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Unprecedented technological advances in single-cell RNA-sequencing (scRNA-seq) technology have now made it possible to profile genome-wide expression in single cells at low cost and high throughput. There is substantial ongoing effort to use scRNA-seq measurements to identify the "cell types" that form components of a complex tissue, akin to taxonomizing species in ecology. Cell type classification from scRNA-seq data involves the application of computational tools rooted in dimensionality reduction and clustering, and statistical analysis to identify molecular signatures that are unique to each type. As datasets continue to grow in size and complexity, computational challenges abound, requiring analytical methods to be scalable, flexible, and robust. Moreover, careful consideration needs to be paid to experimental biases and statistical challenges that are unique to these measurements to avoid artifacts. This chapter introduces these topics in the context of cell-type identification, and outlines an instructive step-by-step example bioinformatic pipeline for researchers entering this field.