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Perform gene set analysis on the result of differential expression using linear (mixed) modeling with variancePartition::dream by considering the correlation between gene expression traits. This package is a slight modification of limma::camera to 1) be compatible with dream, and 2) allow identification of gene sets with log fold changes with mixed sign.


  use.ranks = FALSE,
  allow.neg.cor = FALSE,
  progressbar = TRUE,
  inter.gene.cor = 0.01



result of differential expression with dream


coefficient to test using topTable(fit, coef)


an index vector or a list of index vectors. Can be any vector such that fit[index,] selects the rows corresponding to the test set. The list can be made using ids2indices.


do a rank-based test (TRUE) or a parametric test ('FALSE')?


should reduced variance inflation factors be allowed for negative correlations?


if TRUE, show progress bar


if NA, estimate correlation from data. Otherwise, use specified value


  • NGenes: number of genes in this set

  • Correlation: mean correlation between expression of genes in this set

  • delta: difference in mean t-statistic for genes in this set compared to genes not in this set

  • se: standard error of delta

  • p.less: p-value for hypothesis test of H0: delta < 0

  • p.greater: p-value for hypothesis test of H0: delta > 0

  • PValue: p-value for hypothesis test H0: delta != 0

  • Direction: direction of effect based on sign(delta)

  • FDR: false discovery rate based on Benjamini-Hochberg method in p.adjust


zenith gives the same results as camera(..., inter.gene.cor=NA) which estimates the correlation with each gene set.

For differential expression with dream using linear (mixed) models see Hoffman and Roussos (2020). For the original camera gene set test see Wu and Smyth (2012).


Hoffman GE, Roussos P (2020). “dream: Powerful differential expression analysis for repeated measures designs.” Bioinformatics. doi:10.1093/bioinformatics/btaa687 . Wu D, Smyth GK (2012). “Camera: a competitive gene set test accounting for inter-gene correlation.” Nucleic acids research, 40(17), e133. doi:10.1093/nar/gks461 .



# simulate meta-data
info <- data.frame(Age=c(20, 31, 52, 35, 43, 45),Group=c(0,0,0,1,1,1))

# simulate expression data
y <- matrix(rnorm(1000*6),1000,6)
rownames(y) = paste0("gene", 1:1000)
colnames(y) = rownames(info)

# First set of 20 genes are genuinely differentially expressed
index1 <- 1:20
y[index1,4:6] <- y[index1,4:6]+1

# Second set of 20 genes are not DE
index2 <- 21:40

# perform differential expression analysis with dream
fit = dream(y, ~ Age + Group, info)
fit = eBayes(fit)

# perform gene set analysis testing Age
res = zenith(fit, "Age", list(set1=index1,set2=index2) )

#>      NGenes Correlation      delta        se    p.less p.greater    PValue
#> set1     20        0.01 -0.3663795 0.2351088 0.1085188 0.8914812 0.2170376
#> set2     20        0.01  0.2763873 0.2352550 0.8375895 0.1624105 0.3248210
#>      Direction      FDR
#> set1      Down 0.324821
#> set2        Up 0.324821