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create.sumtable - create a data.table 'summarising' cellular data by sample and population/cluster.

Source: R/create.sumtable.R
create.sumtable.Rd

This function summarises cellular data and generates a summary data.table

Usage

create.sumtable(dat, sample.col, pop.col, use.cols, annot.cols, counts, func, sep)

Arguments

dat

NO DEFAULT. A data.table containing cells (rows) vs features/markers (columns). One column must represent sample names, and another must represent populations/clusters.

sample.col

NO DEFAULT. Character. Name of the sample column (e.g. "Sample").

pop.col

NO DEFAULT. Character. Name of the population/cluster column (e.g. "Population", "Cluster").

use.cols

DEFAULT = NULL A character vector indicating the columns to be measured (e.g. cellular columns – c("CD45", "CD3e") etc).

annot.cols

DEFAULT = NULL. A character vector indicating the columns to be included as annotation columns (e.g. c("Batch", "Group") etc).

parent.col

DEFAULT = NULL. A character entry indicating a column that represents the 'lineage' each population belongs to (e.g. 'CD4 T cells' may belong to the 'T cells' lineage). Use this to also calculate each population as a percentage of lineage.

counts

DEFAULT = NULL. If you wish to calculate the actual number of cells per sample, a data.frame or data.table containing the sample names (in column 1) and cell counts per sample (column 2).

perc.pos

DEFAULT = NULL. If you wish to calculate the percentage of each population that is 'positive' for a marker, you can provide a data.table containing the mark names (in column 1) and cut off values for positivity (column 2).

double.pos

DEDAULT = NULL. List of vectors, each vector containing the names of multiple markers you wish to calculate % positive for (e.g. CD38+HLADR+). Generates 'and' and 'or' combinations.

func

DEFAULT = "median". Can be "median" or "mean". Defines the type of function for calculating MFI data.

sep

DEFAULT = " – ". Character separation of the measurement type and the population (e.g. MFI of CD4 – T cells)

References

https://github.com/ImmuneDynamics/Spectre.

Author

Thomas M Ashhurst, thomas.ashhurst@sydney.edu.au

Examples

## Calculate and export results from demonstration data
dat <- Spectre::demo.clustered
counts <- data.frame('Sample' = unique(dat[['Sample']]), 
'Counts' = c(rep(100000, 6), rep(1000000, 6)))

sum.dat <- create.sumtable(dat = dat,
                            sample.col = "Sample",
                            pop.col = "Population", 
                            use.cols = names(dat)[c(11:19)],
                            counts = counts)
#> Loading required package: data.table
#> Creating sumtable
#>  -- running some initial tests
#>  -- calculting cell proportions
#>  -- calculating cell counts
#>  -- calculting expression levels
#>  -- wrapping up
#>  -- sumtable complete!