Convert spatial data resulting from Aviti Teton sequencers that have been processed by the Element Biosciences cells2stats workflow to H5MU format.

This component processes cells2stats count matrices to create a standardized H5MU file for downstream analysis.

The component reads:

  • Parquet file containing the count matrix and metadata

  • Panel.json with target and batch information

And outputs an H5MU file with:

  • Count data as the main .X matrix

  • Spatial coordinates in obsm

  • Cell Paint intensities in obsm (optional)

  • Nuclear count data as a layer (optional)

  • CellProfiler morphology metrics in obsm (optional)

  • Unassigned targets in obsm (optional)

Python

Converts the output from NanoString experiment into a MuData objcet.

  • <dataset_id>_exprMat_file.csv: File containing the counts.

  • <dataset_id>_metadata_file: File containing the spatial coordinates and additional cell-level metadata.

  • <dataset_id>_fov_file.csv: File containing the coordinates of all the fields of view.
    In addition to reading the regular Nanostring output, it loads CellComposite and CellLabels directories, if present,
    containing the images.

Python

from_cosmx_to_spatialexperiment

Creates a SpatialExperiment object from the downloaded unzipped CosMx directory for Nanostring
CosMx spatial gene expression data, and saves it as a SpatialExperiment object.
The constructor assumes the downloaded unzipped CosMx Folder has the following structure:

Mandatory files
· | — *_exprMat_file.csv
· | — *_metadata_file.csv
Optional files, by default added to the metadata() as a list of paths (will be converted to parquet):
· | —*_fov_positions_file.csv
· | — *_tx_file.csv
· | — *_polygons.csv

R

Converts a single modality of an h5mu file into a Seurat object with a Field of View (FOV)
built from centroid coordinates of spatially-resolved single cells.

R

Reads in an H5MU file and saves it as a SpatialData Zarr store.

Python

Converts an h5mu file into a SpatialExperiment object.

R

from_spaceranger_hd_to_spatialdata

Converts the output from 10X Genomics Space Ranger for Visium HD into a
SpatialData object.

By default it reads the segmented cells (per-cell count matrix and cell
boundary polygons) that Space Ranger 4.x writes to segmented_outputs/.

Python

spaceranger
visium_hd
+2

Converts the output bundle from spaceranger into an h5mu file.

Python

Reads in the Tables field stored in a SpatialData object and converts it to an h5mu file.

Python

Converts the output from Xenium to a single .h5mu file, where the count matrix is written to the rna modality.
The following files are expected to be present in the Xenium output bundle:
├── cell_feature_matrix.h5
├── cells.parquet
├── experiment.xenium
└── metrics_summary.csv

Python

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