Extract a table of the top-ranked genes from a dreamlet fit.
Usage
# S4 method for class 'dreamletResult'
topTable(
fit,
coef = NULL,
number = 10,
genelist = NULL,
adjust.method = "BH",
sort.by = "P",
resort.by = NULL,
p.value = 1,
lfc = 0,
confint = FALSE
)See also
limma::topTable(), variancePartition::topTable()
Examples
library(muscat)
library(SingleCellExperiment)
data(example_sce)
# create pseudobulk for each sample and cell cluster
pb <- aggregateToPseudoBulk(example_sce,
assay = "counts",
cluster_id = "cluster_id",
sample_id = "sample_id",
verbose = FALSE
)
# voom-style normalization
res.proc <- processAssays(pb, ~group_id)
#> B cells...
#> 0.19 secs
#> CD14+ Monocytes...
#> 0.3 secs
#> CD4 T cells...
#> 0.22 secs
#> CD8 T cells...
#> 0.13 secs
#> FCGR3A+ Monocytes...
#> 0.27 secs
# Differential expression analysis within each assay,
# evaluated on the voom normalized data
res.dl <- dreamlet(res.proc, ~group_id)
#> B cells...
#> 0.2 secs
#> CD14+ Monocytes...
#> 0.26 secs
#> CD4 T cells...
#> 0.21 secs
#> CD8 T cells...
#> 0.12 secs
#> FCGR3A+ Monocytes...
#> 0.24 secs
# show coefficients estimated for each cell type
coefNames(res.dl)
#> [1] "(Intercept)" "group_idstim"
# extract results using limma-style syntax
# combines all cell types together
# adj.P.Val gives study-wide FDR
topTable(res.dl, coef = "group_idstim", number = 3)
#> DataFrame with 3 rows and 9 columns
#> assay ID logFC AveExpr t P.Value
#> <character> <character> <numeric> <numeric> <numeric> <numeric>
#> 1 B cells ISG15 6.17666 10.2306 19.1083 1.26348e-14
#> 2 FCGR3A+ Monocytes CXCL10 5.27610 11.9149 27.3747 7.09630e-14
#> 3 B cells ISG20 3.60083 11.5794 16.0129 3.92209e-13
#> adj.P.Val B z.std
#> <numeric> <numeric> <numeric>
#> 1 5.67431e-11 23.2102 19.1083
#> 2 1.59347e-10 22.0773 27.3747
#> 3 5.87136e-10 20.2656 16.0129