read_cell_seg_data makes it easier to use data from Akoya Biosciences'
inForm program. It reads data files written by inForm 2.0 and later and does
useful cleanup on the result. Data files written by inForm 2.0 can be read
read_tsv. However there is still some useful cleanup to
read_cell_seg_data(path = NA, pixels_per_micron = getOption("phenoptr.pixels.per.micron"), remove_units = TRUE)
Path to the file to read, or NA to use a file chooser.
Conversion factor to microns
(default 2 pixels/micron, the resolution of 20x MSI fields
taken on Vectra Polaris and Vectra 3.).
Set to NA to skip conversion. Set to
If TRUE (default), remove the unit name from expression columns.
containing the cleaned-up data set.
read_cell_seg_data reads both single-image tables and merged tables
and does useful cleanup on the data:
Removes columns that are all NA. These are typically unused summary columns.
Converts percent columns to numeric fractions.
Converts pixel distances to microns. The conversion factor may be
specified as a parameter, by setting
options(phenoptr.pixels.per.micron), or by reading an associated
Optionally removes units from expression names
If the file contains multiple sample names,
tag column is created
containing a minimal, unique tag for each sample.
This is useful when a
short name is needed, for example in chart legends.
read_cell_seg_data looks for
component_data.tif file in the same directory as
pixels_per_micron is read from the file and
the cell coordinates are offset to the correct spatial location.
path <- sample_cell_seg_path() csd <- read_cell_seg_data(path) # count all the phenotypes in the data table(csd$Phenotype)#> #> CD68+ CD8+ CK+ FoxP3+ other #> 417 228 2257 228 2942