Journal article
Machine learning applied to whole-blood RNA-sequencing data uncovers distinct subsets of patients with systemic lupus erythematosus
WA Figgett, K Monaghan, M Ng, M Alhamdoosh, E Maraskovsky, NJ Wilson, AY Hoi, EF Morand, F Mackay
Clinical and Translational Immunology | WILEY | Published : 2019
DOI: 10.1002/cti2.1093
Open access
Abstract
Objectives: Systemic lupus erythematosus (SLE) is a heterogeneous autoimmune disease that is difficult to treat. There is currently no optimal stratification of patients with SLE, and thus, responses to available treatments are unpredictable. Here, we developed a new stratification scheme for patients with SLE, based on the computational analysis of patients’ whole-blood transcriptomes. Methods: We applied machine learning approaches to RNA-sequencing (RNA-seq) data sets to stratify patients with SLE into four distinct clusters based on their gene expression profiles. A meta-analysis on three recently published whole-blood RNA-seq data sets was carried out, and an additional similar data set..
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Awarded by Victorian Cancer Agency
Funding Acknowledgements
Computational work was performed using the high-performance computing (HPC) resources of the University of Melbourne (Project#punim0259) and Melbourne Bioinformatics (Project#UOM0044). We acknowledge the HPC training and technical assistance provided by the University of Melbourne, Melbourne Bioinformatics, and the Australian National Computational Infrastructure. This research was supported by use of the NeCTAR Research Cloud, a collaborative Australian research platform supported by the National Collaborative Research Infrastructure Strategy. We acknowledge Dr Kim-Anh Le Cao for helpful discussions about multivariate statistics methods in the mixOmics R package. WF was supported by funding from the Victorian Cancer Agency (grant#ECSG15029).