false discovery rate , FDR (skrót)

Following their approach, the 18% false discovery rate is lowered to about 13% by assuming that not all hypotheses are null.

And the false discovery rate is given by:

Used to estimate the false discovery rates.

The use of a false discovery rate is a more sophisticated approach that has become a popular method for control of multiple hypothesis tests.

The Benjamini-Hochberg-Yekutieli procedure controls the false discovery rate under positive dependence assumptions.

In addition, SAM readily quantitates the trade-offs between false discovery rates and numbers of selected genes.

Selective inference is usually performed by controlling the FDR (false discovery rate criteria).

This however has not performed any statistical significance calculations so hypothesis testing was performed and the false discovery rate was controlled at 1%.

False discovery rate (FDR) control is a statistical method used in multiple hypothesis testing to correct for multiple comparisons.

The FCR has a strong connection to the false discovery rate (FDR).

False discovery rate (FDR)

The Benjamini-Hochberg procedure (BH step-up procedure) controls the false discovery rate (at level ).

Data can be queried for specific proteins, browsed based on organism, tissue, localization and condition and sorted by false discovery rate and expression.

SAM calculates a test statistic for relative difference in gene expression based on permutation analysis of expression data and calculates a false discovery rate.

In 1995 Benjamini and Hochberg suggested to control the false discovery rate instead of the familywise error rate and do selective inference corrections.

False discovery rate ( FDR ) may be defined as the proportion of false positives among the significant genes, see [ 2 ] .

SAM terms the percentage of genes identified by chance the false discovery rate (FDR) and estimates this quantity by recourse to permutation.

Benjamini (2010) said that the false discovery rate, and the paper Benjamini and Hochberg (1995), had its origins in two papers concerned with multiple testing:

Its scoring algorithm is based on that of MyriMatch, but it includes a novel FDR (false discovery rate) validation algorithm as well.

The approximate false discovery rate, , reduced for positively correlated tests, may suffice to adjust alpha for useful comparison to an over-small p-value from Fisher's X.

Most notably is the false discovery rate which was invented by Benjamini and Hochberg in 1995, and address many of the large-scale inferences problems in a more practical way.

The control is in the sense that the specific procedures controls it, it might be controlling the familywise error rate, the false discovery rate, or some other error rate.

It thresholds the T-statistics to provide a 'significant' gene list and provides an estimate of the false discovery rate (the percent of genes identified by chance alone) from randomly permuted data.

Alternatively, if a study is viewed as exploratory, or if significant results can be easily re-tested in an independent study, control of the false discovery rate (FDR) is often preferred.

The false discovery rate (FDR) was computed as the ratio of the estimated number of "falsely significant" probe sets to the total number of "significant" probe sets.