imsig (version 1.1.3)

imsig: Estimate the relative abundance of tissue-infiltrating immune subpopulations abundances using gene expression data

Description

Estimates the relative abundance of immune cells across patients/samples.

Usage

imsig(exp, r = 0.6, sort = TRUE, sort_by = "T cells")

Arguments

exp

Dataframe of transcriptomic data (natural scale) containing genes as rows and samples as columns. Note: Gene names should be set as row names and duplicates are not allowed. Missing values are not allowed within the expression matrix. Check example- head(example_data): example_data.

r

Use a value between 0 and 1. Default is 0.6. This is a user defined correlation cut-off to perform feature selection (feature_select). Feature selection aids to enrich the prediction of relative abundance of immune cells by filtering off poorly correlated ImSig genes. To get an idea of what cut-off to use check the results of (gene_stat) and choose a cut-off that displays high median correlation and maintains a high proportion of genes after feature selection.

sort

Sort the samples based on abundance of a particular cell type. `Set sort = FALSE` if you wish not to apply sorting. By default the function sorts by abundance of T cells. The cell type of interest for sorting can be controlled by the `sort_by` parameter.

sort_by

Can be used to sort the samples by predicted abundance of a particular cell type. All other cell types follow this sorting. By default it is sorted by `T cells`

Value

Relative abundance of immune cells across samples. Returns a dataframe.

See Also

feature_select, example_data

Examples

# NOT RUN {
cell_abundance = imsig (exp = example_data, r = 0.7, sort=TRUE, sort_by='T cells')
head(cell_abundance)
# }