DO.MultiDGE

Performs differential gene expression analysis using both single-cell and pseudo-bulk approaches across all annotated cell types. The single-cell method uses Seurat's FindMarkers, while pseudo-bulk testing uses DESeq2 on aggregated expression profiles. Outputs a merged data frame with DGE statistics from both methods per condition and cell type.

Usage

DO.MultiDGE(
  Seu_obj,
  assay = "RNA",
  method_sc = "wilcox",
  group_by = "condition",
  annotation_col = "annotation",
  sample_col = "orig.ident",
  ident_ctrl = "ctrl",
  min_pct = 0,
  logfc_threshold = 0,
  only_pos = F,
  min_cells_group = 3,
  ...
)

Arguments

assay

Specified assay in Seurat object, default "RNA"

method_sc

method to use for single cell DEG analysis, see FindMarkers from Seurat for options, default "wilcox"

group_by

Column in meta data containing groups used for testing, default "condition"

annotation_col

Column in meta data containing information of cell type annotation

sample_col

Column in meta data containing information of sample annotation, default "orig.ident"

ident_ctrl

Name of the condition in group_by to test against as ctrl, default "ctrl"

min_pct

only test genes that are detected in a minimum fraction of min.pct cells in either of the two populations, default is 0

logfc_threshold

Limit testing to genes which show, on average, at least X-fold difference (log-scale) between the two groups of cells, default is 0.

only_pos

Only return positive markers, default FALSE

min_cells_group

Minimum number of cells in one of the groups, default 3

Seu_object

The seurat object

Author

Mariano Ruz Jurado

Examples

if (FALSE) { # \dontrun{
merged_dge_results <- DO.MultiDGE(
  Seu_obj = your_seurat_object,
  group_by = "condition",
  annotation_col = "celltype",
  ident_ctrl = "healthy"
)
head(merged_dge_results)
} # }