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Related Concept Videos

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...
Student t Distribution01:31

Student t Distribution

The population standard deviation is rarely known in many day-to-day examples of statistics. When the sample sizes are large, it is easy to estimate the population standard deviation using a confidence interval, which provides results close enough to the original value. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
The Student t distribution was developed by William S. Goset (1876–1937) of the...
Sampling Distribution01:12

Sampling Distribution

Given simple random samples of size n from a given population with a measured characteristic such as mean, proportion, or standard deviation for each sample, the probability distribution of all the measured characteristics is called a sampling distribution. How much the statistic varies from one sample to another is known as the sampling variability of a statistic. You typically measure the sampling variability of a statistic by its standard error. The standard error of the mean is an example...
Polymers: Molecular Weight Distribution01:10

Polymers: Molecular Weight Distribution

For any given polymer, the weight average molecular weight (Mw) is higher than, if not equal to, the number average molecular weight (Mn). The only situation in which the weight average molecular weight and the number average molecular weight are equal is when a polymer consists only of chains with equal molecular weight. However, this never happens in a synthetic polymer, since it is difficult to control the polymerization process up to a molecular level with accuracy to a hundred percent.
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the Guinness...

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Related Experiment Video

Updated: Jul 20, 2026

DNA Microarrays: Sample Quality Control, Array Hybridization and Scanning
09:27

DNA Microarrays: Sample Quality Control, Array Hybridization and Scanning

Published on: March 15, 2011

Generalization of DNA microarray dispersion properties: microarray equivalent of t-distribution.

Jaroslav P Novak1, Seon-Young Kim, Jun Xu

  • 1McGill University and Genome Québec Innovation Centre, 740 Docteur Penfield Avenue, Montreal, Québec, H3A 1A4, Canada. jaroslav.novak@mail.mcgill.ca

Biology Direct
|September 9, 2006
PubMed
Summary

DNA microarray gene expression data typically follows a Gaussian distribution, with minor deviations. This study establishes universal coefficients for probability intervals, offering a reliable measure of data dispersion and validating Affymetrix technology.

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Area of Science:

  • Genomics
  • Biostatistics
  • Bioinformatics

Background:

  • DNA microarrays generate extensive gene expression data crucial for research.
  • Statistical methods are vital for analyzing this data due to its volume and variability.
  • Characterizing the dispersion of DNA microarray data has been underexplored.

Purpose of the Study:

  • To characterize the dispersion of DNA microarray data.
  • To establish universal coefficients for probability intervals.
  • To validate the accuracy of gene expression measurements from Affymetrix technology.

Main Methods:

  • Analysis of gene expression data from 682 Affymetrix GeneChips.
  • Application of a consecutive sampling method to approximate standard deviation.
  • Determination of probability interval boundaries and invariant coefficients (K(alpha)).

Main Results:

  • Gene expression variability generally follows a Gaussian distribution, with 5-15% of samples deviating.
  • Consecutive sampling yields standard deviation equivalent to individual gene analysis.
  • Derived probability interval coefficients are invariant across different sample types, conditions, and chips, correlating closely with Student's t-distribution.

Conclusions:

  • Non-systematic variations in gene expression data exhibit Gaussian distribution.
  • Invariant K(alpha) coefficients provide a universal measure of data dispersion.
  • The consistency of K(alpha) distributions suggests Affymetrix data accurately reflects underlying biological processes.