tumor with those observed in a large cohort of patients analyzed by The Cancer Genome Atlas (TCGA). To test this directly, we devised a bioinformatics pipeline to identify differentially expressed genes in tumors resected from patients suffering from the most common malignant adult brain tumor, glioblastoma (GBM). We performed RNA sequencing on tumors from individual GBM patients and filtered the results through the TCGA database in order to identify possible gene networks that are overrepresented in GBM samples relative to controls. Importantly, we demonstrate that hypergeometric-based analysis of gene pairs identifies gene networks that validate experimentally. These studies identify a putative workflow for uncovering differentially expressed patient specific genes and gene networks for GBM and other cancers. RNAseq on 2 gliosblastoma samples and 2 epileptic samples
privacy:
not applicable
aggregation:
instance of dataset
ID:
E-GEOD-62731
refinement:
raw
alternateIdentifiers:
62731
keywords:
functional genomics
dateModified:
12-08-2015
availability:
available
types:
gene expression
name:
Homo sapiens
accessURL: https://www.ebi.ac.uk/arrayexpress/files/E-GEOD-62731/E-GEOD-62731.raw.1.zip
storedIn:
ArrayExpress
qualifier:
gzip compressed
format:
TXT
accessType:
download
authentication:
none
authorization:
none
accessURL: https://www.ebi.ac.uk/arrayexpress/files/E-GEOD-62731/E-GEOD-62731.processed.1.zip
storedIn:
ArrayExpress
qualifier:
gzip compressed
format:
TXT
accessType:
download
authentication:
none
authorization:
none
accessURL: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE62731
storedIn:
Gene Expression Omnibus
qualifier:
not compressed
format:
HTML
accessType:
landing page
primary:
true
authentication:
none
authorization:
none
abbreviation:
EBI
homePage: http://www.ebi.ac.uk/
ID:
SCR:004727
name:
European Bioinformatics Institute
homePage: https://www.ebi.ac.uk/arrayexpress/
ID:
SCR:002964
name:
ArrayExpress
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