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Related Concept Videos

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...
Group Design02:01

Group Design

The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between the two are due to...
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...
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...
Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs01:15

Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs

Bioequivalence experimental study designs play a pivotal role in testing the effectiveness of various treatments. Key among these are the repeated measures, cross-over, carry-over, and Latin square designs. In the repeated measures design, each subject receives all treatments, allowing for temporal comparisons. This type of design is useful in reducing variability but requires careful planning to avoid bias.The cross-over design, an economical method, involves sequential administration of...

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Related Experiment Video

Updated: Jun 21, 2026

Performing Custom MicroRNA Microarray Experiments
07:04

Performing Custom MicroRNA Microarray Experiments

Published on: October 28, 2011

Importance of randomization in microarray experimental designs with Illumina platforms.

Ricardo A Verdugo1, Christian F Deschepper, Gloria Muñoz

  • 1The Jackson Laboratory, Bar Harbor, ME 04609, USA. ricardo.a.verdugo@gmail.com

Nucleic Acids Research
|July 21, 2009
PubMed
Summary

Proper randomization is crucial for accurate gene expression measurements in microarray experiments. Failing to randomize introduces systematic noise, leading to false positives and reduced detection power, even with advanced statistical methods.

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Published on: November 11, 2014

Area of Science:

  • Genomics
  • Bioinformatics
  • Experimental Design

Background:

  • Microarray gene expression measurements are susceptible to systematic noise from experimental design.
  • Illumina BeadChips assay multiple samples in an ordered series of arrays, potentially introducing bias.

Purpose of the Study:

  • To compare the impact of confounded versus randomized experimental designs on microarray data accuracy.
  • To evaluate the effectiveness of statistical modeling and normalization in correcting for design-induced biases.

Main Methods:

  • Two microarray experiments were conducted using identical samples but differing hybridization designs: one confounded and one randomized.
  • Data analysis involved comparing false-positive rates and differential expression detection power between the two designs.
  • The influence of array position effects and the efficacy of normalization were assessed.

Main Results:

  • An ordinal effect of array position on intensity values was observed in both confounded and randomized experiments.
  • The confounded design exhibited a higher rate of false-positive results compared to the randomized design.
  • Statistical modeling to correct for confounding reduced the power to detect true differential expression.

Conclusions:

  • Randomization is the most critical factor for obtaining accurate microarray results, outweighing normalization or complex statistical corrections.
  • Lack of proper randomization cannot be compensated for by post hoc analytical methods.
  • Implementing proper randomization in experimental design is essential for reliable gene expression analysis.