Reads cell segmentation CSV files and returns a
SpatialCellData-class object. This is the recommended
high-level entry point.
Usage
ReadSpatial(
path,
sample_id = NULL,
markers = NULL,
filter = "DAPI|Blank|Empty",
compartment = "Cell",
statistic = "mean",
sep = "auto",
project = "SpatialProject"
)Arguments
- path
Character. Path to a CSV file or a directory containing CSV files.
- sample_id
Character or
NULL. An identifier for the sample.- markers
Character vector or
NULL. If provided, only these marker columns are retained.- filter
Character or
NA. Regex pattern for marker names to exclude. Default"DAPI|Blank|Empty".- compartment
Character. Compartment for intensity extraction (QuPath full format). Default
"Cell".- statistic
Character. Summary statistic to extract (QuPath full format). Default
"mean".- sep
Character or
"auto". Column delimiter. Default"auto".- project
Character. Project name. Default
"SpatialProject".
Value
A SpatialCellData-class object.
Examples
tmp <- tempfile(fileext = ".csv")
write.csv(data.frame(
`Cell ID` = 1:5,
`Cell X Position` = runif(5, 0, 1000),
`Cell Y Position` = runif(5, 0, 1000),
CD3 = rnorm(5, 300, 80),
CD8 = rnorm(5, 200, 60),
check.names = FALSE
), tmp, row.names = FALSE)
obj <- ReadSpatial(tmp, filter = NA)
obj
#> A SpatialCellData object
#> 5 cells across 1 sample
#> Markers: CD3, CD8
#> Normalised: FALSE
#> Project: SpatialProject
unlink(tmp)