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Differential expression for each assay

Source: R/dreamlet.R
dreamlet.Rd

Perform differential expression for each assay using linear (mixed) models

Usage

dreamlet(
  x,
  formula,
  data = colData(x),
  assays = assayNames(x),
  contrasts = NULL,
  min.cells = 10,
  robust = FALSE,
  quiet = FALSE,
  BPPARAM = SerialParam(),
  use.eBayes = TRUE,
  ...
)

# S4 method for dreamletProcessedData
dreamlet(
  x,
  formula,
  data = colData(x),
  assays = assayNames(x),
  contrasts = NULL,
  min.cells = 10,
  robust = FALSE,
  quiet = FALSE,
  BPPARAM = SerialParam(),
  use.eBayes = TRUE,
  ...
)

Arguments

x

SingleCellExperiment or dreamletProcessedData object

formula

regression formula for differential expression analysis

data

metadata used in regression formula

assays

array of assay names to include in analysis. Defaults to assayNames(x)

contrasts

character vector specifying contrasts specifying linear combinations of fixed effects to test. This is fed into makeContrastsDream( formula, data, contrasts=contrasts)

min.cells

minimum number of observed cells for a sample to be included in the analysis

robust

logical, use eBayes method that is robust to outlier genes

quiet

show messages

BPPARAM

parameters for parallel evaluation

use.eBayes

should eBayes be used on result? (defualt: TRUE)

...

other arguments passed to dream

Value

Object of class dreamletResult storing results for each cell type

Details

Fit linear (mixed) model on each cell type separately. For advanced use of contrasts see variancePartition::makeContrastsDream() and vignette https://gabrielhoffman.github.io/variancePartition/articles/dream.html#advanced-hypothesis-testing-1.

See also

variancePartition::dream(), variancePartition::makeContrastsDream()

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...
#> 0.22 secs
#>   CD14+ Monocytes...
#> 0.33 secs
#>   CD4 T cells...
#> 0.26 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...
#> 0.17 secs
#>   CD14+ Monocytes...
#> 0.26 secs
#>   CD4 T cells...
#> 0.19 secs
#>   CD8 T cells...
#> 0.13 secs
#>   FCGR3A+ Monocytes...
#> 0.22 secs

# Examine results
res.dl
#> class: dreamletResult 
#> assays(5): B cells CD14+ Monocytes CD4 T cells CD8 T cells FCGR3A+
#>   Monocytes
#> Genes:
#>  min: 531 
#>  max: 1130 
#> details(7): assay n_retain ... n_errors error_initial
#> coefNames(2): (Intercept) group_idstim

# Examine details for each assay
details(res.dl)
#>               assay n_retain   formula formDropsTerms n_genes n_errors
#> 1           B cells        4 ~group_id          FALSE     847        0
#> 2   CD14+ Monocytes        4 ~group_id          FALSE    1130        0
#> 3       CD4 T cells        4 ~group_id          FALSE     897        0
#> 4       CD8 T cells        4 ~group_id          FALSE     531        0
#> 5 FCGR3A+ Monocytes        4 ~group_id          FALSE    1086        0
#>   error_initial
#> 1         FALSE
#> 2         FALSE
#> 3         FALSE
#> 4         FALSE
#> 5         FALSE

# 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