Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs01:20

Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs

Bioequivalence experimental study designs are crucial methodologies used in evaluating and comparing the bioavailability of different drug products. These designs are categorized into various types: completely randomized, randomized block, repeated measures, cross and carry-over, and Latin square designs.Completely randomized designs involve randomly allocating treatments to all subjects participating in the experiment. This allocation is achieved by assigning unique random numbers to subjects...
One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:
Sample Size Calculation01:19

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...
Randomized Experiments01:13

Randomized Experiments

The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
DNA Microarrays02:34

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Pathogenic characterization and genome-wide evolutionary analysis of Bacillus anthracis from a cutaneous anthrax case in Inner Mongolia, China.

BMC microbiology·2026
Same author

A large language model-based detection method for poisoning attacks in recommender systems.

Scientific reports·2026
Same author

Time-Resolved Transcriptomic Profiling of Surgical Wounds Identifies Stage-Specific Therapeutic Targets for Residual Ovarian Cancer.

Pharmaceutics·2026
Same author

CgCFEM1 and CgCFEM2 modulate virulence in Colletotrichum gloeosporioides by integrated regulation of TOR and cAMP-PKA signaling pathways.

BMC microbiology·2026
Same author

Compliance with an enhanced recovery pathway and postoperative outcomes in elderly colorectal cancer patients: a real-world cohort and structural pathway analysis.

Updates in surgery·2026
Same author

Sound of nurturing: Advanced photoacoustic and ultrasound imaging of placental hemodynamics.

Placenta·2026
Same journal

OpenIMC: an open-source platform for analyzing single-cell and spatial proteomics by imaging mass cytometry.

BMC bioinformatics·2026
Same journal

NAP: an open source pipeline for cross-domain microbiome profiling using Nanopore sequencing-derived amplicon data.

BMC bioinformatics·2026
Same journal

SurvGME: an R package for survival analysis with graphical and measurement error models.

BMC bioinformatics·2026
Same journal

SimMapNet: a Bayesian framework for gene regulatory network inference using gene ontology similarities as external hint.

BMC bioinformatics·2026
Same journal

Dual channel drug-drug interactions extraction based on cross attention.

BMC bioinformatics·2026
Same journal

FeSseqdb: a curated sequence-level database and interpretable machine learning framework for identifying iron-sulfur proteins.

BMC bioinformatics·2026
See all related articles

Related Experiment Video

Updated: Jun 22, 2026

Competitive Genomic Screens of Barcoded Yeast Libraries
11:59

Competitive Genomic Screens of Barcoded Yeast Libraries

Published on: August 11, 2011

Sample size calculation for microarray experiments with blocked one-way design.

Sin-Ho Jung1, Insuk Sohn, Stephen L George

  • 1Department of Biostatistics and Bioinformatics, Duke University Medical Center, North Carolina 27710, USA. sinho.jung@duke.edu

BMC Bioinformatics
|May 30, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for identifying differentially expressed genes in microarray experiments with blocked designs. The proposed sample size calculation ensures accurate gene discovery while controlling the false discovery rate (FDR).

More Related Videos

Performing Custom MicroRNA Microarray Experiments
07:04

Performing Custom MicroRNA Microarray Experiments

Published on: October 28, 2011

Related Experiment Videos

Last Updated: Jun 22, 2026

Competitive Genomic Screens of Barcoded Yeast Libraries
11:59

Competitive Genomic Screens of Barcoded Yeast Libraries

Published on: August 11, 2011

Performing Custom MicroRNA Microarray Experiments
07:04

Performing Custom MicroRNA Microarray Experiments

Published on: October 28, 2011

Area of Science:

  • Genomics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Microarray analysis aims to identify differentially expressed genes across various conditions.
  • Existing statistical methods for assessing treatment effects in microarrays have limitations, especially with blocked designs.

Purpose of the Study:

  • To develop a statistical method for discovering differentially expressed genes among multiple treatments in blocked microarray experiments.
  • To propose a sample size calculation method tailored for blocked one-way ANOVA designs in microarrays.

Main Methods:

  • Utilized the blocked one-way ANOVA F-statistic to test for differential gene expression among K treatments.
  • Employed a permutation method to calculate marginal p-values, accounting for block effects.
  • Controlled for multiple testing by managing the false discovery rate (FDR).

Main Results:

  • Developed a sample size calculation formula for blocked microarray experiments.
  • The formula determines the necessary sample size based on specified FDR levels and gene effect sizes.
  • Simulations confirmed the formula's accuracy in achieving true discoveries and controlling FDR.

Conclusions:

  • The proposed sample size calculation method is effective for blocked microarray designs.
  • Accurate gene discovery can be achieved while maintaining desired FDR control.
  • This method aids in planning robust microarray experiments with multiple treatment groups and block effects.