  Gene expression profiling technologies have been used in various applications
such as cancer biology. The development of gene expression profiling has
expanded the scope of target discovery in transcriptomic studies, and each
technology produces data with distinct characteristics. In order to guarantee
biologically meaningful findings using transcriptomic experiments, it is
important to consider various experimental factors in a systematic way through
statistical power analysis. In this paper, we review and discuss the power
analysis for three types of gene expression profiling technologies from a
practical standpoint, including bulk RNA-seq, single-cell RNA-seq, and
high-throughput spatial transcriptomics. Specifically, we describe the existing
power analysis tools for each research objective for each of the bulk RNA-seq
and scRNA-seq experiments, along with recommendations. On the other hand, since
there are no power analysis tools for high-throughput spatial transcriptomics
at this point, we instead investigate the factors that can influence power
analysis.
