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To test for coordinate regulation of gene homologs or modules across different stem cell types, we developed a pattern recognition algorithm capable of combining the results of any number of experiments to identify significantly and recurrently up- or down-regulated genes and gene modules in stem cells.
Graph comparison can be used to search conserved regions representing functional, orthologous modules across different species or biological systems.
(b.2) Compare the sizes of the modules across different dimensionality (k), and select the module that contains the largest number of genes.
Figure 3B also illustrates the coordinated interplay between modules across different cellular regions, e.g. cytoplasm-extracellular space and organelle-membrane interactions.
By analyzing the distribution and function of modules across different combinations of conditions, hypotheses can be made about the function and complexity of the networks underlying these, and network reconstruction attempts can be directed towards suitable time series combinations.
Past comparisons of networks, which revealed both conserved and divergent control elements for similar modules across different yeasts species [ 5], highlighted the importance for constructing a large-scale regulatory network for fission yeast.
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This indicates that Module 7 represents an efficient (or short) communication pathway to link different proteins and modules across the different locations in this network.
My-Inflamome modules operate across different cellular regions, including the nucleus, cytoplasm and the extra-cellular space.
Comparative network analysis methods allow us to transfer existing knowledge on well-studied organism to less-studied ones and they have the potential to detect potential functional modules conserved across different organisms and species [ 1, 2, 15].
The WGCNA package includes statistical tests to analyze module preservation across different datasets [ 76].
Eigengene networks can be used for describing module relationships in a single data set (single eigengene network analysis) or they can be used to compare module relationships across different data sets (differential eigengene network analysis).
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elements across different
components across different
samples across different
contents across different
nodes across different
examples across different
platforms across different
modes across different
dimensions across different
installations across different
modules across clinical
modules across multiple
modules across pMUM001
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