The depth of sequencing required for specific applications can be extrapolated from a pilot experiment.[1] Data generation artifacts (also known as technical variance): The reagents (e.g., library preparation kit), personnel involved, and type of sequencer (e.g., Illumina, Pacific Biosciences) can result in technical artifacts that might be mis-interpreted as meaningful results. As with any scientific experiment, it is prudent to conduct RNA-Seq in a well controlled setting. If this is not possible or the study is a meta-analysis, another solution is to detect technical artifacts by inferring latent variables (typically principal component analysis or factor analysis) and subsequently correcting for these variables.[47] Data management: A single RNA-Seq experiment in humans is usually on the order of 1 Gb.[48] This large volume of data can pose storage issues.