SSizer

SSizer evaluates sample size sufficiency in comparative biological studies by combining diagnostic accuracy, robustness, and statistical power to estimate the number of samples required for reliable discovery of discriminative features.

Key Features:

• Diagnostic accuracy: Evaluates how well a sample set can distinguish between different biological conditions or groups.
• Robustness: Assesses the stability and reliability of findings across varying sample sizes and experimental conditions.
• Statistical power: Measures the probability of detecting true effects under the chosen experimental design.
• Simulation-based estimation: Expands the input dataset via simulation to estimate the number of samples required for reliable results.
• Assessment of discriminative feature discovery: Evaluates whether the current sample size supports reproducible identification of features that discriminate between groups.

Scientific Applications:

• Sample size assessment in comparative studies: Determines whether existing samples are sufficient for comparisons between biological groups.
• Estimation of required sample numbers: Uses simulation on the input dataset to estimate the number of samples needed for reliable inference.
• Guidance for reproducible feature discovery: Supports evaluation of sample adequacy for discovering discriminative biological features.
• Evaluation of result stability: Assesses how findings change across varying sample sizes and experimental conditions.

Methodology:

Performs simulation based on the user-provided dataset to expand data and estimates required sample sizes while evaluating diagnostic accuracy, robustness across varying sample sizes and experimental conditions, and traditional statistical power.