Test if coefficient is different from a specified value
Usage
# S4 method for dreamletResult
getTreat(fit, lfc = log2(1.2), coef = NULL, number = 10, sort.by = "p")Arguments
- fit
dreamletResult object
- lfc
a minimum log2-fold-change below which changes not considered scientifically meaningful
- coef
which coefficient to test
- number
number of genes to return
- sort.by
column to sort by
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...
#> 0.23 secs
#> CD14+ Monocytes...
#> 0.36 secs
#> CD4 T cells...
#> 0.28 secs
#> CD8 T cells...
#> 0.15 secs
#> FCGR3A+ Monocytes...
#> 0.33 secs
# Differential expression analysis within each assay,
# evaluated on the voom normalized data
res.dl <- dreamlet(res.proc, ~group_id)
#> B cells...
#> 0.18 secs
#> CD14+ Monocytes...
#> 0.24 secs
#> CD4 T cells...
#> 0.2 secs
#> CD8 T cells...
#> 0.14 secs
#> FCGR3A+ Monocytes...
#> 0.23 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
getTreat(res.dl, coef = "group_idstim", number = 3)
#> DataFrame with 3 rows and 7 columns
#> assay logFC AveExpr t P.Value adj.P.Val
#> <character> <numeric> <numeric> <numeric> <numeric> <numeric>
#> 1 B cells 6.17666 10.2306 18.2945 1.75997e-14 7.90401e-11
#> 2 FCGR3A+ Monocytes 5.27610 11.9149 26.0100 9.11216e-14 2.04613e-10
#> 3 FCGR3A+ Monocytes 7.43146 11.1948 22.7202 6.79966e-13 9.94753e-10
#> B
#> <numeric>
#> 1 23.2102
#> 2 22.0773
#> 3 19.7485