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2017 ; 7
(1
): 15066
Nephropedia Template TP
Sci Rep
2017[Nov]; 7
(1
): 15066
PMID29118406
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Single-cell transcriptomic data have rapidly become very popular in genomic
science. Genomic science also has a long history of using network models to
understand the way in which genes work together to carry out specific biological
functions. However, working with single-cell data presents major challenges, such
as zero inflation and technical noise. These challenges require methods to be
specifically adapted to the context of single-cell data. Recently, much effort
has been made to develop the theory behind statistical network models. This has
lead to many new models being proposed, and has provided a thorough understanding
of the properties of existing models. However, a large amount of this work
assumes binary-valued relationships between network nodes, whereas genomic
network analysis is traditionally based on continuous-valued correlations between
genes. In this paper, we assess several established methods for genomic network
analysis, we compare ways that these methods can be adapted to the single-cell
context, and we use mixture-models to infer binary-valued relationships based on
gene-gene correlations. Based on these binary relationships, we find that
excellent results can be achieved by using subnetwork analysis methodology
popular amongst network statisticians. This methodology thereby allows detection
of functional subnetwork modules within these single-cell genomic networks.
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