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• W3041733494 abstract "Abstract Motivation Recently, many chromatin immunoprecipitation sequencing experiments have been carried out for a diverse group of transcription factors (TFs) in many different types of human cells. These experiments manifest large-scale and dynamic changes in regulatory network connectivity (i.e. network ‘rewiring’), highlighting the different regulatory programs operating in disparate cellular states. However, due to the dense and noisy nature of current regulatory networks, directly comparing the gains and losses of targets of key TFs across cell states is often not informative. Thus, here, we seek an abstracted, low-dimensional representation to understand the main features of network change. Results We propose a method called TopicNet that applies latent Dirichlet allocation to extract functional topics for a collection of genes regulated by a given TF. We then define a rewiring score to quantify regulatory-network changes in terms of the topic changes for this TF. Using this framework, we can pinpoint particular TFs that change greatly in network connectivity between different cellular states (such as observed in oncogenesis). Also, incorporating gene expression data, we define a topic activity score that measures the degree to which a given topic is active in a particular cellular state. And we show how activity differences can indicate differential survival in various cancers. Availability and Implementation The TopicNet framework and related analysis were implemented using R and all codes are available at https://github.com/gersteinlab/topicnet. Supplementary information Supplementary data are available at Bioinformatics online." @default.
• W3041733494 created "2020-07-16" @default.
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• W3041733494 date "2020-07-01" @default.
• W3041733494 modified "2023-10-16" @default.
• W3041733494 title "TopicNet: a framework for measuring transcriptional regulatory network change" @default.
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• W3041733494 doi "https://doi.org/10.1093/bioinformatics/btaa403" @default.
• W3041733494 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7355251" @default.
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