# Loading CosMx and Xenium data with SpaceTrooper

Dario Righelli

#### 8 December 2025

# 1 Overview

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`

# 2 CosMx (Nanostring)

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] "/private/var/folders/r0/l4fjk6cj5xj0j3brt4bplpl40000gt/T/Rtmp9op4Uc/Rinst82af2dfc9aa7/SpaceTrooper/extdata/CosMx_DBKero_Tiny"

## 2.1 Route A — Direct loading with SpaceTrooper

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_id

Inspect 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] "/private/var/folders/r0/l4fjk6cj5xj0j3brt4bplpl40000gt/T/Rtmp9op4Uc/Rinst82af2dfc9aa7/SpaceTrooper/extdata/CosMx_DBKero_Tiny/DBKero-polygons.csv"

### 2.1.1 CosMx protein

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)$technology

## 2.2 Route B — Via SpatialExperimentIO, then standardize

If 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] TRUE

# 3 Xenium (10x Genomics)

A small Xenium example is also included for demonstration.

    xepath <- system.file("extdata", "Xenium_small", package = "SpaceTrooper")
    xepath
    #> [1] "/private/var/folders/r0/l4fjk6cj5xj0j3brt4bplpl40000gt/T/Rtmp9op4Uc/Rinst82af2dfc9aa7/SpaceTrooper/extdata/Xenium_small"

## 3.1 Route A — Direct loading with SpaceTrooper

`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_id

Quick 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)$polygons
    #> [1] "/private/var/folders/r0/l4fjk6cj5xj0j3brt4bplpl40000gt/T/Rtmp9op4Uc/Rinst82af2dfc9aa7/SpaceTrooper/extdata/Xenium_small/cell_boundaries.parquet"
    metadata(spe_xen_a)$technology
    #> [1] "10X_Xenium"

## 3.2 Route B — Via SpatialExperimentIO, then standardize

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_id

Validate:

    identical(metadata(spe_xen_b)$technology, "10X_Xenium")
    #> [1] TRUE
    identical(spe_xen_a, spe_xen_b)
    #> [1] TRUE

# 4 File layout notes

- CosMx: per-cell metadata (e.g. `metadata_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`.
- Xenium: unzipped bundle contains an `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.

# 5 Session info

    sessionInfo()
    #> R Under development (unstable) (2025-11-04 r88984)
    #> Platform: aarch64-apple-darwin20
    #> Running under: macOS Ventura 13.7.8
    #>
    #> Matrix products: default
    #> BLAS:   /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
    #> LAPACK: /Library/Frameworks/R.framework/Versions/4.6-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.1
    #>
    #> locale:
    #> [1] C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
    #>
    #> time zone: America/New_York
    #> tzcode source: internal
    #>
    #> attached base packages:
    #> [1] stats4    stats     graphics  grDevices utils     datasets  methods
    #> [8] base
    #>
    #> other attached packages:
    #>  [1] SpaceTrooper_1.1.0          SpatialExperiment_1.21.0
    #>  [3] SingleCellExperiment_1.33.0 SummarizedExperiment_1.41.0
    #>  [5] Biobase_2.71.0              GenomicRanges_1.63.1
    #>  [7] Seqinfo_1.1.0               IRanges_2.45.0
    #>  [9] S4Vectors_0.49.0            BiocGenerics_0.57.0
    #> [11] generics_0.1.4              MatrixGenerics_1.23.0
    #> [13] matrixStats_1.5.0           BiocStyle_2.39.0
    #>
    #> loaded via a namespace (and not attached):
    #>   [1] DBI_1.2.3                 gridExtra_2.3
    #>   [3] rlang_1.1.6               magrittr_2.0.4
    #>   [5] scater_1.39.0             e1071_1.7-16
    #>   [7] compiler_4.6.0            DelayedMatrixStats_1.33.0
    #>   [9] SpatialExperimentIO_1.3.0 sfheaders_0.4.5
    #>  [11] vctrs_0.6.5               shape_1.4.6.1
    #>  [13] pkgconfig_2.0.3           fastmap_1.2.0
    #>  [15] backports_1.5.0           magick_2.9.0
    #>  [17] XVector_0.51.0            labeling_0.4.3
    #>  [19] scuttle_1.21.0            rmarkdown_2.30
    #>  [21] ggbeeswarm_0.7.3          tinytex_0.58
    #>  [23] purrr_1.2.0               bit_4.6.0
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    #>  [31] DelayedArray_0.37.0       Rhdf5lib_1.33.0
    #>  [33] BiocParallel_1.45.0       irlba_2.3.5.1
    #>  [35] broom_1.0.11              parallel_4.6.0
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