This vignette shows how to build a SpatialExperiment (SPE) from: - Nanostring CosMx (RNA/protein) outputs - 10x Genomics Xenium outputs
For each technology, we demonstrate: - Route A: SpaceTrooper’s high-level reader (readCosmxSPE, readCosmxProteinSPE, readXeniumSPE) - Route B: read with SpatialExperimentIO, then standardize with updateCosmxSPE / updateXeniumSPE
We begin with CosMx. The package ships with a small CosMx example for demonstration.
cospath <- system.file(file.path("extdata", "CosMx_DBKero_Tiny"), package="SpaceTrooper")
cospath
#> [1] "/tmp/Rtmp5Xkh9H/Rinstf03b8659e84ad/SpaceTrooper/extdata/CosMx_DBKero_Tiny"Use readCosmxSPE() to construct an SPE from CosMx outputs; it also normalizes names/metadata and records polygons/FOV info if present.
spe_cos <- readCosmxSPE(
dirName=cospath,
sampleName="DBKero_Tiny",
coordNames=c("CenterX_global_px", "CenterY_global_px"),
countMatFPattern="exprMat_file.csv",
metadataFPattern="metadata_file.csv",
polygonsFPattern="polygons.csv",
fovPosFPattern="fov_positions_file.csv",
fovdims=c(xdim=4256, ydim=4256)
)
spe_cos
#> class: SpatialExperiment
#> dim: 1010 905
#> metadata(4): fov_positions fov_dim polygons technology
#> assays(1): counts
#> rownames(1010): RAMP2 CD83 ... NegPrb09 NegPrb10
#> rowData names(0):
#> colnames(905): f16_c1 f16_c10 ... f16_c98 f16_c99
#> colData names(20): fov cellID ... sample_id cell_id
#> reducedDimNames(0):
#> mainExpName: NULL
#> altExpNames(0):
#> spatialCoords names(2) : CenterX_global_px CenterY_global_px
#> imgData names(1): sample_idInspect essentials:
assayNames(spe_cos)
#> [1] "counts"
dim(spe_cos)
#> [1] 1010 905
head(colnames(spatialCoords(spe_cos)))
#> [1] "CenterX_global_px" "CenterY_global_px"
metadata(spe_cos)$technology
#> [1] "Nanostring_CosMx"
metadata(spe_cos)$polygons
#> [1] "/tmp/Rtmp5Xkh9H/Rinstf03b8659e84ad/SpaceTrooper/extdata/CosMx_DBKero_Tiny/DBKero-polygons.csv"If working with CosMx Protein, use the convenience wrapper:
protfolder <- system.file("extdata", "S01_prot", package = "SpaceTrooper")
spe_cos_prot <- readCosmxProteinSPE(
dirName=protfolder,
sampleName="cosmx_prots",
coordNames=c("CenterX_global_px", "CenterY_global_px"),
countMatFPattern="exprMat_file.csv",
metadataFPattern="metadata_file.csv",
polygonsFPattern="polygons.csv",
fovPosFPattern="fov_positions_file.csv",
fovdims=c(xdim=4256, ydim=4256)
)
metadata(spe_cos_prot)$technologyIf you prefer to read with SpatialExperimentIO first, upgrade the object with updateCosmxSPE() to harmonize names/metadata and attach polygons.
spe_cos_raw <- SpatialExperimentIO::readCosmxSXE(
dirName=cospath,
returnType="SPE",
countMatPattern="exprMat_file.csv",
metaDataPattern="metadata_file.csv",
coordNames=c("CenterX_global_px", "CenterY_global_px"),
addFovPos=TRUE,
fovPosPattern="fov_positions_file.csv",
altExps=NULL,
addParquetPaths=FALSE
)
spe_cos_std<-updateCosmxSPE(
spe=spe_cos_raw,
dirName=cospath,
sampleName="DBKero_Tiny",
polygonsFPattern="polygons.csv",
fovdims=c(xdim=4256, ydim=4256)
)
identical(spe_cos_std, spe_cos)
#> [1] TRUEA small Xenium example is also included for demonstration.
xepath <- system.file("extdata", "Xenium_small", package = "SpaceTrooper")
xepath
#> [1] "/tmp/Rtmp5Xkh9H/Rinstf03b8659e84ad/SpaceTrooper/extdata/Xenium_small"readXeniumSPE() builds the SPE from a Xenium Output Bundle (root or outs/) and standardizes metadata.
