Wrapper around `liana_wrap` to run liana for each sample.
Usage
liana_bysample(
sce,
idents_col,
sample_col,
verbose = TRUE,
inplace = TRUE,
aggregate_how = NULL,
...
)Arguments
- idents_col
name of the cluster column
- sample_col
name of the sample/context column
- verbose
verbosity logical
- inplace
logical (TRUE by default) if liana results are to be saved to the SingleCellExperiment object (`sce@metadata$liana_res`)
- aggregate_how
if running multiple methods (default), then one cal also choose to aggregate the CCC results by sample.
- ...
Arguments passed on to
liana_wrapsce`SingleCellExperiment` object or `SeuratObject`
methodmethod(s) to be run via liana
resourceresource(s) to be used by the methods (`Consensus` by default), Use `all` to run all `human` resources in one go), or `custom` to run liana_wrap with an appropriately formatted custom resource, passed via `exernal_resource`
external_resourceexternal resource in OmniPath tibble format
min_cellsminimum cell per cell identity to be considered for analysis
return_allwhether to return all possible interactions. Any interaction with `expr_prop` below the specific threshold will be assigned to the *worst* possible score in those that pass the threshold. For example, p-values from CellPhoneDB will be assigned to max(pvalue) - likely 1, and lr_means will be assigned to min(lr_means). Note that this applies only to the internal methods of liana.
supp_columnsany supplementary/additional columns which are to be returned by liana. Possibilities include: c("ligand.expr", "receptor.expr" "ligand.stat", "receptor.stat", "ligand.pval", "receptor.pval", "ligand.FDR", "receptor.FDR", etc)
assayassay to be used by Seurat, by default set to `NULL` and will use the DefaultAssay.
.simplifyif methods are run with only 1 resource, return a list of tibbles for each method (default), rather than a list of lists with method-resource combinations
baseDefault to NULL (i.e. log2-transformation is assumed for SCE, and log-tranformation for Seurat). This is a requred step for the calculation of the logFC method - ensures that any other preprocessing of the counts is preserved. One could also pass `NaN` if they wish to use the counts stored in the counts assay/slot, or any other number according to the base that was used for log-tranformation.
cell.adjcell adjacency tibble/dataframe /w weights by which we will `multiply` the relevant columns. Any cell pairs with a weights of 0 will be filtered out. Note that if working with LIANA's default methods, we suggest weights >= 0 & =< 1. This ensure that all methods' score will be meaningfully weighed without changing the interpretation of their scores, thus allow one to filter SCA, rank NATMI, etc.