WattEG

Power analysis for element–gene pairs in single-cell CRISPR screens, built on sceptre.

Given a sceptre object from a completed screen, it answers: for each element–gene pair, if the element really did reduce expression of the gene by X%, how often would we have detected it? The output is a per-pair power estimate with a confidence interval, plus the smallest tested effect size at which each pair becomes detectable.

The method is a generalisation of the analysis in DC_TAP_Paper, which was written for one specific dataset.

How it works

For each perturbation target, and for each simulation:

Draw per-gRNA effect sizes around the requested effect size, so guides targeting the same element differ in strength.
Simulate a count matrix from each gene’s mean and dispersion, applying those effect sizes to the perturbed cells.
Run sceptre’s discovery analysis on the simulated data.
Record whether the pair would have been called significant.

Power is the fraction of simulations in which it would have been. Because that is a binomial proportion over a finite number of simulations, every estimate is reported with a Wilson confidence interval.

Quickstart

# 1. environment -- one Python package plus Nextflow, no R
pixi install

# 2. the whole pipeline
nextflow run . -profile sherlock -params-file config/config.yml

Or step by step, each with --help:

watteg-prepare-sim-input --dataset export/dataset.h5mu --outdir prepared/
watteg-split-pairs --pairs prepared/pairs.tsv --n-splits 280 --outdir splits/
watteg-run-power-simulation \
    --prepared prepared/ --pairs splits/split_001.tsv \
    --effect-size 0.15 --reps 100 --seed 20250812 \
    --out sim/split_001_es0.15.tsv
watteg-compute-power --simulations sim/ \
    --threshold-file prepared/discovery_threshold.txt --out power_es0.15.tsv
watteg-summarize-power --power power_es0.15.tsv power_es0.2.tsv \
    --sim-input prepared/sim_input.h5 --out power_summary.tsv

See Usage for all parameters, including how to produce the .h5mu from a sceptre object.

Input

One file: a .h5mu, exported once from a sceptre object on which assign_grnas() and run_qc() have been called, using grna_integration_strategy = "union". Everything else — the discovery pairs, the gRNA-to-target mapping, the significance threshold, the analysis parameters — travels with it.

Producing that export is the only step that needs R, and it is not part of the pipeline; see Usage. It must be written with --all-genes --all-cells, for reasons that are not cosmetic: the size factors reduce over the whole gene set, and are computed against a geometric mean over every cell including the ones QC removed.

Choosing parameters

The one parameter that changes your results is num_replicates; n_splits and reps_per_chunk only change how the work is divided, and that is checked end to end rather than assumed — see Reproducibility in Usage. Read Choosing num_replicates before picking one — the right value depends on whether you report aggregate power, per-pair power, or make per-pair decisions at a cutoff.

Two settings deserve a warning, both documented in Methods:

alpha should normally be left unset so the threshold is derived from the real discovery results, which reflects the multiple-testing correction actually applied.
expression_model should be left at fitted. size_factor exists only to reproduce sweeps produced before 2026-09-21; it mixes two statistical models of the same data and reproduces 86.5% of the observed count variance against the fitted model’s 99.5%.

n_control_cells and cell_batches are gone. They sampled control cells to buy speed, cost 21-60% of power, and were never on; the Python path is fast enough that the trade has no upside. See Plan - pysceptre backend section 9 for why dropping the batch stratification does not expose the arms to drift.

Documentation

Usage — every parameter, and running each step by hand
Output — every column of every output file
Choosing num_replicates — precision, cost, and the confidence intervals
Methods — how the simulation is parameterised
Development — environment, sceptre pinning, conventions