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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...
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
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.
What is Gene Expression?01:42

What is Gene Expression?

Overview
Gene expression is the process in which DNA directs the synthesis of functional products, that is, proteins. Cells can regulate gene expression at various stages. It allows organisms to generate different cell types and enables cells to adapt to internal and external factors.
Genetic Information Flows from DNA to RNA to Protein
A gene is a stretch of DNA that serves as the blueprint for functional RNAs and proteins. Since DNA is made up of nucleotides and proteins consist of amino...
What is Gene Expression?01:36

What is Gene Expression?

A gene is a stretch of DNA that serves as the blueprint for functional RNAs and proteins. Since DNA is comprised  of nucleotides and proteins are comprised of amino acids, a mediator is required to convert the information encoded in DNA into proteins. This mediator is the messenger RNA (mRNA). mRNA copies the blueprint from DNA by a process called transcription. In eukaryotes, transcription occurs in the nucleus by complementary base-pairing with the DNA template. The mRNA is then processed and...

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An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations
10:17

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations

Published on: November 3, 2010

Gene-expression measurement: variance-modeling considerations for robust data analysis.

Shankar Subramaniam1, Gene Hsiao

  • 1Department of Bioengineering, University of California at San Diego, La Jolla, California, USA. shankar@ucsd.edu

Nature Immunology
|February 21, 2012
PubMed
Summary
This summary is machine-generated.

Modern biology tools like DNA microarrays and RNA sequencing offer deep insights but often use flawed analyses. Ignoring statistical variance methods leads to inaccurate biological interpretations and pathway understanding.

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Last Updated: May 24, 2026

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations
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Published on: November 3, 2010

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
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Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • DNA microarray and RNA sequencing are standard tools for measuring gene expression.
  • These technologies have advanced our understanding of biological mechanisms and pathways.
  • Current analytical methods often neglect robust statistical approaches for variance analysis.

Purpose of the Study:

  • To highlight the limitations of current gene expression data analysis.
  • To emphasize the need for statistically sound methods accounting for variance.
  • To improve the accuracy of biological interpretations from high-throughput expression data.

Main Methods:

  • Review of common gene expression analysis techniques.
  • Identification of statistical shortcomings in variance handling.
  • Comparison of standard vs. robust statistical methods.

Main Results:

  • Analysis of gene expression data frequently overlooks critical statistical methods.
  • Failure to account for variance can lead to erroneous conclusions about biological pathways.
  • Misleading interpretations arise from non-robust analytical strategies.

Conclusions:

  • Statistically robust methods are essential for accurate gene expression analysis.
  • Proper variance analysis is crucial for reliable biological interpretation.
  • Adopting rigorous statistical approaches will enhance the validity of findings in modern biology.