Molly Robinson: Adapting Weighted Gene Co-Expression Network Analysis for Next Generation Sequencing

New technologies such as single-cell RNA-sequencing (scRNA-seq) have become vital to the understanding of cell type heterogeneity of the brain. One limitation of this technique is that the resulting datasets are sparse. That is, genes often have read counts of zero in a given cell. Computational challenges arise when sparse data sets are analyzed with Weighted Gene Co-Expression Network Analysis (WGCNA), a technique that has been used to study the underlying genetic network of bulk data sets. This project aims to study how sparsity degrades the performance of WGCNA. This is done by modifying datasets where the method has been successfully applied. Gene clusters, or modules, of the generated network can then be tracked from the original dataset across varying levels of sparsity. This gives insight into how the network construction is altered when a sparse dataset is used. We will then study imputation and smoothing techniques to recover performance. Finally, we will seek to determine significant statistical features of the data that predict model performance.