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Related Concept Videos

Multiple Comparison Tests01:13

Multiple Comparison Tests

Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and Cox...
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...
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures from...
Classification of Signals01:30

Classification of Signals

In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
Aggregates Classification01:29

Aggregates Classification

Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...

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Related Experiment Video

Updated: Jul 1, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

A class comparison method with filtering-enhanced variable selection for high-dimensional data sets.

Lara Lusa1, Edward L Korn, Lisa M McShane

  • 1Department of Medical Informatics, University of Ljubljana, Slovenia. Lara.Lusa@mf.uni-lj.si

Statistics in Medicine
|September 11, 2008
PubMed
Summary
This summary is machine-generated.

We introduce a new method, filtering-enhanced variable selection (FEVS), to improve the identification of differentially expressed variables in high-throughput molecular data. FEVS enhances detection sensitivity while controlling false discoveries in complex datasets.

Related Experiment Videos

Last Updated: Jul 1, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Area of Science:

  • Bioinformatics
  • Statistical Genetics
  • Computational Biology

Background:

  • High-throughput molecular analyses generate vast datasets, necessitating robust statistical methods for identifying differentially expressed variables between sample classes.
  • Traditional multiple testing strategies face challenges with high-dimensional data, risking false positives and often employing arbitrary pre-analysis filtering.
  • Existing filtering methods can be subjective, impacting the reliability and reproducibility of differential expression analysis.

Purpose of the Study:

  • To develop and validate a novel multiple testing strategy, filtering-enhanced variable selection (FEVS), for robust identification of differentially expressed variables.
  • To address the limitations of arbitrary pre-analysis filtering in high-dimensional data analysis.
  • To enhance sensitivity in detecting true biological signals while maintaining statistical rigor.

Main Methods:

  • The filtering-enhanced variable selection (FEVS) method integrates results from multiple filtering strategies, avoiding reliance on a single, potentially suboptimal, filtering approach.
  • FEVS combines differential expression analysis across various filtering thresholds to identify consistently significant variables.
  • The method's probabilistic control of false discoveries was mathematically proven.

Main Results:

  • FEVS probabilistically controls the number of false discoveries, ensuring reliable identification of differentially expressed variables.
  • Simulations and a literature-based example demonstrated FEVS's superior sensitivity in detecting truly differentially expressed variables compared to single-filtering methods.
  • The approach mitigates the arbitrary nature of pre-analysis filtering, leading to more consistent results.

Conclusions:

  • The filtering-enhanced variable selection (FEVS) method offers a statistically sound and more sensitive approach for identifying differentially expressed variables in high-throughput molecular data.
  • FEVS provides a robust alternative to conventional filtering techniques, improving the power of differential expression analysis.
  • This method enhances the discovery of biologically relevant molecular signatures in complex datasets.