Key options: - type: "HDF5" or "sparse" (feature matrix) - boundariesType: "parquet" or "csv" (cell boundaries) - computeMissingMetrics: compute QC metrics (area/aspect ratio) if needed - keepPolygons: append polygons to colData - addFOVs: derive FOV IDs from transcript parquet
spe_xen_a <- readXeniumSPE(
dirName=xepath,
type="HDF5",
coordNames=c("x_centroid", "y_centroid"),
boundariesType="parquet",
computeMissingMetrics=TRUE,
keepPolygons=TRUE,
countsFilePattern="cell_feature_matrix",
metadataFPattern="cells.csv.gz",
polygonsFPattern="cell_boundaries",
polygonsCol="polygons",
txPattern="transcripts",
addFOVs=FALSE
)
#> Computing missing metrics, this could take some time...
spe_xen_a
#> class: SpatialExperiment
#> dim: 4 6
#> metadata(2): polygons technology
#> assays(1): counts
#> rownames(4): ABCC11 ACTA2 ACTG2 ADAM9
#> rowData names(3): ID Symbol Type
#> colnames(6): 1 2 ... 5 6
#> colData names(11): X cell_id ... Area_um polygons
#> reducedDimNames(0):
#> mainExpName: NULL
#> altExpNames(0):
#> spatialCoords names(2) : x_centroid y_centroid
#> imgData names(1): sample_idQuick checks:
assayNames(spe_xen_a)
#> [1] "counts"
dim(spe_xen_a)
#> [1] 4 6
colnames(spatialCoords(spe_xen_a))
#> [1] "x_centroid" "y_centroid"
metadata(spe_xen_a)$polygons
#> [1] "/tmp/Rtmp5Xkh9H/Rinstf03b8659e84ad/SpaceTrooper/extdata/Xenium_small/cell_boundaries.parquet"
metadata(spe_xen_a)$technology
#> [1] "10X_Xenium"Read with SpatialExperimentIO and then pass through updateXeniumSPE() for SpaceTrooper-standardized metadata and optional metrics/FOV extraction.
spe_xen_b <- SpatialExperimentIO::readXeniumSXE(
dirName=xepath,
countMatPattern="cell_feature_matrix.h5",
metaDataPattern="cells.csv.gz",
coordNames=c("x_centroid", "y_centroid"),
returnType="SPE",
addExperimentXenium=FALSE,
altExps=NULL,
addParquetPaths=FALSE
)
spe_xen_b <- updateXeniumSPE(
spe=spe_xen_b,
dirName=xepath,
boundariesType="parquet",
computeMissingMetrics=TRUE,
keepPolygons=TRUE,
polygonsFPattern="cell_boundaries",
polygonsCol="polygons",
txPattern="transcripts",
addFOVs=FALSE
)
#> Computing missing metrics, this could take some time...
spe_xen_b
#> class: SpatialExperiment
#> dim: 4 6
#> metadata(2): polygons technology
#> assays(1): counts
#> rownames(4): ABCC11 ACTA2 ACTG2 ADAM9
#> rowData names(3): ID Symbol Type
#> colnames(6): 1 2 ... 5 6
#> colData names(11): X cell_id ... Area_um polygons
#> reducedDimNames(0):
#> mainExpName: NULL
#> altExpNames(0):
#> spatialCoords names(2) : x_centroid y_centroid
#> imgData names(1): sample_idValidate:
identical(metadata(spe_xen_b)$technology, "10X_Xenium")
#> [1] TRUE
identical(spe_xen_a, spe_xen_b)
#> [1] TRUEmetadata_file.csv), expression matrix (exprMat_file.csv), optional polygon CSV (polygons.csv), and FOV positions. updateCosmxSPE() also fixes common field names and records FOV dims in metadata.outs/ folder. Feature matrix may be cell_feature_matrix.h5 (HDF5) or sparse folder; cells metadata cells.csv.gz; boundaries as .parquet or .csv.gz; transcript parquet for FOV attribution. readXeniumSPE() auto-detects outs/ if you pass the bundle root.sessionInfo()
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