Accurate differential analysis of transcription factor activity from gene expression

Abstract

Motivation Activity of transcriptional regulators is crucial in elucidating the mechanism of phenotypes. However regulatory activity hypotheses are difficult to experimentally test. Therefore, we need accurate and reliable computational methods for regulator activity inference. There is extensive work in this area, however, current methods have difficulty with one or more of the following: resolving activity of TFs with overlapping regulons, reflecting known regulatory relationships, or flexible modeling of TF activity over the regulon. Results We present Effector and Perturbation Estimation Engine (EPEE), a method for differential analysis of transcription factor (TF) activity from gene expression data. EPEE addresses each of these principal challenges in the field. Firstly, EPEE collectively models all TF activity in a single multivariate model, thereby accounting for the intrinsic coupling among TFs that share targets, which is highly frequent. Secondly, EPEE incorporates context-specific TF-gene regulatory networks and therefore adapts the analysis to each biological context. Finally, EPEE can flexibly reflect different regulatory activity of a single TF among its potential targets. This allows the flexibility to implicitly recover other regulatory influences such as co-activators or repressors. We comparatively validated EPEE in 15 datasets from three well-studied contexts, namely immunology, cancer, and hematopoiesis. We show that addressing the aforementioned challenges enable EPEE to outperform alternative methods and reliably produce accurate results. Availability and implementation https://github.com/Cobanoglu-Lab/EPEE. Supplementary information Supplementary data are available at Bioinformatics online.

Determinants of correlated expression of transcription factors and their target genes

“…As evidence of TF-activity, we probed for pairwise gene expression correlation among candidate TGs, predicted from the DAP-seq dataset, i.e. we estimated the activity of a TF, based on its effect on its TGs as done similarly before ( 63 , 64 ). The distribution of correlation coefficients computed among TGs proved significantly shifted to larger positive values compared to correlation levels of randomly chosen genes (Figure 1B ).…”

Characterisation of the Circulating Transcriptomic Landscape in Inflammatory Bowel Disease Provides Evidence for Dysregulation of Multiple Transcription Factors Including NFE2, SPI1, CEBPB, and IRF2

“…Traits correlated with modules included: diagnosis, gender, age, C-reactive protein [CRP], smoking, Montreal classification, and the need for treatment escalation. Three methods were used for the inference of TF activity from the expression data: Effector and Perturbation Estimation Engine [EPEE], 12 ChIP-X Enrichment Analysis 3 [ChEA3], 13 and Discriminant Regulon Expression Analysis [DoRothEA2] v2. 14 In order to maximise the true-positive rate, the EPEE and ChEA3 results were intersected [ Supplementary Methods ].…”

Atg7 deficiency in microglia drives an altered transcriptomic profile associated with an impaired neuroinflammatory response

“…However, even if the expression level of transcription factors may provide with an indication of their potential transcriptional activities in cells, accurate assessment includes the analysis of their regulon, i.e. the co-expression of their target genes [ 71 ]. The subsequent transcriptional regulatory networks analysis of the transcriptomic data obtained with Atg7 -deficient microglia exposed that the NFKB1-dependent regulon was negatively affected, implying an impairment of the NF-κB-dependent signaling pathways in these cells.…”