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Series GSE162329

Query DataSets for GSE162329

Status

Public on Dec 08, 2020

Title

An Omics based meta-analysis to support infection state stratification [Affymetrix dataset]

Sample organism

Homo sapiens

Experiment type

Third-party reanalysis
Expression profiling by array

Summary

A fundamental problem for disease treatment is that while antibiotics are a powerful counter to bacteria, they are ineffective against viruses. Often, bacterial and viral infections are confused due to their similar symptoms and lack of rapid diagnostics. With many clinicians relying primarily on symptoms for diagnosis, overuse and misuse of modern antibiotics are rife, contributing to the growing pool of antibiotic resistance. To ensure an individual receives optimal treatment given their disease state and to reduce over-prescription of antibiotics, the host response can in theory be measured quickly to distinguish between the two states. To establish a predictive biomarker panel of disease state (viral/bacterial/no-infection) we conducted a meta-analysis of human blood infection studies using Machine Learning (ML). We focused on publicly available gene expression data from two widely used platforms, Affymetrix and Illumina microarrays as they represented a significant proportion of the available data. We were able to develop multi-class models with high accuracies with our best model predicting 93% of bacterial and 89% viral samples correctly. To compare the selected features in each of the different technologies, we reverse engineered the underlying molecular regulatory network and explored the neighbourhood of the selected features. The networks highlighted that although on the gene-level the models differed, they contained genes from the same areas of the network. Specifically, this convergence was to pathways including the Type I interferon Signalling Pathway, Chemotaxis, Apoptotic Processes, and Inflammatory / Innate Response.

Overall design

The study is a batch corrected combination of several study datasets. Partial sample description (GSE,GPL,GSM,organism, and sample type) are provided (in the re-analyzed_sample_reference.txt) as all data is coming from pre-published studies and can be found using the sample references provided.
1676 samples from published studies (including bacterial(b,b?), viral(v,v?), and control(c) samples) combined, batch corrected and then analysed following the outlined methods in https://www.medrxiv.org/content/10.1101/2020.07.28.20163329v2

Contributor(s)

Myall A, Perkins S, Rushton D, David J, Spencer P, Jones A, Antczak P

Citation(s)

33560295

Submission date

Nov 30, 2020

Last update date

Aug 31, 2021

Contact name

ashleigh Myall

E-mail(s)

a.myall19@imperial.ac.uk

Organization name

Imperial College London

Street address

Exhibition Rd, South Kensington, London

City

London

ZIP/Postal code

SW7 2BU

Country

United Kingdom

Relations

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Download family	Format
SOFT formatted family file(s)	SOFT
MINiML formatted family file(s)	MINiML
Series Matrix File(s)	TXT

Supplementary file	Size	Download	File type/resource
GSE162329_re-analyzed_data.txt.gz	113.5 Mb	(ftp)(http)	TXT
GSE162329_re-analyzed_sample_reference.txt.gz	5.6 Kb	(ftp)(http)	TXT
Processed data are available on Series record