Probabilistic inference of transcription factor concentrations and gene-specific regulatory activities
By G. Sanguinetti, Neil D. Lawrence, Magnus Rattray. 2006
102 citations recorded by the source corpus.
MOTIVATION Quantitative estimation of the regulatory relationship between transcription factors and genes is a fundamental stepping stone when trying to develop models of cellular processes. Recent experimental high-throughput techniques, such as Chromatin Immunoprecipitation (ChIP) provide important information about the architecture of the regulatory networks in the cell. However, it is very difficult to measure the concentration levels of transcription factor proteins and determine their regulatory effect on gene transcription. It is therefore an important computational challenge to infer these quantities using gene expression data and network architecture data. RESULTS We develop a probabilistic state space model that allows genome-wide inference of both transcription factor protein concentrations and their effect on the transcription rates of each target gene from microarray data. We use variational inference techniques to learn the model parameters and perform posterior inference of protein concentrations and regulatory strengths. The probabilistic nature of the model also means that we can associate credibility intervals to our estimates, as well as providing a tool to detect which binding events lead to significant regulation. We demonstrate our model on artificial data and on two yeast datasets in which the network structure has previously been obtained using ChIP data. Predictions from our model are consistent with the underlying biology and offer novel quantitative insights into the regulatory structure of the yeast cell. AVAILABILITY MATLAB code is available from http://umber.sbs.man.ac.uk/resources/puma
Subfields: Molecular Biology
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In Molecular Biology
Citations in the corpus
- Variational Inference: A Review for Statisticians (2016, methodology): “…VI has been used in genome-wide association studies (Logsdon, Hoffman, and Mezey 2010; Carbonetto and Stephens 2012), regulatory network analysis (Sanguinetti, Lawrence, and Rattray 2006), motif detection (Xing et al. 2004), phylogenetic hidden Markov models (Jojic et al. 2004), population…”