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Related Concept Videos

Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This number is...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
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Outliers and Influential Points01:08

Outliers and Influential Points

An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the vertical...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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Related Experiment Video

Updated: Jul 4, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Genomic outlier profile analysis: mixture models, null hypotheses, and nonparametric estimation.

Debashis Ghosh1, Arul M Chinnaiyan

  • 1Department of Statistics and Department of Public Health Sciences, Pennsylvania State University, University Park, PA 16802, USA. ghoshd@psu.edu

Biostatistics (Oxford, England)
|June 10, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces outlier profile analysis, a new method to detect gene expression patterns where a subset of samples shows overexpression. This approach enhances genomic data analysis beyond traditional mean-difference tests.

Related Experiment Videos

Last Updated: Jul 4, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Area of Science:

  • Genomics
  • Bioinformatics
  • Statistical Genetics

Background:

  • Traditional differential expression analysis focuses on mean differences between groups.
  • A novel pattern involves a fraction of samples exhibiting overexpression, not captured by mean-based methods.

Purpose of the Study:

  • To develop a statistical framework for assessing outlier expression patterns.
  • To introduce outlier profile analysis for genomic data.

Main Methods:

  • A general mixture model framework is proposed.
  • Nonparametric estimation procedures are developed, linked to multiple testing.
  • Multivariate extensions are created for genome-wide analyses.

Main Results:

  • Identifiability results are established for the single-gene case.
  • The methodology is validated through simulation studies.
  • Application to a prostate cancer gene expression dataset is demonstrated.

Conclusions:

  • Outlier profile analysis provides a robust method for detecting specific differential expression patterns.
  • The framework is applicable to both single-gene and genome-wide studies.
  • This method complements existing techniques in large-scale genomic data analysis.