Related Experiment Video
Updated: Jul 12, 2026
08:12
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
Published on: March 1, 2022
Comments on probabilistic models behind the concept of false discovery rate
1Department of Biostatistics and Computational Biology, University of Rochester, 601 Elmwood Avenue, Box 630, Rochester, New York 14642, USA. xqiu@bst.rochester.edu
Journal of Bioinformatics and Computational Biology
|September 6, 2007
Summary
This study examines the formula for false discovery rate (FDR) estimation, highlighting its limitations under special conditions. It explores probabilistic models and the impact of inter-gene correlations in microarray data analysis.
Area of Science:
- Statistical genetics
- Bioinformatics
- Computational biology
Background:
- The false discovery rate (FDR) is a key metric in multiple hypothesis testing.
- Existing FDR estimation formulas have limitations under specific conditions.
- Microarray data analysis presents unique challenges for FDR control.
Purpose of the Study:
- To critically evaluate the commonly used formula for false discovery rate estimation.
- To discuss the underlying probabilistic models of the FDR concept.
- To investigate the impact of inter-gene correlations on FDR in microarray data.
Main Methods:
- Theoretical analysis of FDR estimation formulas.
- Exploration of probabilistic models relevant to FDR.
- Simulation studies to assess the effect of inter-gene correlations.
Main Results:
- The standard FDR formula is shown to be valid only under restrictive assumptions.
- Inter-gene correlations can significantly affect theoretical FDR results.
- The study highlights potential issues in FDR control for complex biological data.
Conclusions:
- Re-evaluation of the theoretical basis for FDR estimation is necessary.
- Accounting for inter-gene correlations is crucial for accurate FDR control in microarray studies.
- This work underscores the need for robust statistical methods in high-throughput data analysis.
Related Concept Videos
01:14Errors In Hypothesis Tests
When performing a hypothesis test, there are four possible outcomes depending on the actual truth (or falseness) of the null hypothesis and the decision to reject or not.
01:13Accuracy 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...
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...
01:18False Memories
False memories represent a cognitive distortion in which individuals recall events that did not happen, or remember them in an altered form. This phenomenon highlights the brain's constructive nature in processing and recalling memories, emphasizing that memory is not a perfect representation of past events but rather a dynamic reconstruction influenced by various factors.
One primary source of false memories is misattribution, where individuals incorrectly associate external information with...
One primary source of false memories is misattribution, where individuals incorrectly associate external information with...
01:24Testing 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...
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...
01:13Sensitivity, Specificity, and Predicted Value
In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
Sensitivity is the...
01:09Decision Making: P-value Method
The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim is also stated. These statements can act as null and alternative hypotheses: a null hypothesis would be a neutral statement while the alternative hypothesis can have a...
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim is also stated. These statements can act as null and alternative hypotheses: a null hypothesis would be a neutral statement while the alternative hypothesis can have a...