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Sample Size Calculation

Sample Size Calculation

Knowledge of the sample size is the first requirement to conduct random sampling or an experiment. The sample size is the total number of units, observations, or groups (in some cases) used to get the data to estimate a population parameter. As the name suggests, the sample size is that of the sample drawn from the population and differs from the population size.
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...

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Related Experiment Video

Updated: Jan 4, 2026

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

SCOPIT: sample size calculations for single-cell sequencing experiments.

Alexander Davis^1,2, Ruli Gao¹, Nicholas E Navin^3,4

¹Department of Genetics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.

BMC Bioinformatics

|November 14, 2019

Summary

This summary is machine-generated.

Planning single-cell sequencing experiments is simplified with SCOPIT. This tool calculates cell sampling probabilities, ensuring adequate cell numbers for accurate subpopulation analysis.

Keywords:

Multinomial distributions Sample size Single cell sequencing

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Determining optimal cell numbers is crucial for single-cell DNA and RNA sequencing experiments.
Assessing if sufficient cells were sampled is necessary for accurate subpopulation analysis.

Purpose of the Study:

To develop a tool for calculating the probability of sampling at least a defined number of cells from each subpopulation.
To aid in prospectively planning and retrospectively evaluating single-cell sequencing experiments.

Main Methods:

Developed SCOPIT (Single-Cell One-sided Probability Interactive Tool), an interactive web application.
Utilized multinomial distribution for probability calculations.
Created an R package named pmultinom for scripting these calculations.

Main Results:

SCOPIT calculates required probabilities for single-cell sequencing experimental design.
The tool provides an intuitive procedure for evaluating cell sampling adequacy.

Conclusions:

SCOPIT offers a fast and simple method for planning and evaluating single-cell experiments.
The web application is accessible at navinlab.com/SCOPIT for practical use.