Generates a matrix of counts aggregated by gene and/or cell group.
Usage
bb_aggregate(
obj,
assay = "RNA",
experiment_type = "Gene Expression",
gene_group_df = NULL,
cell_group_df = NULL,
norm_method = c("log", "binary", "size_only"),
pseudocount = 1,
scale_agg_values = TRUE,
max_agg_value = 3,
min_agg_value = -3,
binary_min = 0,
exclude.na = TRUE
)Arguments
- obj
A Seurat or cell data set object
- assay
Gene expression assay to use for aggregation; currently only applies to Seurat objects, Default: 'RNA'
- gene_group_df
A 2-column dataframe with gene names or ids and gene groupings, Default: NULL
- cell_group_df
A 2-coumn dataframe with cell ids and gene groupings, Default: NULL
- norm_method
Gene normalization method, Default: c("log", "binary", "size_only")
- pseudocount
Pseudocount, Default: 1
- scale_agg_values
Whether to scale the aggregated values, Default: TRUE
- max_agg_value
If scaling, make this the maximum aggregated value, Default: 3
- min_agg_value
If scaling, make this the minimum aggregated value, Default: -3
- binary_min
Minimum value below which a cell is considered not to express a feature, Default: 0
- exclude.na
Exclude NA?, Default: TRUE
Details
The best way to group genes or cells is by using bb_*meta and then select cell_id or feature_id plus one metadata column with your group labels.
See also
cli_div, cli_alert
normalized_counts, my.aggregate.Matrix
character(0)