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Table 1 Statistical power to detect differential expression varies with effect size, sequencing depth and number of replicates

From: A survey of best practices for RNA-seq data analysis

  Replicates per group
  3 5 10
Effect size (fold change)
 1.25 17 % 25 % 44 %
 1.5 43 % 64 % 91 %
 2 87 % 98 % 100 %
Sequencing depth (millions of reads)
 3 19 % 29 % 52 %
 10 33 % 51 % 80 %
 15 38 % 57 % 85 %
  1. Example of calculations for the probability of detecting differential expression in a single test at a significance level of 5 %, for a two-group comparison using a Negative Binomial model, as computed by the RNASeqPower package of Hart et al. [190]. For a fixed within-group variance (package default value), the statistical power increases with the difference between the two groups (effect size), the sequencing depth, and the number of replicates per group. This table shows the statistical power for a gene with 70 aligned reads, which was the median coverage for a protein-coding gene for one whole-blood RNA-seq sample with 30 million aligned reads from the GTEx Project [214]