Pepkio Power & Effect Navigator

Execute biostatistical power analysis, sample size determination, minimum detectable effect sizing, and IACUC protocol text generation for t-tests, ANOVA, proportions, and RNA-seq assays via Python or REST API.

What It Does

Determining appropriate sample sizes and statistical power ($1 - \beta \ge 0.80$) is vital for biomedical experiments, including cell culture knockouts, animal treatment trials, quantitative PCR (qPCR), ELISA, and high-throughput RNA-sequencing (RNA-seq). Planning sample sizes ensures studies possess sufficient statistical sensitivity to detect biological differences while avoiding excessive sample usage that wastes reagents or animal lives.

The pepkio-power-and-effect-navigator package connects Python data pipelines directly to the Pepkio Power & Effect Navigator engine. It calculates required sample sizes, achieved statistical power, or minimum detectable effect (MDE) across standard univariate designs and negative binomial RNA-seq models. Calculations automatically account for real-world experimental factors including sample dropout rates, unequal group allocation ratios ($n_2 / n_1$), pilot variance uncertainty corrections, and False Discovery Rate (FDR) control.

Features

• Multi-Test Statistical Suite: Supports independent two-sample t-tests, paired t-tests, one-sample t-tests, One-Way ANOVA, two-sample proportion tests, and bulk RNA-seq differential expression analysis.
• Flexible Optimization Targets: Computes required sample size per group ($n$), achieved statistical power ($1 - \beta$), or minimum detectable effect size (Cohen's $d$, Cohen's $f$, proportion delta, or RNA-seq log-fold change).
• Experimental Dropout & Allocation Adjustment: Calculates both analyzable sample count ($n_{\text{analyzable}}$) and total enrolled subjects ($n_{\text{enrolled}}$) based on expected sample loss and unequal group allocation ratios ($n_2 / n_1$).
• Pilot Variance Uncertainty Correction: Applies non-central distribution confidence bounds to variance estimates derived from small pilot studies ($n_{\text{pilot}} \ge 4$), preventing sample size underestimation in pivotal trials.
• RNA-Seq Negative Binomial Power Model: Incorporates average read depth ($\mu$), biological coefficient of variation ($\text{BCV} = \sqrt{\phi}$), total gene counts ($m$), expected differentially expressed fraction ($\pi_1$), and target False Discovery Rate ($q$).
• Protocol Justification Generator: Produces formatted sample size justification text tailored for IACUC protocol submissions, grant applications (NIH, ERC), and manuscript methods sections.
• Python API & CLI: Provides both a programmatic Python client (PepkioClient, PowerInput) and a command-line interface (pepkio-power-and-effect-navigator) for batch processing.

Installation

Install the package via pip:

pip install pepkio-power-and-effect-navigator

Set your Pepkio API key as an environment variable before running calculations:

export PEPKIO_API_KEY="your-api-key"

Quick Example

Python API usage:

from pepkio_power_and_effect_navigator import PepkioClient, PowerInput

with PepkioClient() as client:
    ttest_input = PowerInput(
        test_type="ttest",
        solve_for="n",
        alpha=0.05,
        power=0.80,
        effect_size=0.8,
        allocation_ratio=1.0,
        dropout_rate=0.10,
        ttest_design="two_sample",
        justification_template="iacuc",
    )

    result = client.run(ttest_input)
    output = result.parsed_output

    if output:
        print("Analyzable Sample Size per Group:", output.n_per_group_1)
        print("Total Enrolled Sample Size:", output.n_total_enrolled)
        print(f"Achieved Power: {output.achieved_power:.3f}")
        print("\nIACUC Protocol Justification:\n", output.justification_text)

Command-line interface usage:

pepkio-power-and-effect-navigator manifest
pepkio-power-and-effect-navigator run --example ttest_two_sample_n

Typical Use Cases

• Pre-clinical Animal Trials: Calculate group sample sizes for a multi-arm mouse study evaluated by One-Way ANOVA, adjusting for a 15% animal loss rate and generating ready-to-paste IACUC protocol justification text.
• Cell Line Knockout Validation: Determine the minimum detectable effect size (Cohen's $d$) when comparing target protein expression between wild-type and knockout lines with a fixed sample size ($n = 4$ per group).
• Bulk RNA-Seq Assay Design: Estimate biological replicate counts required to detect a 1.5-fold expression change at FDR $q = 0.05$, given mean gene read depth of 20 counts and biological coefficient of variation ($\text{BCV}$) of 0.4.
• Paired Microplate Screening: Evaluate statistical power for paired t-test designs comparing organoid responses before and after compound exposure ($n = 10$ paired samples).
• Flow Cytometry Population Analysis: Calculate sample size requirements for comparing marker-positive cell fractions ($p_1 = 5%$ vs $p_2 = 15%$) using two-sample proportion tests with an unequal $2:1$ treatment-to-control ratio.

Scientific Background

Statistical power ($1 - \beta$) measures the probability of correctly rejecting a false null hypothesis. Univariate power calculations rely on non-central $t$ or $F$ distributions, where non-centrality parameters depend on standardized effect size (Cohen's $d$ or $f$), sample size ($n$), and significance level ($\alpha$). Bulk RNA-seq data exhibit overdispersion and are modeled using negative binomial distributions where variance is a function of mean read count $\mu$ and biological coefficient of variation ($\text{BCV} = \sqrt{\phi}$), with significance thresholds adjusted for genome-wide multiple testing via False Discovery Rate (FDR) control. Standard deviation estimates from small pilot studies ($n < 10$) carry high uncertainty; applying non-central chi-squared confidence corrections ensures full-scale studies are not underpowered due to pilot sampling error.

Web Application

For researchers who prefer a graphical interface, an interactive web version is available.

Web Application: https://www.pepkio.com/tools/power-and-effect-navigator

The web interface features interactive power-versus-sample-size curves, minimum detectable effect sensitivity contour plots, downloadable CSV worksheets, shareable calculation permalinks, and interactive protocol text editors.

Documentation and Resources

GitHub Repository: https://github.com/pepkio/pepkio-power-and-effect-navigator

Web Application: https://www.pepkio.com/tools/power-and-effect-navigator

About Pepkio

Pepkio (https://www.pepkio.com/) develops software tools and bioinformatics solutions for life science researchers, including laboratory calculators and analysis services (https://www.pepkio.com/cro).

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