View source: R/preprocessing.R
ReadNanostring | R Documentation |
Read and Load Nanostring SMI data
ReadNanostring(
data.dir,
mtx.file = NULL,
metadata.file = NULL,
molecules.file = NULL,
segmentations.file = NULL,
type = "centroids",
mol.type = "pixels",
metadata = NULL,
mols.filter = NA_character_,
genes.filter = NA_character_,
fov.filter = NULL,
subset.counts.matrix = NULL,
cell.mols.only = TRUE
)
LoadNanostring(data.dir, fov, assay = "Nanostring")
data.dir |
Path to folder containing Nanostring SMI outputs |
mtx.file |
Path to Nanostring cell x gene matrix CSV |
metadata.file |
Contains metadata including cell center, area, and stain intensities |
molecules.file |
Path to molecules file |
segmentations.file |
Path to segmentations CSV |
type |
Type of cell spatial coordinate matrices to read; choose one or more of:
|
mol.type |
Type of molecule spatial coordinate matrices to read; choose one or more of:
|
metadata |
Type of available metadata to read; choose zero or more of:
|
mols.filter |
Filter molecules that match provided string |
genes.filter |
Filter genes from cell x gene matrix that match provided string |
fov.filter |
Only load in select FOVs. Nanostring SMI data contains 30 total FOVs. |
subset.counts.matrix |
If the counts matrix should be built from molecule coordinates for a specific segmentation; One of:
|
cell.mols.only |
If TRUE, only load molecules within a cell |
fov |
Name to store FOV as |
assay |
Name to store expression matrix as |
ReadNanostring
: A list with some combination of the
following values:
“matrix
”: a
sparse matrix with expression data; cells
are columns and features are rows
“centroids
”: a data frame with cell centroid
coordinates in three columns: “x”, “y”, and “cell”
“pixels
”: a data frame with molecule pixel coordinates
in three columns: “x”, “y”, and “gene”
LoadNanostring
: A Seurat
object
This function uses
progressr to
render status updates and progress bars. To enable progress updates, wrap
the function call in with_progress
or run
handlers(global = TRUE)
before running
this function. For more details about progressr, please read
vignette("progressr-intro")
This function uses
future to enable
parallelization. Parallelization strategies can be set using
plan
. Common plans include “sequential
”
for non-parallelized processing or “multisession
” for parallel
evaluation using multiple R sessions; for other plans, see the
“Implemented evaluation strategies” section of
?future::plan
. For a more thorough introduction
to future, see
vignette("future-1-overview")
This function requires the data.table package to be installed
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