RepeatedHighDim (version 2.3.0)

fc_ci: Calculation of adjusted confidence intervals

Description

Calculation of adjusted confidence intervals

Usage

fc_ci(fit, alpha = 0.05, method = "raw")

Value

A results matrix with one row per gene, and one column for the p-value, the log fold change, the lower limit of the CI, and the upper limit of the CI

Arguments

fit

Object as returned from the function eBayes of the limma package

alpha

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

method

Either 'raw' for unadjusted confidence intervals, or 'BH' for Bejamini Hochberg-adjusted confidence intervals, or 'BY' for Benjamini Yekutieli-adjusted confidence intervals

Author

Klaus Jung

Details

Calculation of unadjusted and adjusted confidence intervals for the log fold change

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")
}