Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Real Time RT-PCR

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...

Detection of Gross Error: The Q Test

Detection of Gross Error: The Q Test

When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...

You might also read

Related Articles

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

Sort by

Same author

Bepirovirsen induces innate immune activation in the liver potentially through TLR8 signaling.

JHEP reports : innovation in hepatology·2026

Same author

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

Biostatistics (Oxford, England)·2026

Same author

PARiS: Probabilistic Assignment and Repartitioning of isomiR Sequences: A data-driven method for denoising isomiR read count data.

bioRxiv : the preprint server for biology·2026

Same author

Comparison of Resampling Algorithms to Address Class Imbalance when Developing Machine Learning Models to Predict Foodborne Pathogen Presence in Agricultural Water.

Frontiers in environmental science·2026

Same author

Response letter to Oka et al.'s letter to the editor.

The journal of pain·2026

Same author

A cautionary tale: Non-steroidal anti-inflammatory drug use and localized provoked vulvodynia.

The journal of pain·2025

Same journal

SNPio: a Python interface for population genomic data processing.

BMC bioinformatics·2026

Same journal

SpaHNR: a spatial domain identification method via sparse attention-based hierarchical node representation and multi-view contrastive learning.

BMC bioinformatics·2026

Same journal

OpenIMC: an open-source platform for analyzing single-cell and spatial proteomics by imaging mass cytometry.

BMC bioinformatics·2026

Same journal

NAP: an open source pipeline for cross-domain microbiome profiling using Nanopore sequencing-derived amplicon data.

BMC bioinformatics·2026

Same journal

SurvGME: an R package for survival analysis with graphical and measurement error models.

BMC bioinformatics·2026

Same journal

SimMapNet: a Bayesian framework for gene regulatory network inference using gene ontology similarities as external hint.

BMC bioinformatics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Nov 28, 2025

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

Multiple imputation and direct estimation for qPCR data with non-detects.

Valeriia Sherina¹, Helene R McMurray^2,3, Winslow Powers⁴

¹Department of Biostatistics and Computational Biology, University of Rochester Medical Center, 265 Crittenden Blvd., 14642, Rochester, NY, USA.

BMC Bioinformatics

|November 27, 2020

Summary

This summary is machine-generated.

This study introduces novel statistical methods to accurately analyze quantitative real-time PCR (qPCR) data with non-detects. Our approach improves gene expression measurement accuracy and enhances confidence in downstream analyses.

Keywords:

Direct estimation Gene expression Missing not at random (MNAR)Multiple imputation Non-detects Quantitative real-time PCR (qPCR)

More Related Videos

qKAT: Quantitative Semi-automated Typing of Killer-cell Immunoglobulin-like Receptor Genes

qKAT: Quantitative Semi-automated Typing of Killer-cell Immunoglobulin-like Receptor Genes

Published on: March 6, 2019

A Duplex Digital PCR Assay for Simultaneous Quantification of the Enterococcus spp. and the Human Fecal-associated HF183 Marker in Waters

A Duplex Digital PCR Assay for Simultaneous Quantification of the Enterococcus spp. and the Human Fecal-associated HF183 Marker in Waters

Published on: March 9, 2016

Related Experiment Videos

Last Updated: Nov 28, 2025

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

qKAT: Quantitative Semi-automated Typing of Killer-cell Immunoglobulin-like Receptor Genes

qKAT: Quantitative Semi-automated Typing of Killer-cell Immunoglobulin-like Receptor Genes

Published on: March 6, 2019

A Duplex Digital PCR Assay for Simultaneous Quantification of the Enterococcus spp. and the Human Fecal-associated HF183 Marker in Waters

A Duplex Digital PCR Assay for Simultaneous Quantification of the Enterococcus spp. and the Human Fecal-associated HF183 Marker in Waters

Published on: March 9, 2016

Area of Science:

Molecular Biology
Bioinformatics
Statistical Genetics

Background:

Quantitative real-time PCR (qPCR) is a standard technique for gene expression analysis.
Non-detects, or reactions failing to reach the quantification threshold, are often ignored or improperly handled.
Current methods for handling non-detects introduce bias and underestimate variance, affecting inference.

Purpose of the Study:

To develop and validate statistical methods for accurately analyzing qPCR data containing non-detects.
To address the bias and underestimation of variance caused by current non-detect handling.
To provide a robust framework for gene expression analysis in the presence of missing data.

Main Methods:

Treating non-detects as non-random missing data and modeling the missing data mechanism.
Employing a multiple imputation procedure to account for imputation uncertainty.
Validating methods through simulation studies and application to experimental datasets.

Main Results:

The proposed methods effectively reduce discrepancies in gene expression values compared to mean imputation, single imputation, and PEMM.
Multiple imputation provides a more accurate estimation of variance and reduces anti-conservative inference.
The R/Bioconductor package 'nondetects' implements these advanced statistical techniques.

Conclusions:

The developed statistical methods offer a significant improvement for qPCR data analysis with non-detects.
Accurate handling of non-detects leads to increased confidence in downstream gene expression analyses.
These methods provide a more reliable foundation for biological interpretation of qPCR results.