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

Real Time RT-PCR02:57

Real Time RT-PCR

Real-time reverse transcription-polymerase chain reaction, or Real-time RT-PCR, is an analytical tool used to determine the expression level of target genes. The method involves converting mRNA to complementary DNA with the help of an enzyme known as reverse transcriptase, followed by the PCR amplification of the cDNA. These two processes can be performed simultaneously in a single tube or separately as a two-step reaction.
The real-time quantification of the number of amplified products is...

You might also read

Related Articles

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

Sort by
Same author

Spatially Correlated Analysis of Infectious Disease Outcomes Based on Bayesian Functional Hierarchical Models.

Statistics in medicine·2026
Same author

Loss of ELF2 drives topotecan resistance in retinoblastoma revealed by genome-wide CRISPR-Cas9 screening.

Cell death & disease·2025
Same author

Predictive Value of Preoperative Simulated Night Vision Disturbance Psychological Testing and the Impact of Pupil Size on Visual Quality After KLEx.

Journal of refractive surgery (Thorofare, N.J. : 1995)·2025
Same author

Comparison of optical zone decentration and visual outcomes after KLEx and FS-LASIK in patients with asymmetrical high astigmatism.

Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie·2025
Same author

Downregulation of the core circadian gene Nr1d2 in the lacrimal gland contributes to postoperative dry eye disease by impairing lipid metabolism.

The ocular surface·2025
Same author

Prospective, Randomized, Contralateral Eye Comparison of the Lenticule Decentration Following SMILE: Coaxially Sighted Corneal Light Reflex Versus Tear Film Mark Centration.

Journal of refractive surgery (Thorofare, N.J. : 1995)·2025
Same journal

Balanced mediated pathway detection in genomic data.

Statistical applications in genetics and molecular biology·2026
Same journal

Annealed variational mixtures for disease subtyping and biomarker discovery.

Statistical applications in genetics and molecular biology·2026
Same journal

Performance of the permutation test approach with base calling errors for detecting changes in variant allele frequencies in ctDNA for a single patient.

Statistical applications in genetics and molecular biology·2026
Same journal

BLOG: Bayesian longitudinal omics with group constraints.

Statistical applications in genetics and molecular biology·2026
Same journal

AI-driven risk prediction and categorization in cystic fibrosis leveraging AttentiveLSTM and Fox Wolf Optimizer.

Statistical applications in genetics and molecular biology·2026
Same journal

Perfect collinearity not created equal: measuring and visualizing the severity of multi-collinearity of modern omics data.

Statistical applications in genetics and molecular biology·2026
See all related articles

Related Experiment Video

Updated: Jun 19, 2026

Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards
10:50

Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards

Published on: February 25, 2017

M-quantile regression analysis of temporal gene expression data.

Veronica Vinciotti1, Keming Yu

  • 1Brunel University. veronica.vinciotti@brunel.ac.uk

Statistical Applications in Genetics and Molecular Biology
|October 6, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a robust M-quantile regression method for analyzing temporal gene expression data. The approach effectively identifies significant differences in gene expression across biological conditions, outperforming standard methods.

More Related Videos

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans
09:23

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans

Published on: August 16, 2017

Optimized Quantitative Assessment of Enhancer RNA Stability in Mouse Embryonic Stem Cells
03:34

Optimized Quantitative Assessment of Enhancer RNA Stability in Mouse Embryonic Stem Cells

Published on: November 21, 2025

Related Experiment Videos

Last Updated: Jun 19, 2026

Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards
10:50

Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards

Published on: February 25, 2017

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans
09:23

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans

Published on: August 16, 2017

Optimized Quantitative Assessment of Enhancer RNA Stability in Mouse Embryonic Stem Cells
03:34

Optimized Quantitative Assessment of Enhancer RNA Stability in Mouse Embryonic Stem Cells

Published on: November 21, 2025

Area of Science:

  • Bioinformatics
  • Statistical Genetics
  • Computational Biology

Background:

  • Analyzing temporal gene expression data is crucial for understanding biological processes.
  • Existing methods may lack robustness, especially with small sample sizes or noisy data.
  • Detecting subtle differences in gene expression profiles across conditions requires advanced statistical techniques.

Purpose of the Study:

  • To develop a robust statistical method for detecting significant differences in temporal gene expression profiles between biological conditions.
  • To leverage M-quantile regression for improved performance in small-sample, outlier-prone gene expression datasets.
  • To introduce a novel approach for identifying differentially expressed genes in time-course experiments.

Main Methods:

  • Parametric M-quantile regression models were used to capture temporal gene expression profiles.
  • M-quantile coefficients were computed to enhance robustness across various quantile levels.
  • A polynomial M-quantile regression model and Hotelling T(2)-test were applied to assess differences across conditions.

Main Results:

  • The proposed M-quantile regression method demonstrated increased power and robustness compared to standard regression techniques.
  • Simulations confirmed the superior performance of M-quantile methods over previously published approaches.
  • The method successfully identified differentially expressed genes in time-course microarray data for muscular dystrophy.

Conclusions:

  • M-quantile regression offers a powerful and robust tool for analyzing temporal gene expression data.
  • The developed method enhances the ability to detect statistically significant differences in gene expression across biological conditions.
  • This approach has practical applications in identifying key genes from time-course experiments, as demonstrated in muscular dystrophy research.