Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

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...
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.
On...
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...
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...
Extraction: Partition and Distribution Coefficients01:14

Extraction: Partition and Distribution Coefficients

The distribution law or Nernst's distribution law is the law that governs the distribution of a solute between two immiscible solvents. This law, also known as the partition law, states that if a solute is added to the mixture of two immiscible solvents at a constant temperature, the solute is distributed between the two solvents in such a way that the ratio of solute concentrations in the solvents remains constant at equilibrium.
For extracting a solute from an aqueous phase into an organic...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

The roles of the acetyltransferase domains of the chromatin regulators KAT6A and KAT6B in vivo.

Development (Cambridge, England)·2026
Same author

To what extent is air pollution associated with cardiorespiratory hospitalisations across Australia? A whole-of-population ecological study.

Public health research & practice·2026
Same author

KAT6A is essential for developmental control gene expression in neural stem and progenitor cells.

PLoS genetics·2026
Same author

Complete genetic and epigenetic architecture of D4Z4 macrosatellites in FSHD, BAMS, and reference cohorts with D4Z4End2End.

Genome research·2026
Same author

The E3-ome gene-centric compendium reveals the human E3 ligase landscape.

Cell·2026
Same author

Haemodynamic and blood viscosity profiles of culprit vs. non-culprit coronary vessels: Insights from NIRS-IVUS and CFD analysis.

Atherosclerosis·2026
Same journal

A Bayesian functional concurrent zero-inflated Dirichlet-multinomial regression model with application to infant microbiome.

Biostatistics (Oxford, England)·2026
Same journal

Towards optimal environmental policies: policy learning under arbitrary bipartite network interference.

Biostatistics (Oxford, England)·2026
Same journal

Multilevel functional quantile principal component analysis.

Biostatistics (Oxford, England)·2026
Same journal

Adaptive transfer learning for time-to-event modeling with applications in disease risk assessment.

Biostatistics (Oxford, England)·2026
Same journal

High-dimensional test for one-sided hypotheses.

Biostatistics (Oxford, England)·2026
Same journal

NBSR: a Negative Binomial Softmax Regression model for microRNA-seq data analysis.

Biostatistics (Oxford, England)·2026
See all related articles

Related Experiment Video

Updated: Jun 27, 2026

Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer
08:20

Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer

Published on: May 21, 2019

Microarray background correction: maximum likelihood estimation for the normal-exponential convolution.

Jeremy D Silver1, Matthew E Ritchie, Gordon K Smyth

  • 1Bioinformatics Division, Walter and Eliza Hall Institute, Parkville 3050, Victoria, Australia. j.silver@biostat.ku.dk

Biostatistics (Oxford, England)
|December 11, 2008
PubMed
Summary
This summary is machine-generated.

This study enhances the normexp method for microarray background correction. Improved parameter estimation and a reliable algorithm lead to more accurate data analysis and better differential expression assessment.

More Related Videos

Enrichment of Native Lipoprotein Particles with microRNA and Subsequent Determination of Their Absolute/Relative microRNA Content and Their Cellular Transfer Rate
11:13

Enrichment of Native Lipoprotein Particles with microRNA and Subsequent Determination of Their Absolute/Relative microRNA Content and Their Cellular Transfer Rate

Published on: May 9, 2019

Cerebrospinal Fluid MicroRNA Profiling Using Quantitative Real Time PCR
09:26

Cerebrospinal Fluid MicroRNA Profiling Using Quantitative Real Time PCR

Published on: January 22, 2014

Related Experiment Videos

Last Updated: Jun 27, 2026

Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer
08:20

Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer

Published on: May 21, 2019

Enrichment of Native Lipoprotein Particles with microRNA and Subsequent Determination of Their Absolute/Relative microRNA Content and Their Cellular Transfer Rate
11:13

Enrichment of Native Lipoprotein Particles with microRNA and Subsequent Determination of Their Absolute/Relative microRNA Content and Their Cellular Transfer Rate

Published on: May 9, 2019

Cerebrospinal Fluid MicroRNA Profiling Using Quantitative Real Time PCR
09:26

Cerebrospinal Fluid MicroRNA Profiling Using Quantitative Real Time PCR

Published on: January 22, 2014

Area of Science:

  • Bioinformatics
  • Genomics
  • Statistical Modeling

Background:

  • Background correction is crucial for accurate microarray data analysis.
  • The normexp method models intensities using normal and exponential distributions for noise and signal.
  • Previous work established normexp as effective for 2-color microarray data.

Purpose of the Study:

  • To refine the normexp method for improved parameter estimation.
  • To develop a robust algorithm for maximum likelihood estimation (MLE) in background correction.
  • To enhance the performance of normexp for differential expression analysis.

Main Methods:

  • Mathematical development of the normexp model and saddle-point approximation.
  • Addressing numerical issues in the original normexp implementation.
  • Implementing an exact maximum likelihood estimation (MLE) algorithm using saddle-point estimates as starting values.

Main Results:

  • The enhanced normexp method demonstrates superior estimation accuracy compared to heuristic methods.
  • The new MLE algorithm provides reliable and accurate background correction for diverse datasets.
  • Adding a small offset to corrected intensities improves differential expression assessment.

Conclusions:

  • The refined normexp method offers a more accurate and reliable approach to microarray background correction.
  • The developed MLE algorithm overcomes limitations of previous implementations.
  • This work advances the application of normexp for robust genomic data analysis.