Gene co-expression analysis for

https://academic.oup.com/bib/article/19/4/575/2888441
bootstrapping
co-splicing network：

WGCNA was also used to identify biologically relevant associations from single-cell RNA-seq data.

Intra-modular hubs are central to specific modules in the network, while inter-modular hubs are central to the entire network

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In biological terms this means that the expression of two genes is only considered to be correlated if their different splice variants show co-ordinated expression. If this is not the case, they are not considered to be co-expressed even if the overall expression levels of the genes are correlated

Regulatory network construction：

ARACNE
GENIE3

Differential co-expression analysis：
Genes that are differentially co-expressed between different sample groups are more likely to be regulators（调控因子）, and are therefore likely to explain differences between phenotypes

Differential co-expression analysis between sample groups
WGCNA,
DICER
DICER is tailored to identify module pairs that correlate differently between sample groups, e.g. modules that form one large interconnected module in one group compared with several smaller modules in another ([Figure 3D](javascript:;)). DICER may be particularly useful for time series experiments in which co-expression changes are gradual, e.g. cell cycle series experiments, where modules are specific to a particular phase and co-expressed in transitions between phases.

and DiffCoEx

DiffCoEx focuses on modules that are differentially co-expressed with the same sets of genes. The most extreme case of this behaviour is sets of genes that ‘hop’ from one set of correlated genes to another in a coordinated manner ([Figure 3E](javascript:;)). In this case, DiffCoEx would cluster ‘hopping’ genes in a similar manner
DINGO
DINGO is a more recent tool that works similarly to DiffCoEx by grouping genes based on how differently they behave in a particular subset of samples (representing e.g. a particular condition) from the baseline co-expression determined from all samples [[102](javascript:;)]. These are the most likely genes to explain different phenotypes that are associated with the two different networks. Each of the methods detects specific module changes by design, but they can also detect modular changes that they were not specifically designed for and may outperform other tools in the identification of these changes

Differential co-expression without prior grouping
biclustering

An alternative method for detecting differentially expressed clusters between subpopulations of data is biclustering. If a data set contains several biologically distinct but unknown sample groups, biclustering can identify genes with a similar expression pattern in only a sub-set of the samples without the need for prior sample classification ([Figure 3F](javascript:;)). This is particularly useful when such information is not available, as can be the case for large-scale single-cell RNA-seq experiments like those using the Drop-seq system [[139](javascript:;)] or inDrop [[140](javascript:;)]

Biclustering has also been applied to single-cell RNA-seq data. Because biclustering groups genes and samples simultaneously, it enabled the simultaneous identification of groups of cell types and corresponding gene modules to reveal 49 different cell types and their corresponding cell-type-specific gene modules, results that were later supported by experimental validation [[5](javascript:;)]. With the emergence of single-cell RNA-seq, biclustering methods may be able to identify cell-type-specific modules that are present in diseased but not in healthy cells.

Several of the tools described in this review have been compared in publications introducing a competing method. DICER has been argued to perform better than DiffCoEx and CoXpress [[4](javascript:;)] based on functional enrichment analysis of differentially expressed modules. HO-GSVD outperformed WGCNA and DiffCoEx based on its ability to detect clusters in simulated data [[136](javascript:;)]. Although biclustering is a powerful approach, it does not necessarily perform better than other network analysis methods such as WGCNA, as shown by a comparison using different tools on simulated data [[144](javascript:;)]. However, as discussed earlier, biclustering can be performed without the need for prior sample group classification.

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