A Bayesian noisy logic model for inference of transcription factor activity from single cell and bulk transcriptomic data

被引:1
作者
Arriojas, Argenis [1 ,2 ,3 ]
Patalano, Susan [3 ]
Macoska, Jill [3 ]
Zarringhalam, Kourosh [1 ,3 ]
机构
[1] Univ Massachusetts, Dept Math, Boston, MA 02125 USA
[2] Univ Massachusetts, Dept Phys, Boston, MA 02125 USA
[3] Univ Massachusetts, Ctr Personalized Canc Therapy, Boston, MA 02125 USA
基金
美国国家卫生研究院;
关键词
C/EBP-DELTA; HOX GENES; CANCER; NETWORKS; PATHWAY; DIFFERENTIATION; PROSTATE; PROTEIN; YAP1; IDENTIFICATION;
D O I
10.1093/nargab/lqad106
中图分类号
Q3 [遗传学];
学科分类号
071007 ; 090102 ;
摘要
The advent of high-throughput sequencing has made it possible to measure the expression of genes at relatively low cost. However, direct measurement of regulatory mechanisms, such as transcription factor (TF) activity is still not readily feasible in a high-throughput manner. Consequently, there is a need for computational approaches that can reliably estimate regulator activity from observable gene expression data. In this work, we present a noisy Boolean logic Bayesian model for TF activity inference from differential gene expression data and causal graphs. Our approach provides a flexible framework to incorporate biologically motivated TF-gene regulation logic models. Using simulations and controlled over-expression experiments in cell cultures, we demonstrate that our method can accurately identify TF activity. Moreover, we apply our method to bulk and single cell transcriptomics measurements to investigate transcriptional regulation of fibroblast phenotypic plasticity. Finally, to facilitate usage, we provide user-friendly software packages and a web-interface to query TF activity from user input differential gene expression data: https://umbibio.math.umb.edu/nlbayes/.
引用
收藏
页数:18
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