Network Inference Method

GeNeCK provides 8 network inference methods that use distinct statistical strategies: 1) partial correlation methods (GeneNet, NS, SPACE); 2) likelihood methods (GLASSO, GLASSO-SF); 3) information theory-based methods (PCACMI, CMI2NI); and 4) Bayesian methods (BayesianGLASSO).

Incorporate Hub Gene

Gene networks usually have scale-free characteristics. In other words, there are usually a few hub genes regulating many others. EGLASSO and ESPACE are two methods that incorporate the prior knowledge of hub genes to enhance network inference. In these extended methods, during the covariance estimation, an additional tuning parameter is introduced to reduce the penalty on edges connected to hub genes.

Integrative Method

Ensemble-based network aggregation (ENA) is an approach to combine network reconstructed from different methods, which borrows the strength from different strategies and generally yields good results.

Gene expression data

Gene expression data (e.g. microarray and RNA-Seq) monitors the transcription activities of different genes in a cell simultaneously, which is the foundation of all statistical algorithms. >>> Example

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Example of gene expression data

Expression data requirements:

1. Only CSV file is accepted here, and the maximum size is 12MB.
2. The first row will be used as gene name. The rest of the row must be numeric type.
3. Each sample (row) must contain the same number of columns (genes) as the first row.
4. Each sample will be scaled to have mean 0 and standard deviation 1.

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Method parameters

GeNeCK offers the flexibility for user to specify the value of parameters for different methods. These parameters usually control the sparsity of the constructed network. Though they may share the same name ($\lambda$ or $\alpha$), they have different and sometimes opposite meanings in the context of different methods. Refer to each method to choose an appropriate value.

Hub genes

Incorporating prior hub gene information can enhance the network inference, but make sure those hub genes has real biological support. User can change the corresponding parameters in EGLASSO and ESPACE to control the confidence level for hub genes.

After you submit your job, you will be directed to a waiting page. You are free to leave and come back to check the job status at any time. If you provide your e-mail address, the link will also be sent to you. Once the job is done, the constructed network will be displayed on the page. You can also download the result that contains all the estimated edges. Each row represent two connected nodes. >>> Example