TFA inference poster 2020.pdf (2.32 MB)

Systematically validated inference of quantitative regulatory networks and condition-specific TF activity

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poster
posted on 20.04.2020 by Cynthia Z. Ma, Michael R. Brent
A brief overview of systematically validating inferred values for quantitative regulatory networks and transcription factor activity. The model organism used is yeast, S. cerevisiae, and the validation utilizes objective metrics made possible by two large datasets of directly perturbing transcription factors.

•Correct direction of perturbation:

In samples where TFs are deleted/induced, percent correctly inferred activity values as less/greater than in the WT sample

•Median rank percentile of perturbed TF:

In samples where TFs are deleted/induced, median rank of inferred activity levels compared to unperturbed TFs. Rank 1 = 100%

•Positive TFA-mRNA correlation:

Percent of TFs whose inferred activity positively correlates with mRNA levels of their encoding genes


Additional metrics used to emphasize the benefits of optimizing a quantitative regulatory network together with TF activity:

•Percent of literature supported edges identified:

In samples where a known regulator of TFA are deleted, the percent of TFs with a change in activity above a rank threshold

•Percent of fits in the correct direction:

In time-courses where TF-encoding genes are induced, the percent of TFs whose pattern of activity can be fit to an increasing sigmoid model above a variance explained threshold

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