Cell-type-specific predictive network yields novel insights into mouse embryonic stem cell self-renewal and cell fate.
Bottom Line: We then integrated these data into a consensus mESC functional relationship network focused on biological processes associated with embryonic stem cell self-renewal and cell fate determination.Computational evaluations, literature validation, and analyses of predicted functional linkages show that our results are highly accurate and biologically relevant.Our mESC network predicts many novel players involved in self-renewal and serves as the foundation for future pluripotent stem cell studies.
Affiliation: The Jackson Laboratory, Bar Harbor, Maine, USA.
Self-renewal, the ability of a stem cell to divide repeatedly while maintaining an undifferentiated state, is a defining characteristic of all stem cells. Here, we clarify the molecular foundations of mouse embryonic stem cell (mESC) self-renewal by applying a proven Bayesian network machine learning approach to integrate high-throughput data for protein function discovery. By focusing on a single stem-cell system, at a specific developmental stage, within the context of well-defined biological processes known to be active in that cell type, we produce a consensus predictive network that reflects biological reality more closely than those made by prior efforts using more generalized, context-independent methods. In addition, we show how machine learning efforts may be misled if the tissue specific role of mammalian proteins is not defined in the training set and circumscribed in the evidential data. For this study, we assembled an extensive compendium of mESC data: ∼2.2 million data points, collected from 60 different studies, under 992 conditions. We then integrated these data into a consensus mESC functional relationship network focused on biological processes associated with embryonic stem cell self-renewal and cell fate determination. Computational evaluations, literature validation, and analyses of predicted functional linkages show that our results are highly accurate and biologically relevant. Our mESC network predicts many novel players involved in self-renewal and serves as the foundation for future pluripotent stem cell studies. This network can be used by stem cell researchers (at http://StemSight.org) to explore hypotheses about gene function in the context of self-renewal and to prioritize genes of interest for experimental validation.
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Mentions: As a negative control, we assembled a feature set composed of a large amount of inappropriate data: 656 datasets from a broad range of mouse tissues and cell types, excluding mESCs (Table S6). This feature set was composed largely of microarray data and spanned ∼13,500 experimental conditions. To further explore the impact of using a combination of any type of mouse data, we created a feature “superset” based on a sprawling compendium of all available high-throughput mouse data, including data from our negative control, minimalist set, and mESC-specific datasets: a total of 810 datasets representing ∼14,500 conditions (Table S7). Both the negative control and superset networks achieved higher AUCs than the mESC network (0.88 and 0.86, respectively), but they also exhibited more dramatic evidence of overfitting (Figure 5A). This was not unexpected as the number of features in these test sets far exceeded the number of genes in the training set. Subsequently, the Bayes net was able to find patterns in the noise of the input data that most likely did not reflect real biology, often manifested as over-inflated results. Overfitting in networks generated using the superset of input data was even more apparent when trained on randomly generated, negative control gold standards. These test networks all achieved AUCs in the mid-to-high 0.80 s; however, overfitting was largely mitigated by regularization and bootstrap aggregation, which reduced test AUCs back to the expected random levels (∼0.5) (Figure 5B).