High-throughput genomic technologies are rapidly evolving including the areas of DNA and RNA sequencing. Novel types of complex data are being quickly generated and require novel methods for quality control and analysis. We are currently focused on developing and/or applying methods for identifying genomic alterations in cancer, quantifying the mutagenic effect of carcinogens, and characterizing cellular heterogeneity using single cell RNA sequencing. We have developed the CELDA framework (CEllular Latent Dirichlet Allocation), which can be used to identify hidden transcriptional states and cellular populations in count-based single-cell RNA-seq data. A beta version of this software can be accessed at GitHub.
