Understanding q-value and FDR in Differential Expression Analysis

Daqian

Introduction to q-value and FDR

In differential gene expression analysis, researchers are often confronted with the challenge of distinguishing true signals — those genes that are genuinely differentially expressed, from the noise — genes that appear to be differentially expressed due to random chance. The p-value is a commonly used statistic to address this issue, but when dealing with thousands of genes, the false discovery rate (FDR) becomes critically important. The q-value is an adjusted p-value that controls for the FDR, and it’s particularly useful in large-scale testing scenarios, such as genomic studies.

The hedenfalk dataset

The hedenfalk dataset includes results from an analysis of gene expression related to BRCA1 and BRCA2 mutations in breast cancer patients. It contains p-values, test statistics, and empirical null statistics for 3170 genes.

Let’s load the dataset and inspect its structure:

library(qvalue)

## Warning: package 'qvalue' was built under R version 4.3.1

data(hedenfalk)
class(hedenfalk)

## [1] "list"

names(hedenfalk)

## [1] "p"     "stat"  "stat0"

We can extract the p-values and observe the statistics as follows:

pvalues = hedenfalk$p 
obs_stats = hedenfalk$stat
null_stats = hedenfalk$stat0

length(obs_stats)

## [1] 3170

length(null_stats)

## [1] 317000

Calculating q-values

Using the qvalue package, we can calculate q-values for each p-value:

qobj = qvalue(p = pvalues)

class(qobj)

## [1] "qvalue"

names(qobj)

## [1] "call"       "pi0"        "qvalues"    "pvalues"    "lfdr"      
## [6] "pi0.lambda" "lambda"     "pi0.smooth"

qvalues = qobj$qvalues
pi0 = qobj$pi0
lfdr = qobj$lfdr

A quick summary and visualization can be provided by:

summary(qobj)

## 
## Call:
## qvalue(p = pvalues)
## 
## pi0: 0.669926    
## 
## Cumulative number of significant calls:
## 
##           <1e-04 <0.001 <0.01 <0.025 <0.05 <0.1   <1
## p-value       15     76   265    424   605  868 3170
## q-value        0      0     1     73   162  319 3170
## local FDR      0      0     3     30    85  167 2241

hist(qobj)

plot(qobj)

Interpreting q-values and FDR

When we execute the command max(qvalues[qobj$pvalues <= 0.01]), we find the highest q-value among all p-values less than or equal to 0.01. Suppose this q-value is 0.07932; it implies that we estimate about 7.932% of the genes identified as differentially expressed (p-value <= 0.01) are false positives. In other words, we have an FDR of 0.07932.

Conversely, running max(pvalues[qobj$qvalues <= 0.01]) gives us the highest p-value that corresponds to a controlled q-value of less than or equal to 0.01. Let’s say this p-value is 3.15e-06. This means, for all genes with a p-value <= 3.15e-06, their FDR does not exceed 1%.

Using a specific FDR level

When we run the qvalue function with an fdr.level = 0.01, we get:

qobj_fdrlevel = qvalue(p = hedenfalk$p, fdr.level = 0.01)
head(qobj_fdrlevel$significant); length(qobj_fdrlevel$significant)

## [1] FALSE FALSE FALSE FALSE FALSE FALSE

## [1] 3170

This command also yields a logical vector significant, which indicates which genes meet the FDR threshold of 0.01. To get the indices of these genes, we can use:

significant_genes = which(qobj_fdrlevel$significant)
head(significant_genes)

## [1] 1413

Conclusion

The q-value is a powerful tool for researchers performing large-scale hypothesis testing, as it provides a means to control the false discovery rate. By utilizing the qvalue package in R, we can confidently identify genes that are truly differentially expressed, while minimizing the rate of false positives. This is crucial in fields like genomics, where the sheer number of simultaneous tests can lead to a high number of false discoveries if not properly controlled.