Thanks! Any others that infer causal relationships from time series data? (Horvath package seems more general, focused on co-expression networks.)
I am looking for software (preferably an R package) that can accept a matrix of gene expression values from a time course experiment and then compute a possible gene regulatory network based on the data.
4 answers
I am looking for the same answers. The following is the results of my literature review:
First, three review papers should be checked:
- Lopes, M. and G. Bontempi (2013). "Experimental assessment of static and dynamic algorithms for gene regulation inference from time series expression data." Front Genet 4: 303.
- Kim, Y., et al. (2014). "Inference of dynamic networks using time-course data." Brief Bioinform 15(2): 212-228.
- Liu, Z. P. (2015). "Reverse Engineering of Genome-wide Gene Regulatory Networks from Gene Expression Data." Curr Genomics 16(1): 3-22.
According to the review paper, several packages are available currently for construct transcription regulatory network from time series expression data:
- GRNInfer, Huber group LASSO, these two can construct the regulatory network from multiple time series expression data.
- TDARACNE, DPC.
- Bayes network: Banjo, BNFinder, GlobalMIT
- TESLA
- KELLER
- ARTIVA
Hope this will help you. Any suggestions on this topic are welcome!
For this case, you need to perform correlation analysis and use of any test statistics (eg. z test) to identify significant co regulation. For this case, you need large sample size (> 10) for reliable result.
Hi, did you tried RTN package?
ARACNE is pretty good
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Also I'm looking at aracne2, Califano lab. But would like to try several options.