: Fig. S10). Taken together, we showed that the NetAct-constructed GRN model captures the multiple cellular state transitions during macrophage polarization.
In conclusion, we show that NetAct can identify the core TF-based GRN using both the literature-based TF-target database and the gene expression data. We also demonstrate how RACIPE-based mathematical modeling complements NetAct-based GRN inference in elucidating the dynamical behaviors of the inferred GRNs. Together, these two methods can be applied to infer biologically relevant regulatory interactions and the dynamical behavior of biological processes.
One of the key components of NetAct is a pre-compiled TF-target gene set database. Here, we have evaluated different types of TF-target databases in identifying knocked-down TFs using publicly available transcriptomics datasets. In this test, we have considered databases derived from the literature, gene co-expression, cis-motif prediction, and TF-binding motif data. Our benchmark tests suggest that the literature-based database clearly outperformed the other databases.
NetAct also has a unique approach to infer the TF activity from the gene expression of the target genes with the consideration of activation/inhibition nature. From our in silico benchmark tests, we found that NetAct outperforms major activity inference methods, owing to the design of the filtering step and the use of a high-quality TF-target database.
One potential issue is the assignment of the sign of TF activity, as it is algorithmically assigned according to the correlation with TF expression. In the case where the TF expression is very noisy or the expression is completely unrelated to TF activity, the sign assignment might be inaccurate. To deal with this issue, we have devised a semi-manual approach that identifies the sign of TF activity according to the sign of other interacting TFs.
Source: The AI Report (theaireport.net)
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