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Related Concept Videos

Test for Homogeneity01:23

Test for Homogeneity

The goodness–of–fit test can be used to decide whether a population fits a given distribution, but it will not suffice to decide whether two populations follow the same unknown distribution. A different test, called the test for homogeneity, can be used to conclude whether two populations have the same distribution. To calculate the test statistic for a test for homogeneity, follow the same procedure as with the test of independence. The hypotheses for the test for homogeneity can be stated as...
Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
Types of Hypothesis Testing01:11

Types of Hypothesis Testing

There are three types of hypothesis tests: right-tailed, left-tailed, and two-tailed.
When the null and alternative hypotheses are stated, it is observed that the null hypothesis is a neutral statement against which the alternative hypothesis is tested. The alternative hypothesis is a claim that instead has a certain direction. If the null hypothesis claims that p = 0.5, the alternative hypothesis would be an opposing statement to this and can be put either p > 0.5, p < 0.5, or p ≠ 0.5.
Statistical Hypothesis Testing01:16

Statistical Hypothesis Testing

Hypothesis testing is a critical statistical procedure facilitating informed, evidence-based decisions. It begins with a hypothesis, which is a tentative explanation, or a prediction about a population parameter. This hypothesis can be either a null hypothesis (H0), indicating no effect or difference, or an alternative hypothesis (Ha), suggesting an effect or difference.
Statistical significance measures the probability that an observed result occurred by chance. If this probability, known as...
Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5% chance...
Expected Frequencies in Goodness-of-Fit Tests01:19

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).

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Related Experiment Video

Updated: Jun 16, 2026

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
10:10

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Published on: September 18, 2021

Validation of differential gene expression algorithms: application comparing fold-change estimation to hypothesis

Corey M Yanofsky1, David R Bickel

  • 1Ottawa Institute of Systems Biology, Department of Biochemistry, Microbiology, and Immunology, University of Ottawa, Ottawa, Ontario, Canada.

BMC Bioinformatics
|January 30, 2010
PubMed
Summary
This summary is machine-generated.

Comparing gene expression algorithms is crucial for accurate results. Hierarchical models demonstrated superior performance in predicting expression ratios, outperforming other methods in this study.

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Area of Science:

  • Bioinformatics
  • Statistical Genetics
  • Computational Biology

Background:

  • Numerous statistical algorithms exist for identifying differentially expressed genes from microarray data, often yielding varied results.
  • A concordance method assesses gene expression detection but may be unstable due to fixed gene list sizes.

Purpose of the Study:

  • To compare statistical algorithms for detecting differential gene expression using novel predictive error estimation methods.
  • To evaluate the performance of algorithms based on hierarchical models versus fold-change or non-hierarchical criteria.

Main Methods:

  • Introduced cross-validation and posterior predictive methods for assessing predictive error in differential gene expression algorithms.
  • Applied these methods to analyze microarray data, considering both single-channel and dual-channel platforms.

Main Results:

  • Cross-validation and posterior predictive methods provide robust assessments of algorithm performance, especially with limited biological replicates.
  • Empirical Bayes methods utilizing hierarchical models consistently outperformed algorithms based on fold-change or non-hierarchical model selection criteria.

Conclusions:

  • Hierarchical model-based algorithms show superior performance in predicting gene expression ratios compared to other tested methods.
  • Further investigation is warranted into algorithms that integrate significance testing with fold-change estimation.