Graph theory analysis of single cell transcriptomes define islet signaling networks and cell identity

Several challenges face bioinformaticians on a regular basis. One of these is unsupervised clustering. In RNA sequencing (RNAseq), this may come in the form of blindly sequencing single cells without a priori knowledge of the cell types being sequenced. Here we create new methods to address this problem that show increased accuracy and speed compared to competing methods. We also have developed a methodology for discovering non-parametric networks which represent relationships between the variables that have been measured across samples. In the context of RNAseq, this is the expression relationships between genes (for example a positive or negative Spearman correlation). We have packaged these techniques into a software tool called PyMINEr. We show the implementation of PyMINEr here in the analysis of single cell RNAseq (scRNAseq), and integrate this dataset with others to yield novel insights to the signaling networks among within and between pancreatic islet cell types. Additionally we used this data to predict the cell type specific importance of Type 2 Diabetes (T2D) single nucleotide polymorphisms (SNPs). Lastly we have demonstrated the use of PyMINEr’s analytic techniques in discovering genetic circuitry underlying the transcriptional networks of two transcription factors (NeuroD1 and Pdx1) in beta cells. We utilized a RNA interference to modulate the expression of these transcription factors in a beta cell line (MIN6), and observe the changes in the transcriptome over time. We used this data to generate graph network models of transcription and integrated them with ChIP-seq of these transcription factors; this enabled annotation of the functional binding sites of these transcription factors. Furthermore, this approach has enabled the discovery of regulators of beta and alpha cell identity. Overall, we have developed novel informatics methods which can be applied to complex datasets to guide bench experiments towards to discovery of molecular signaling networks.