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Related Concept Videos

Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor 't,' or...
Uncertainty: Overview00:59

Uncertainty: Overview

In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this particular...
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
Cluster Sampling Method01:20

Cluster Sampling Method

Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...

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Updated: Jun 19, 2026

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

Modeling and visualizing uncertainty in gene expression clusters using dirichlet process mixtures.

Carl Edward Rasmussen1, Bernard J de la Cruz, Zoubin Ghahramani

  • 1Department of Engineering, University of Cambridge, Trumpington Street, Cambridge, CB2 1PZ, UK. cer54@cam.ac.uk

IEEE/ACM Transactions on Computational Biology and Bioinformatics
|October 31, 2009
PubMed
Summary
This summary is machine-generated.

Dirichlet process mixture (DPM) models offer a novel way to analyze gene expression data uncertainty. This study applies DPM to high-dimensional, non-time series data, yielding biologically relevant clustering results.

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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Area of Science:

  • Computational biology
  • Bioinformatics
  • Statistical genetics

Background:

  • Clustering methods are standard for microarray gene expression data analysis.
  • Uncertainty in clustering results is often overlooked.
  • Dirichlet process mixture (DPM) models offer a Bayesian approach to quantify this uncertainty.

Purpose of the Study:

  • To apply nonparametric Bayesian clustering methods to high-dimensional, non-time series gene expression data.
  • To utilize DPM models for modeling uncertainty in gene expression clustering.
  • To demonstrate the utility of DPM in analyzing complex gene expression datasets.

Main Methods:

  • Application of nonparametric Bayesian clustering using Dirichlet process mixture (DPM) models.
  • Utilizing full Gaussian covariances for high-dimensional data.
  • Employing cluster membership probability as a gene similarity measure.
  • Integrating DPM-derived dissimilarity with hierarchical clustering for visualization.

Main Results:

  • Successful application of DPM to high-dimensional, non-time series gene expression data.
  • Generation of a gene similarity measure based on cluster membership probability.
  • Visualization of gene expression profiles using hierarchical clustering with DPM-derived dissimilarities.
  • Obtained biologically plausible clustering results from the Rosetta compendium.

Conclusions:

  • Nonparametric Bayesian clustering with DPM models is effective for high-dimensional, non-time series gene expression data.
  • DPM provides a robust framework for assessing uncertainty in gene expression clustering.
  • The approach extends previous analyses and yields biologically meaningful insights.