Functional Transcription Factor Target Networks Illuminate Control of Epithelial Remodelling
Abstract
1. Introduction
2. Results and Discussion
2.1. A Comprehensive Drosophila melanogaster Functional Gene Network (DroFN)
2.2. Prediction of Functional Transcription Factor Targets
2.3. Analysis of EMT Transcription Factors and Highly Occupied Target (HOT) Regions
2.4. Genome-Scale Functional Transcription Factor Target Networks
2.5. Breast Cancer Subtypes are Recovered by Unsupervised Clustering with Orthologous Snail and Twist Functional Targets
2.6. Integrating NetNC Functional Target Networks and Breast Cancer Transcriptome Profiling
2.7. Novel Twist and Snail Functional Targets Influence Invasion in a Breast Cancer Model of EMT
3. Methods
3.1. A Comprehensive D. Melanogaster Functional Gene Network (DroFN)
3.2. Network Neighbourhood Clustering (NetNC) Algorithm
- 1.
- A two times two contingency table is derived for each edge Sij by conditioning on the Boolean connectivity of nodes in S to Si and Sj. Nodes Si and Sj are not counted in the contingency table.
- 2.
- Exact hypergeometric p-values [46] for enrichment of the nodes in S that have edges to the nodes Si and Sj are calculated using Fisher’s Exact Test from the contingency table. Therefore, a distribution of p-values (H1) is generated for all edges Sij.
- 3.
- The NetNC edge-centric analysis setting (NetNC-FTI) employs positive false discovery rate [157] and an iterative minimum cut procedure [158] to derive clusters as follows:
- (a)
- Subgraphs with the same number of nodes as S are resampled from G, application of steps 1 and 2 to these subgraphs generates an empirical null distribution of neighbourhood clustering p-values (H0). This H0 accounts for the effect of the sample size and the structure of G on the Sij hypergeometric p-values (pij). Each NetNC run on TF_ALL in this study resampled 1000 subgraphs to derive H0.
- (b)
- Each edge in S is associated with a positive false discovery rate (q) estimated over pij using H1 and H0. The neighbourhood clustering subgraph C is induced by edges where the associated q ≤ Q. Therefore, Q is the NetNC-FTI threshold for false discovery rate (q).
- (c)
- An iterative minimum cut procedure [158] is applied to C until all components have density greater than or equal to a threshold Z. Edge weights in this procedure are taken as the negative log p-values from H1. Therefore, Z is the threshold for the density of network components output by NetNC-FTI.
- (d)
- As described below, thresholds Q and Z were chosen to optimise the performance of NetNC on the “Functional Target Identification” task using training data taken from KEGG. Connected components with less than three nodes are discarded, in line with common definitions of a “cluster”. Remaining nodes are taken as functionally coherent.
- 4.
- The node-centric, parameter-free approach (NetNC-FBT) proceeds by calculating degree-normalised node functional coherence scores (NFCS) from H1, then identifies statistical modes of the NFCS distribution using Gaussian Mixture Modelling (GMM) [159].
- (a)
- The node functional coherence score (NFCS) is calculated by summation of Sij p-values in H1 (pij) for fixed Si, normalised by the Si degree value in S (di) (Equation (2)):
- (b)
- GMM is applied to identify structure in the NFCS distribution. Expectation-maximization fits a mixture of Gaussians to the distribution using independent mean and standard deviation parameters for each Gaussian [159,160]. Models with 1..9 Gaussians are fitted and the final model selected using the Bayesian Information Criterion (BIC).
- (c)
- Nodes in high-scoring statistical mode(s) are predicted to be “Functionally Bound Targets” (FBTs) and retained. Firstly, any mode at NFCS < 0.05 is excluded because this typically represents nodes with no edges in S (where NFCS = 0). A second step eliminates the lowest scoring mode if >1 mode remains. Very rarely a unimodal model is returned, which may be due to a large non-Gaussian peak at NFCS = 0 confounding model fitting; if necessary, this is addressed by introducing a tiny Gaussian noise component (SD = 0.01) to the NFCS = 0 nodes to produce NFCS_GN0. GMM is performed on NFCS_GN0 and nodes eliminated according to the above procedure on the resulting model. This procedure was developed following manual inspection of results on training data from KEGG pathways with “synthetic neutral target genes” (STNGs) as nodes resampled from G (TRAIN-CL).
