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

Probability Laws

Probability Laws

Testing a Claim about Population Proportion

Testing a Claim about Population Proportion

A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...

Binomial Probability Distribution

Binomial Probability Distribution

A binomial distribution is a probability distribution for a procedure with a fixed number of trials, where each trial can have only two outcomes.
The outcomes of a binomial experiment fit a binomial probability distribution. A statistical experiment can be classified as a binomial experiment if the following conditions are met:
There are a fixed number of trials. Think of trials as repetitions of an experiment. The letter n denotes the number of trials.
There are only two possible outcomes,...

Expected Frequencies in Goodness-of-Fit Tests

Expected Frequencies in Goodness-of-Fit Tests

A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n) to the number of categories (k).

Prediction Intervals

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
The...

Bioequivalence Data: Statistical Interpretation

Bioequivalence Data: Statistical Interpretation

The statistical interpretation of bioequivalence data is a significant aspect of pharmaceutical research. Bioequivalence refers to the absence of any significant difference in the rate and extent to which the active ingredient in pharmaceutical products becomes available at the site of drug action when administered at the same molar dose under similar conditions. This helps determine if different drug products have similar absorption rates, ensuring their interchangeability.Statistical...

You might also read

Related Articles

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

Sort by

Same author

Clinical clusters during acute illness predict long-term mortality in older patients.

BMC medicine·2025

Same author

Cationic Polystyrene Latex Nanocarriers for Immunostimulatory Long Double-Stranded RNA Delivery to Ovarian Cancer Cells.

Journal of biomedical materials research. Part B, Applied biomaterials·2024

Same author

Accelerometer-based estimation of oxygen uptake in adults with Down syndrome: vector magnitude vs. vertical axis.

Journal of intellectual disability research : JIDR·2022

Same author

Statistical properties of sketching algorithms.

Biometrika·2022

Same author

The molecular landscape and associated clinical experience in infant medulloblastoma: prognostic significance of second-generation subtypes.

Neuropathology and applied neurobiology·2020

Same author

Fast nonadiabatic dynamics of many-body quantum systems.

Science advances·2019

Search research articles

Related Experiment Video

Updated: Jul 9, 2026

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

Published on: March 1, 2024

Tail posterior probability for inference in pairwise and multiclass gene expression data.

N Bochkina¹, S Richardson

¹Centre for Biostatistics, Imperial College, London W2 1PG, UK. n.bochkina@imperial.ac.uk

|December 15, 2007

Summary

This study introduces a new Bayesian method, tail posterior probability, for identifying differentially expressed genes in microarray data. It offers a robust approach for analyzing gene expression differences between conditions and in multiclass settings.

More Related Videos

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Related Experiment Videos

Last Updated: Jul 9, 2026

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

Published on: March 1, 2024

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Area of Science:

Bioinformatics
Statistical genetics
Computational biology

Background:

Microarray data analysis is crucial for understanding gene expression.
Identifying differentially expressed genes between conditions is a key challenge.
Bayesian frameworks offer a powerful approach for statistical inference in genomics.

Purpose of the Study:

To introduce a novel Bayesian rule, tail posterior probability, for identifying differentially expressed genes.
To develop a frequentist estimator for the false discovery rate associated with the new rule.
To extend the tail posterior probability rule for multiclass data analysis.

Main Methods:

Bayesian inference with a noninformative prior distribution.
Utilizing the posterior distribution of the standardized difference.
Deriving a frequentist estimator for the false discovery rate.
Extending the rule for compound null hypotheses in multiclass settings.

Main Results:

The tail posterior probability rule effectively identifies differentially expressed genes.
The derived frequentist false discovery rate estimator is associated with the new rule.
The method demonstrates comparable or superior performance to existing Bayesian rules.
The tail posterior probability rule is adaptable to complex multiclass comparisons.

Conclusions:

The tail posterior probability offers a statistically sound and effective method for differential gene expression analysis.
This approach provides a reliable way to control false discoveries in genomic studies.
The extension to multiclass data enhances its applicability in diverse biological research scenarios.