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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...
Sign Test for Matched Pairs01:17

Sign Test for Matched Pairs

The sign test for matched pairs offers a robust method for comparing two paired samples, often for the effects of an intervention in one of them. This method is very useful in situations where the underlying distribution of the data is unknown. The test compares two related samples—often pre- and post-treatment measurements on the same subjects—to determine if there are significant differences in their median values.
To conduct the sign test, we first calculate the differences in value between...
Significance Testing: Overview01:04

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Significance testing is a set of statistical methods used to test whether a claim about a parameter is valid. In analytical chemistry, significance testing is used primarily to determine whether the difference between two values comes from determinate or random errors. The effect of a particular change in the measurement protocol, analyst, or sample itself can cause a deviation from the expected result. In the case of a suspected deviation/outlier, we need to be able to confirm mathematically...
Wilcoxon Signed-Ranks Test for Median of Single Population01:14

Wilcoxon Signed-Ranks Test for Median of Single Population

The Wilcoxon signed-rank test for the median of a single population is a nonparametric test used to evaluate whether the median of a population differs from a specified value. Unlike parametric tests, it does not require data to follow a normal distribution, making it suitable for non-normal or small samples. The test begins by calculating the difference (d) between each observation and the hypothesized median. The absolute values of these differences are ranked in ascending order, with ties...
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...
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This number is...

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Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer
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Assessing statistical significance in microarray experiments using the distance between microarrays.

Douglas Hayden1, Peter Lazar, David Schoenfeld

  • 1Biostatistics Center, Massachusetts General Hospital, Boston, Massachusetts, United States of America. dhayden@partners.org

Plos One
|June 17, 2009
PubMed
Summary
This summary is machine-generated.

We introduce permutation tests using microarray distances to compare gene expression between populations. This method is computationally efficient for high-dimensional data and available as an R package.

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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

Area of Science:

  • Genomics
  • Bioinformatics
  • Statistical Genetics

Background:

  • Comparing gene expression profiles between populations is crucial in biological research.
  • High-dimensional microarray data presents computational challenges for statistical analysis.

Purpose of the Study:

  • To develop a computationally efficient permutation test for comparing gene expression between two populations.
  • To provide a flexible method for assessing differences in location, variability, or equivalence of gene expression.

Main Methods:

  • Utilizing pairwise distances between microarrays as the basis for permutation tests.
  • Employing the entire microarray or a subset of genes as the unit of analysis.
  • Calculating pairwise distances once to ensure computational efficiency.

Main Results:

  • The proposed permutation test effectively compares gene expression patterns.
  • The method is computationally feasible even with high-dimensional genomic data.
  • The approach allows for the assessment of location, variability, and equivalence.

Conclusions:

  • Permutation tests based on pairwise distances offer an efficient and robust method for analyzing high-dimensional gene expression data.
  • The freely available R package 'permtest' facilitates the application of this statistical approach in biological studies.