3.3. Estimating Positive False Discovery Rate for Hypergeometric Mutual Clustering p-Values
3.4. Estimating Local False Discovery Rate from Global False Discovery Rate
3.5. Median Difference and Correlation between Estimates of Functional Binding from NetNC Functional Target Identification and Local False Discovery Rate
3.6. NetNC Benchmarking Data
3.7. Comparison of Synthetic NetNC Benchmark to Experimentally Determined TF Binding Data
3.8. NetNC-FTI Parameter Optimisation
3.9. Performance on Blind Test Data
3.10. Subsampling of Transcription Factor Binding Datasets and Statistical Testing
3.11. Transcription Factor Binding and Notch Modifier Datasets
3.12. Breast Cancer Transcriptome Datasets and Molecular Subtypes
3.13. Invasion Assays for Validation of Genes Selected from NetNC Results
3.14. Data and Software Availability
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Dataset | Predicted Functional Targets ‡ | ||||
---|---|---|---|---|---|
Name | Developmental Time Period(s) | Total Candidate Target Genes * | Candidate Target Genes in DroFN | NetNC-FTI | NetNC-lcFDR (95% CI) |
twi_1–3h_hiConf | 1–3 h | 755 | 664 | 202 (30%) | 37% (32–39%) |
twi_2–6h_intersect | 2–4 h and 4–6 h | 743 | 615 | 241 (39%) | 31% (25–33%) |
twi_2–4h_intersect | 2–4 h only (not 4–6 h) | 1028 | 801 | 182 (23%) | 19% (14–21%) |
twi_4–6h_intersect | 4–6 h only (not 2–4 h) | 1026 | 818 | 126 (15%) | 20% (14–22%) |
HOT | 0–12 h + | 1648 | 677 | 174 (26%) | 27% (19–28%) |
twi_2–3h_union | 2–3 h | 2285 | 1848 | 424 (23%) | 21% (17–22%) |
sna_2–3h_union | 2–3 h | 1424 | 1158 | 226 (20%) | 20% (15–21%) |
twi_2–4h_Toll10b | 2–4 h | 1578 | 1238 | 279 (23%) | 25% (20–25%) |
sna_2–4h_Toll10b | 2–4 h | 1822 | 1488 | 211 (14%) | 13% (5–14%) |
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Overton, I.M.; Sims, A.H.; Owen, J.A.; Heale, B.S.E.; Ford, M.J.; Lubbock, A.L.R.; Pairo-Castineira, E.; Essafi, A. Functional Transcription Factor Target Networks Illuminate Control of Epithelial Remodelling. Cancers 2020, 12, 2823. https://doi.org/10.3390/cancers12102823
Overton IM, Sims AH, Owen JA, Heale BSE, Ford MJ, Lubbock ALR, Pairo-Castineira E, Essafi A. Functional Transcription Factor Target Networks Illuminate Control of Epithelial Remodelling. Cancers. 2020; 12(10):2823. https://doi.org/10.3390/cancers12102823
Chicago/Turabian StyleOverton, Ian M., Andrew H. Sims, Jeremy A. Owen, Bret S. E. Heale, Matthew J. Ford, Alexander L. R. Lubbock, Erola Pairo-Castineira, and Abdelkader Essafi. 2020. "Functional Transcription Factor Target Networks Illuminate Control of Epithelial Remodelling" Cancers 12, no. 10: 2823. https://doi.org/10.3390/cancers12102823
APA StyleOverton, I. M., Sims, A. H., Owen, J. A., Heale, B. S. E., Ford, M. J., Lubbock, A. L. R., Pairo-Castineira, E., & Essafi, A. (2020). Functional Transcription Factor Target Networks Illuminate Control of Epithelial Remodelling. Cancers, 12(10), 2823. https://doi.org/10.3390/cancers12102823