Perform Differential Gene Expression on a SingleCellExperiment

Source: R/diffexp_models.R

Perform Differential Gene Expression on a SingleCellExperiment

perform_de(
  sce,
  de_method = "MASTZLM",
  mast_method = "glm",
  min_counts = 1,
  min_cells_pc = 0.1,
  rescale_numerics = TRUE,
  dependent_var = "group",
  ref_class = "Control",
  confounding_vars = c("individual", "cngeneson", "sex", "age", "PMI", "RIN", "seqdate",
    "pc_mito"),
  random_effects_var = NULL,
  interaction_vars = NULL,
  unique_id_var = "individual",
  species = getOption("scflow_species", default = "human"),
  parallel = TRUE,
  ...
)

Arguments

sce

a SingleCellExperiment object

de_method

The differential gene expression method.

mast_method

If de_method is "MASTZLM" then mast_method should be provided. Possible values are "glm", "glmer", "bayesglm". Default is "glm". For "glmer" and "random_effects_var" should be provided.

min_counts

minimum number of counts

min_cells_pc

percentage of cells with min_counts for gene selection

rescale_numerics

rescaling numerics may improve model

dependent_var

the name of the colData variable for contrasts

ref_class

the class of dependent_var used as reference

confounding_vars

the independent variables of the model

random_effects_var

variable(s) to model as random effects

interaction_vars

two or more variables to model as interacting

unique_id_var

the colData variable identifying unique samples

species

human or mouse

parallel

enable parallel processing

...

advanced options

Value

results_l a list of DE table results