Abstract
Single cell trajectory analysis is a computational approach that orders cells along a pseudotime axis. This temporal modeling approach allows the characterization of transitional processes such as lineage development, response to insult, and tissue regeneration. The concept can also be applied to resolve spatial organization of cells within the originating tissue. Known as temporal and spatial transcriptomics, respectively, these methods belong to the most powerful analytical techniques for quantitative gene expression data currently available. Here, we discuss three different approaches: principal component analysis, the 'Monocle' algorithm, and self-organizing maps. We use a previously published qRT-PCR dataset of single neuroblast cells isolated from the developing mouse inner ear to highlight the basic features of the three methods and their individual limitations, as well as the distinct advantages that make them useful for research on the inner ear. The complex developmental morphogenesis of the inner ear and its specific challenges such as the paucity of cells as well as important open questions such as sensory hair cell regeneration render this organ a prime target for single cell trajectory analysis strategies.