Impact of sample size and sequencing depth on statistical power
One of the most important decisions to be made during the design of an RNA-Seq experiment is the number of samples to sequence. A set of “truly differentially expressed genes” or TDEGs was established based on a stringent FDR threshold and replication in both sites using different DEG detection methods. This strategy to define a “truth set” has been used effectively by prior studies examining the relationship of RNA-Seq parameters with power (i. ii. iii. While sequencing depth did not profoundly influence statistical power, we did observe that reducing sequencing depth had a stronger influence on transcript representation. We found that even at relatively deep sequencing depths, only approximately 65% of the total fraction of PBMC transcripts are reported in any individual sample. Decreasing sequencing depths led to a loss of content of approximately 5% of identifiable genes for each 10 M drop in depth. For example, reducing coverage from 30 M reads to 10 M reads detected 2,515 fewer genes. Taken together with our power simulations, this analysis demonstrates that while sequencing depth does not alter overall power, a substantial proportion of genes with lower expression will be lost or provide unreliable quantification. The true impact on the biological interpretation of reduced transcript representation needs to be evaluated for each RNA-Seq dataset independently. In summary, these results demonstrated that while it is possible to reduce costs in an RNA-Seq experiment with an overall loss of power by decreasing coverage, this step must be carefully considered as there is a tangible loss of gene content. As the cost-per-base continues to drop as sequencing technology improves, the relative savings in reducing sequencing depth are becoming minimal compared to the cost of gene-content loss in an RNA-Seq experiment.(iv) The strength of the biological signal appeared to be the greatest influencer on type-I error, but this effect was balanced by the power and FDR. For example, Day 14 shows the highest type-I-error rates (although still below 2% in all cases), but this is accompanied by the highest power and lowest FDR. Sequencing depth had a negligible impact on the type-I-error rate, consistent with the findings for power and FDR.