Compute cell type processing
Usage
compute.deconvolution.analysis(
deconvolution,
corr = 0.7,
corr_type = "spearman",
zero_thr = 0.9,
cv_thr = 0.1,
batch = NULL,
cells_extra = NULL,
file_name = NULL,
return = FALSE,
verbose = FALSE
)Arguments
- deconvolution
Deconvolution output of compute.deconvolution() with features as columns and samples as rows
- corr
Minimum correlation threshold for subgroupping the deconvolution features
- corr_type
Correlation type for computing the cell subgroups, whether "spearman" or "pearson".
- zero_thr
Maximum fraction of zeros allowed per feature before it is discarded.
- cv_thr
Minimum coefficient of variation (standard deviation / mean) across samples; features below it are removed.
- batch
Optional batch labels, one per sample in the same order as the rows. A factor or character is treated as categorical: correlations become partial correlations controlling for one indicator column per batch. A numeric vector is used as a single linear covariate. With only one batch, ordinary correlations are used.
- cells_extra
A string specifying the cells names to consider and that are not including in the nomenclature of multideconv (see Readme). This includes groups created with
aggregate_cell_groups()under a new name (e.g.Lymphocytes): if they are not listed here they are discarded.- file_name
A string specifying the file name of the .csv file with the deconvolution subgroups
- return
Boolean value to whether return and saved the plot and csv files of deconvolution generated during the run inside the Results/ directory.
- verbose
Boolen value to whether print or no the function messages
Value
A list containing
A matrix with the deconvolution after processing
The deconvolution subgroups per cell type
The deconvolution subgroups composition
The discarded features because they contain a high number of zeros across samples (> 90%)
Discarded features due to low variance across samples
Discarded cell types because they are not supported in the pipeline
Examples
data("deconvolution")
processed_deconvolution = compute.deconvolution.analysis(deconvolution, corr = 0.7)
processed_deconvolution = compute.deconvolution.analysis(deconvolution, cells_extra = "mesenchymal")