Fdr Value
Fdr Value - Related Videos
05:35
An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome
0 Views •
2022
We constructed an untargeted metabolomic workflow that integrated XY-Meta and metaX together. In this protocol, we displayed how to use XY-Meta to generate a decoy spectral library from open access spectra reference, and then performed FDR control and used the metaX to quantitate the metabolites after identifying the metabolomics spectra.
14:23
Bottom-up and Shotgun Proteomics to Identify a Comprehensive Cochlear Proteome
0 Views •
Cited by 6 •
2014
Proteome analysis of the cochlear sensory epithelium can be challenging due to its small size and because membrane proteins are difficult to isolate and identify. Both membrane and soluble proteins can be identified by combining multiple preparative methods and separation techniques along with high-resolution mass spectrometry.
09:35
A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research
0 Views •
Cited by 6 •
2017
We provide a standardized protocol for the use of gene set enrichment analysis of transcriptomic data to identify an ideal mouse model for translational research. This protocol can be used with DNA microarray and RNA sequencing data and can further be extended to other omics data if data are available.
07:20
Observing the Transformation of Bodily Self-consciousness in the Squeeze-machine Experiment
0 Views •
Cited by 6 •
2019
The squeeze machine, a V-shaped device that provides pressure along the whole body is used in therapy especially for people with autism. Here, we focus on its possible use in experiments to capture the typical transformations of bodily self-consciousness with peri-personal space measurement.
09:40
Identification of a Murine Erythroblast Subpopulation Enriched in Enucleating Events by Multi-spectral Imaging Flow Cytometry
0 Views •
Cited by 6 •
2014
The present protocol describes a novel method of identifying a population of enucleating orthochromatic erythroblasts by multi-spectral imaging flow cytometry, providing a visualization of the erythroblast enucleation process.
FAQs
Testing thousands of hypotheses increases the chance that some apparently significant findings arise randomly. FDR analysis addresses this problem by considering the collection of results and the number of hypotheses examined. This matters in biology because genomic, transcriptomic, and proteomic datasets can contain chance findings, helping researchers distinguish a broader signal from an isolated result.
The Benjamini-Hochberg procedure first ranks observed p-values from smallest to largest, then adjusts them in relation to the total number of tests. This ranking provides a consistent way to evaluate evidence across a large hypothesis set rather than treating comparisons independently. Researchers can then compare adjusted results with a selected FDR threshold when deciding which findings merit interpretation.
A lower FDR value indicates that a smaller proportion of findings designated significant is expected to be false positives. In a differential gene-expression analysis, this strengthens confidence that the reported association reflects biological signal rather than chance. The value does not by itself prove every finding is correct, but it provides a multiple-testing-aware basis for judging the overall result set.
A p-value describes evidence for an individual statistical comparison, whereas an FDR value reflects interpretation after many comparisons are considered together. Consequently, a result that appears notable from its unadjusted p-value may be less persuasive once the full hypothesis set is taken into account. This distinction is essential when high-throughput experiments generate thousands of candidate associations simultaneously.
To apply FDR control, researchers begin with the p-values from all planned or performed hypothesis tests, organize them for ranking, and use a procedure such as Benjamini-Hochberg to adjust them according to the number of tests. They then select an FDR threshold and evaluate the adjusted results against it. This workflow supports consistent filtering of large biological result tables.
In transcriptomic and genomic studies, FDR values help prioritize genes or associations for biological interpretation after many comparisons. The same logic extends to proteomic experiments and other high-throughput analyses. By identifying results that remain supported after adjustment, researchers can focus follow-up analysis on patterns less likely to be collections of chance findings, while retaining awareness that statistical evidence alone does not establish a biological mechanism.
The selected FDR threshold determines how stringently researchers classify adjusted findings. A stricter threshold yields a more conservative set of results, while a less stringent threshold permits more findings to be considered significant but offers weaker control of expected false discoveries. Reporting the threshold alongside adjusted results keeps biological interpretation tied to the chosen level.