write.sumtables: write.sumtables - Export generated data as a .csv file

Description Usage Arguments Author(s) References Examples

View source: R/write.sumtables.R

Description

This function summarises generated results and exports them as a .csv file. It can include the calculation of median fluorescence intensity (or equivalent), proportion (%) or cell counts for clusters/subsets of interest. Makes use of the packages 'data.table' and 'tidyr' to handle the data.

Usage

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make.sumtables(dat, sample.col, pop.col, measure.col, annot.col, group.col, do.proportions, cell.counts, do.mfi.per.sample, do.mfi.per.marker, perc.pos.markers, perc.pos.cutoff, mfi.type, path)

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").

measure.col

NO DEFAULT. A character or numeric vector indicating the columns to be measured (e.g. cellular columns – c("CD45", "CD3e") etc).

annot.col

DEFAULT = NULL. A character or numeric vector indicating the columns to be included as annotation columns (e.g. cellular columns – c("Batch", "Group") etc). If groups are present, this column must be present here also.

group.col

DEFAULT = NULL. Character. Which column represent groups (e.g. "Group"). This is for dividing data within the function. If you wish for groups to be included in the annotations, you will need to also include it in the annot.col argument.

do.proportions

DEFAULT = TRUE. Do you wish to create cell proportion and/or cell count results?

cell.counts

DEFAULT = NULL. If you wish to generate cell.count results, a vector of cell counts (e.g. c(1000, 1500, 2439,)) representing the cell counts in each of the samples. Must be entered in the order the that unique sample names appear in the dataset.

do.mfi.per.sample

DEFAULT = TRUE. Do you wish to generate MFI data (markers vs clusters) for each sample?

do.mfi.per.marker

DEFAULT = FALSE. Do you wish to generate MFI data (sample vs clusters) for each marker?

perc.pos.markers

DEFAULT = NULL. A vector of column names of calculating percent positive summary stats.

perc.pos.cutoff

DEFAULT = NULL. A vector of 'positive' cut-off values for the markers defined in perc.pos.markers. Must be in same order.

mfi.type

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

path

DEFAULT = getwd(). Defines the directory for write CSV files.

Author(s)

Thomas M Ashhurst, thomas.ashhurst@sydney.edu.au Felix Marsh-Wakefield, felix.marsh-wakefield@sydney.edu.au

References

https://sydneycytometry.org.au/spectre.

Examples

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# Calculate and export results from demonstration data
Spectre::write.sumtables(dat = Spectre::demo.clustered,
                         sample.col = "Sample",
                         pop.col = "FlowSOM_metacluster",
                         measure.col = c(2,5:6,8:9,11:13,16:19,21:30,32),
                         annot.col = c(33:34,36:37),
                         group.col = "Group",
                         cell.counts = c(rep(2.0e+07, 6), rep(1.8e+07, 6)),
                         do.mfi.per.marker = TRUE,
                         perc.pos.markers = c("BV711.SCA.1","APC.BrdU"),
                         perc.pos.cutoff = c(580, 450)
                         )

sydneycytometry/Spectre documentation built on March 20, 2021, 2:15 a.m.