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Expression data from relapsing-remitting MS samples

GSE16214 Homo sapiens (human) Expression microarray 240 samples Submitted 2009/08/01 Platform GPL570
At a glance

Expression microarray · human · 240 samples

What it is
Expression microarrayGene activity read on a chip (the older technology that RNA-seq replaced).
Organism
Homo sapiens (human)
Samples
240 samples
Data you can get
A processed expression table (series matrix) and the array files, from NCBI.
Summary (written by the submitters)
One of our new major finding among the genes that contributes to MS susceptibility is ICSBP1. The so called disease modifying therapies like interferon-beta (IFN-β), possibly acting on the peripheral T-cells, reduce the disease activity and the clinical progression, with a MRI-detectable effect in preventing lesion burden and cerebral atrophy development in RR-MS.
Read the full summary ↓
It suggests a critical role of peripheral blood mononuclear cells (PBMCs) immune response and modulation in developing inflammation in the brain. We tested the hypothesis that the genetic effect of the susceptible allele ICSBP1 can impact the gene expression profile of molecules belonging to the interferon pathway. We therefore interrogated the PBMC for changes in gene expression profile. We correlate those changes with the minor allele frequency for ICSBP1, performing independent quantitative trait analysis for each treatment category. Expression Quantitative Trait Loci Association with a p value < 0.05 have been used in follow up analysis. The regression coefficient of the Quantitative trait association represents the degree of correlation between the gene expression for each interrogated target gene and the minor allele frequency of the SNP for our gene of interest. This coefficient has been used as input in the subsequent Gene Set Enrichment Analysis performed in a pre-ranked approach. The resulting GSEA-SNP method rests on the assumption that SNPs underlying a disease phenotype might affect genes constituting a signaling pathway or genes with a common regulation. Therefore, GSEA-SNP can facilitate the identification of pathways or of underlying biological mechanisms. We used microarrays to capture gene expression profile of untreated subjects and of subjects under disease modyfing treatment (Interferon Beta and Glatiramer Acetate), in order to correlate gene expression and genotype data and in order to identify sets of genes specifically regulated in the different treatment categories.
This dataset
Download · which file do I need?

Direct links to NCBI, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.

1 · Just have a look

Processed table

The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.

2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need.

All supplementary files ↗
3 · The original raw files

Whole study · RAW.tar

Everything the authors deposited as one archive (GSE16214_RAW.tar), including the raw array or instrument files. Can be large; the picker in step 2 takes just the samples you need.

What the files usually look like: For arrays the series matrix is normally the normalised expression table you want; the raw array files (CEL and similar) are in the RAW.tar.

Also filed as BioProject PRJNA117153. Searching any of these in the dataset finder brings you back here.

Samples in this study

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