With the rapid accumulation of gene expression data, gene functional module
identification has become a widely used approach in functional analysis. However,
tools to identify organelle functional modules and analyze their relationships are still
missing. We present a soft thresholding approach to construct networks of functional modules using gene expression
datasets, in which nodes are strongly co-expressed genes that encode proteins residing in the same subcellular localization,
and links represent strong inter-module connections. Our algorithm has three steps. First, we identify functional
modules by analyzing gene expression data. Next, we use a self-adaptive approach to construct a mixed network of functional
modules and genes. Finally, we link functional modules that are tightly connected in the mixed network. Analysis
of experimental data from Arabidopsis demonstrates that our approach is effective in improving the interpretability of
high-throughput transcriptomic data and inferring function of unknown genes.
Keywords: Arabidopsis thaliana, Organelle, Functional module, Biological network, Gene expression.
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