2-AIN-505, 2-AIN-251: Seminár z bioinformatiky (1) a (3)
Zima 2020

Benjamin A. Logsdon, Jason Mezey. Gene expression network reconstruction by convex feature selection whenincorporating genetic perturbations. PLoS Comput Biol, 6(12):e1001014. 2010.

Download preprint: not available

Download from publisher: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1001014#pcbi-1001014-g001 PubMed

Related web page: not available

Bibliography entry: BibTeX


Cellular gene expression measurements contain regulatory information that can be 
used to discover novel network relationships. Here, we present a new algorithm
for network reconstruction powered by the adaptive lasso, a theoretically and
empirically well-behaved method for selecting the regulatory features of a
network. Any algorithms designed for network discovery that make use of directed 
probabilistic graphs require perturbations, produced by either experiments or
naturally occurring genetic variation, to successfully infer unique regulatory
relationships from gene expression data. Our approach makes use of appropriately 
selected cis-expression Quantitative Trait Loci (cis-eQTL), which provide a
sufficient set of independent perturbations for maximum network resolution. We
compare the performance of our network reconstruction algorithm to four other
approaches: the PC-algorithm, QTLnet, the QDG algorithm, and the NEO algorithm,
all of which have been used to reconstruct directed networks among phenotypes
leveraging QTL. We show that the adaptive lasso can outperform these algorithms
for networks of ten genes and ten cis-eQTL, and is competitive with the QDG
algorithm for networks with thirty genes and thirty cis-eQTL, with rich
topologies and hundreds of samples. Using this novel approach, we identify unique
sets of directed relationships in Saccharomyces cerevisiae when analyzing
genome-wide gene expression data for an intercross between a wild strain and a
lab strain. We recover novel putative network relationships between a tyrosine
biosynthesis gene (TYR1), and genes involved in endocytosis (RCY1), the spindle
checkpoint (BUB2), sulfonate catabolism (JLP1), and cell-cell communication
(PRM7). Our algorithm provides a synthesis of feature selection methods and
graphical model theory that has the potential to reveal new directed regulatory
relationships from the analysis of population level genetic and gene expression