RepeatedHighDim (version 2.4.0)

fc_plot: Volcano plot of adjusted confidence intervals

Description

Volcano plot of adjusted confidence intervals

Usage

fc_plot(
  CI,
  alpha = 0.05,
  updown = "all",
  xlim = c(-3, 3),
  ylim = -log10(c(1, 0.001))
)

Arguments

CI

Object as returned from the function fc_ci

alpha

1 - confidence level (e.g., if confidence level is 0.95, alpha is 0.05)

updown

Character, 'all' if CIs for all genes, 'down' if CIs for down-regulated genes, or 'up' if CIs for up-regulated genes to be plotted

xlim

Vector of length 2 with the lower and upper limits for the X-axis

ylim

Vector of length 2 with the lower and upper limits for the Y-axis. Please note, that p-values are usually displayed on the -log10-scale in a volcano plot

Author

Klaus Jung

Details

Volcano plot of adjusted confidence intervals

References

Dudoit, S., Shaffer, J. P., & Boldrick, J. C. (2003). Multiple hypothesis testing in microarray experiments. Statistical Science, 18(1), 71-103. https://projecteuclid.org/journals/statistical-science/volume-18/issue-1/Multiple-Hypothesis-Testing-in-Microarray-Experiments/10.1214/ss/1056397487.full

Jung, K., Friede, T., & Beißbarth, T. (2011). Reporting FDR analogous confidence intervals for the log fold change of differentially expressed genes. BMC bioinformatics, 12, 1-9. https://link.springer.com/article/10.1186/1471-2105-12-288

See Also

For more information, please refer to the package's documentation and the tutorial: https://software.klausjung-lab.de/.

Examples

### Artificial microarray data
d = 1000 ### Number of genes
n = 10 ### Sample per group
fc = rlnorm(d, 0, 0.1)
mu1 = rlnorm(d, 0, 1) ### Mean vector group 1
mu2 = mu1 * fc ### Mean vector group 2
sd1 = rnorm(d, 1, 0.2)
sd2 = rnorm(d, 1, 0.2)
X1 = matrix(NA, d, n) ### Expression levels group 1
X2 = matrix(NA, d, n) ### Expression levels group 2
for (i in 1:n) {
  X1[,i] = rnorm(d, mu1, sd=sd1)
  X2[,i] = rnorm(d, mu2, sd=sd2)
}
X = cbind(X1, X2)
heatmap(X)

### Differential expression analysis with limma
if(check_limma()){
group = gl(2, n)
design = model.matrix(~ group)
fit1 = limma::lmFit(X, design)
fit = limma::eBayes(fit1)

### Calculation of confidence intervals
CI = fc_ci(fit=fit, alpha=0.05, method="raw")
head(CI)
CI = fc_ci(fit=fit, alpha=0.05, method="BH")
head(CI)
CI = fc_ci(fit=fit, alpha=0.05, method="BY")
head(CI)

fc_plot(CI, xlim=c(-0.5, 3), ylim=-log10(c(1, 0.0001)), updown="up")
fc_plot(CI, xlim=c(-3, 0.5), ylim=-log10(c(1, 0.0001)), updown="down")
fc_plot(CI, xlim=c(-3, 3), ylim=-log10(c(1, 0.0001)), updown="all")
}