Uncovering biochemical processes that drive the transformation of a totipotent cell into various cell types is essential to our understanding of living systems. This complex machinery determines how tissues differ in terms of their anatomy, physiology, morphology, and, more importantly, how various cellular control mechanisms contribute to the observed similarities/differences. Recent single-cell experiments have shown that tissues/cell types are related to each other through a complex hierarchical structure that blurs the defining lines of "cell type identity." Furthermore, seemingly identical cell types may exhibit different transcriptional characteristics, motivating new approaches to defining refined groupings of cells. These issues, in turn, strongly influence techniques for identification of key marker genes that characterize and/or drive tissue-specific processes.   Motivated by emerging single-cell experiments, we propose a novel statistical approach and associated methods for cell-type classification and marker identification. We present a framework for identification and removal of the shared subspace (signature of housekeeping genes) of a given expression domain. We use this framework to reduce the effect of universally expressed genes and show that this reduction step enhances the signal-to-noise ratio (SNR) for known markers. We show that repeated application of subspace reduction within groups of cell types allows us to identify highly specific markers. Finally, we employ a label propagation algorithm for automatically clustering cells and identifying putative groups of relevant cell types. We use the set of identified markers in each cluster to build tissue-specific transcriptional regulatory networks (tsTRNs), which are highly determinant of the cell type identity. Our framework allows one to start from a compendium of unknown cells, automatically identify groups of similar cell types, extract relevant markers, and construct accurate regulatory circuits for each group of putative cell types.
['Shahin Mohammadi', 'Ananth Grama']
De novo identification of cell type hierarchy with application to compound marker detection