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Related Concept Videos

Variability: Analysis01:11

Variability: Analysis

Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
Variance01:15

Variance

The deviations show how spread out the data are about the mean. A positive deviation occurs when the data value exceeds the mean, whereas a negative deviation occurs when the data value is less than the mean. If the deviations are added, the sum is always zero. So one cannot simply add the deviations to get the data spread. By squaring the deviations, the numbers are made positive; thus, their sum will also be positive.The standard deviation measures the spread in the same units as the data.
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...
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures from...
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...
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
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An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations
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Published on: November 3, 2010

Variance component estimation for mixed model analysis of cDNA microarray data.

Barbara Sarholz1, Hans-Peter Piepho

  • 1General Motors Powertrain Germany GmbH, Rüsselsheim, Germany.

Biometrical Journal. Biometrische Zeitschrift
|November 28, 2008
PubMed
Summary

This study introduces a new method to improve gene expression analysis in microarrays by estimating variance components across genes. This approach enhances the detection of differentially expressed genes, especially with limited replicates.

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Area of Science:

  • Genomics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Microarrays are crucial for gene expression quantification.
  • Limited replicates often lead to poor variance estimates in gene-wise mixed models.
  • Combining information across genes is desirable for robust analysis.

Purpose of the Study:

  • To develop a method for estimating variance components across genes in linear mixed models.
  • To improve the accuracy of variance estimation, especially with few arrays.
  • To enhance the detection of differentially expressed genes.

Main Methods:

  • Proposed a method for variance component estimation across genes for linear mixed models with two random effects.
  • Assumed variance components follow a log-normal distribution.
  • Adopted an empirical Bayes approach using the expectation of the posterior distribution for estimation.

Main Results:

  • The proposed method significantly increases the detection of differentially expressed genes compared to gene-wise estimates.
  • Improved detection is most pronounced in studies with a small number of arrays.
  • The method demonstrated effectiveness on a real maize endosperm dataset.

Conclusions:

  • The novel variance component estimation method enhances statistical power in microarray gene expression studies.
  • This approach is particularly beneficial for studies with limited sample sizes.
  • The method offers a valuable tool for more accurate gene expression analysis.