read_cell_seg_data: Read and clean an inForm data file.
In PerkinElmer/phenoptr: inForm Helper Functions

Description Usage Arguments Details Value See Also Examples

read_cell_seg_data makes it easier to use data from PerkinElmer's 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 easily using read.delim or read_tsv. However there is still some useful cleanup to be done.

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read_cell_seg_data(path = NA,
  pixels_per_micron = getOption("phenoptr.pixels.per.micron"),
  remove_units = TRUE)

`path`	Path to the file to read, or NA to use a file chooser.
`pixels_per_micron`	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 `'auto'` to read from an associated `component_data.tif` file.
`remove_units`	If TRUE (default), remove the unit name from expression columns.

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 component_data.tif file.
Optionally removes units from expression names
If the file contains multiple sample names, a 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.

If pixels_per_micron='auto', read_cell_seg_data looks for a component_data.tif file in the same directory as path. If found, pixels_per_micron is read from the file and the cell coordinates are offset to the correct spatial location.

A data_frame containing the cleaned-up data set.

Other file readers: get_field_info, list_cell_seg_files, read_components, read_maps

path <- sample_cell_seg_path()
csd <- read_cell_seg_data(path)

# count all the phenotypes in the data
table(csd$Phenotype)

## Not run: 
# Use purrr::map_df to read all cell seg files in a directory
# and return a single data_frame.
paths <- list_cell_seg_files(path)
csd <- purrr::map_df(paths, read_cell_seg_data)

## End(Not run)