RT Journal Article
SR Electronic
T1 BiCoN: Network-constrained biclustering of patients and omics data
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.01.31.926345
DO 10.1101/2020.01.31.926345
A1 Olga Lazareva
A1 Hoan Van Do
A1 Stefan Canzar
A1 Kevin Yuan
A1 Jan Baumbach
A1 David B. Blumenthal
A1 Paolo Tieri
A1 Tim Kacprowski
A1 Markus List
YR 2020
UL http://biorxiv.org/content/early/2020/04/21/2020.01.31.926345.abstract
AB Motivation Unsupervised learning approaches are frequently employed to identify patient subgroups and biomarkers such as disease-associated genes. Thus, clustering and biclustering are powerful techniques often used with expression data, but are usually not suitable to unravel molecular mechanisms along with patient subgroups. To alleviate this, we developed the network-constrained biclustering approach BiCoN (Biclustering Constrained by Networks) which (i) restricts biclusters to functionally related genes connected in molecular interaction networks and (ii) maximizes the difference in gene expression between two subgroups of patients.Results Our analyses of non-small cell lung and breast cancer gene expression data demonstrate that BiCoN clusters patients in agreement with known cancer subtypes while discovering gene subnetworks pointing to functional differences between these subtypes. Furthermore, we show that BiCoN is robust to noise and batch effects and can distinguish between high and low load of tumor-infiltrating leukocytes while identifying subnetworks related to immune cell function. In summary, BiCoN is a powerful new systems medicine tool to stratify patients while elucidating the responsible disease mechanism.Availability PyPI package: https://pypi.org/project/biconWeb interface: https://exbio.wzw.tum.de/biconContact olga.lazareva{at}tum.deSupplementary information Supplementary data are available at Bioinformatics online.Competing Interest StatementThe authors have declared no competing interest.