RT Journal Article
SR Electronic
T1 Independent component analysis provides clinically relevant insights into the biology of melanoma patients
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 395145
DO 10.1101/395145
A1 Petr V. Nazarov
A1 Anke K. Wienecke-Baldacchino
A1 Andrei Zinovyev
A1 Urszula Czerwińska
A1 Arnaud Muller
A1 Dorothée Nashan
A1 Gunnar Dittmar
A1 Francisco Azuaje
A1 Stephanie Kreis
YR 2018
UL http://biorxiv.org/content/early/2018/08/20/395145.abstract
AB The integration of publicly available and new patient-derived transcriptomic datasets is not straightforward and requires specialized approaches to deal with heterogeneity at technical and biological levels. Here we present a methodology that can overcome technical biases, predict clinically relevant outcomes and identify tumour-related biological processes in patients using previously collected large reference datasets. The approach is based on independent component analysis (ICA) – an unsupervised method of signal deconvolution. We developed parallel consensus ICA that robustly decomposes merged new and reference datasets into signals with minimal mutual dependency. By applying the method to a small cohort of primary melanoma and control samples combined with a large public melanoma dataset, we demonstrate that our method distinguishes cell-type specific signals from technical biases and allows to predict clinically relevant patient characteristics. Cancer subtypes, patient survival and activity of key tumour-related processes such as immune response, angiogenesis and cell proliferation were characterized. Additionally, through integration of transcriptomes and miRNomes, the method identified biological functions of miRNAs, which would otherwise not be possible.