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power-analysis

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Calculates statistical power for high-dimensional genomics experiments (bulk RNA-seq, scRNA-seq, ATAC-seq, ChIP-seq, methylation, proteomics) under negative-binomial count models using RNASeqPower, PROPER, and simulation via powsimR, distinguishing per-gene from marginal (transcriptome-wide) power, the role of mean expression and dispersion, and the sequencing-depth-versus-replicate tradeoff. Covers simulation as the honest default for overdispersed counts, FDR-aware average power versus single-test power, observed/post-hoc power as an anti-pattern, and the winner's-curse / Type-S / Type-M consequences of underpowering. Use when planning replicate number for a sequencing experiment, deciding whether to add depth or samples, choosing closed-form versus simulation power, estimating power from pilot dispersions, or justifying replication in a grant. For clinical-trial power see clinical-biostatistics/power-and-sample-size; for the inverse sample-size question see experimental-design/samp…

When to use

• planning replicate number for a sequencing experiment
• deciding whether to add depth or samples
• choosing closed-form versus simulation power
• estimating power from pilot dispersions
• justifying replication in a grant
• bulk RNA-seq power analysis
• scRNA-seq power analysis
• ATAC-seq, ChIP-seq, methylation, and proteomics power analysis

When not to use

• clinical-trial power
• inverse sample-size question
• observed/post-hoc power calculation

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