10x Genomics Support/Xenium Onboard Analysis 1.9/Analysis/

Understanding Xenium Outputs

This page describes raw output (decoded transcript counts and morphology images) and other standard output files derived from them, which are included in the Xenium output directory for each selected region (also see Archiving Xenium data). These data reduce low-level internal image sensor data, preserving details needed to assess decoded transcript quality (learn more at Overview of Xenium Algorithms).

Important
See what's new in the Xenium Onboard Analysis software pipeline. Click here to read the release notes.

All run data will be stored in the output/ directory on the Xenium Analysis Computer and will be accessible on the Desktop. Refer to the Xenium Instrument User Guide (CG000584) for instructions to export run data from the instrument.

Within the output/ directory, the data from individual runs are stored as subfolders and include the user-defined run name in the folder name. Within the top-level run folder, there are subfolders for each of the user-defined regions on the Xenium slides. The overall organization of subfolders is shown below:

output └── <yyyymmdd>__<hhmmss>__<runName> └── output-<instrumentSN>__<slideID>__<regionName>__<yyyymmdd>__<hhmmss>

The runName and regionName strings are user-defined; the other components of the directory names are auto-generated. <yyymmdd> is the start date and <hhmmss> is the start time (in UTC). The separators between the strings in the directory name are two underscores. Spaces in runName and regionName will be replaced by an underscore (_) in the output directory name.

The remaining sections describe the Xenium output bundle files for each analysis run.

The experiment.xenium is an experiment manifest file in JSON format that includes experiment metadata and relative file paths to other data files in the output folder needed by Xenium Explorer to visualize results.

FieldDescription
major_versionIndicates major version of analysis output file formats read by Xenium Explorer
minor_versionIndicates minor version of analysis output file formats read by Xenium Explorer
patch_versionPatch version of analysis output file formats read by Xenium Explorer
run_nameUser-specified Run Name entered on instrument
run_start_timeInstrument run start time
region_nameUser-specified name for region selected on instrument
preservation_methodUser-specified sample preservation method
num_cellsCells detected by Xenium Onboard Analysis pipeline
transcripts_per_cellMedian transcripts per cell calculated by Xenium Onboard Analysis pipeline
transcripts_per_100umTranscripts per 100 µm2 calculated by Xenium Onboard Analysis pipeline
cassette_nameUser-specified Xenium Cassette Name entered on instrument
slide_idUser-specified Xenium Slide ID entered on instrument
panel_design_idPanel design ID specified by panel selection on instrument (additionally panel_predesigned_id is included for add-on custom panel designs)
panel_namePanel name specified by panel selection on instrument
panel_organismSample organism specified by selected gene panel
panel_tissue_typeUser-specified tissue type selected on instrument
panel_num_targets_predesignedNumber of gene targets from the pre-designed gene panel
panel_num_targets_customNumber of gene targets from add-on custom panel if included in panel design
pixel_sizePixel size in the morphology.ome.tif image file (in µm)
instrument_snXenium Analyzer instrument serial number
instrument_sw_versionVersion of the Xenium Analyzer firmware used during analysis run
analysis_sw_versionVersion of Xenium Onboard Analysis pipeline used to analyze data
analysis_uuidInstrument metadata
experiment_uuidInstrument metadata
cassette_uuidInstrument metadata
roi_uuidInstrument metadata
z_step_sizeZ-step size (in µm) used for subsampling the morphology.ome.tif image Z-stacks
well_uuidInstrument metadata
calibration_uuidInstrument metadata
imagesSpecifies the file paths to the morphology image files; used by Xenium Explorer to find input files
xenium_explorer_filesSpecifies the file paths to transcript, cell, secondary analysis, and analysis summary files; used by Xenium Explorer to find input files
xenium_rangerIf the data was reanalyzed with Xenium Ranger, this section specifies the run_id, Xenium Ranger version, and commands used to analyze the data.

The Xenium onboard analysis pipeline outputs an interactive HTML file named analysis_summary.html. Open it on-instrument, in a web browser, or in Xenium Explorer. It contains summary metrics and automated secondary analysis results. Any alerts issued by the pipeline are displayed at the top of the page.

There are four clickable tabs that capture different information:

  • The Summary tab contains summary metrics, images, and experiment information for a quick overview of the data.
  • The Decoding tab contains more specific transcript decoding metrics.
  • The Cell Segmentation tab shows the metrics for cell segmentation and partitioning transcripts into single cells.
  • The Analysis tab captures the results from the pipeline's secondary analysis run on single cell data.
  • The Image QC tab contains a gallery of RNA images for each cycle and channel.

Click the ? at the top of each dashboard for more information about each metric. For detailed descriptions and guidance on interpretation, see the Overview of the Xenium Analysis Summary documentation.

A series of tissue morphology images are output by the pipeline, which are nuclei-stained (DAPI) images in OME-TIFF format. These files include a pyramid of resolutions and tiled chunks of image data, which allows for efficient interactive image visualization (JPEG-2000 compression, 16-bit grayscale, full and downsampled resolutions down to 256 x 256 pixels, learn more here). All morphology image files can be read by Xenium Explorer.

  • The morphology.ome.tif is a 3D Z-stack of the DAPI image that can be useful to resegment cells, assess segmentation quality, and view data. DAPI image processing is described here.
  • The morphology_mip.ome.tif is a 2D maximum projection intensity (MIP) image of the tissue morphology image.
  • The morphology_focus.ome.tif is a 2D autofocus projection image of the tissue morphology image.

The cell summary file (cells.csv.gz) in gzipped CSV format contains data to help QC the transcript counts for each identified cell. The file contains one row for each cell, with the following columns:

Column NameDescription
cell_idUnique ID of the cell, consisting of a cell prefix and dataset suffix
x_centroidX location of the cell centroid in µm
y_centroidY location of the cell centroid in µm
transcript_countsMolecule count of gene features with Q-Score ≥ 20
control_probe_countsMolecule count of negative control probes
control_codeword_countsCount of negative control codewords
unassigned_codeword_countsCount of unassigned codewords
deprecated_codeword_countsCount of deprecated codewords
total_countsSum total of transcript_counts, control_probe_counts, control_codeword_counts, and unassigned_codeword_counts
cell_areaThe two-dimensional area covered by the cell in µm2
nucleus_areaThe two-dimensional area covered by the nucleus in µm2

The cell summary is also provided in Parquet format (cells.parquet) to enable faster loading and reading of data.

Nucleus boundaries are determined by a nucleus segmentation algorithm that runs on the nuclei-stained (DAPI) morphology image. Cell boundaries are determined by expanding the nucleus boundaries or until the expanded boundary hits another cell.

The masks in the cells Zarr file can be used for downstream cell segmentation or morphology analysis. The polygon information in the cells Zarr file and nucleus and cell boundary output files are a simplification of the true segmentation masks and are only intended for data visualization.

The cells.zarr.zip file in zipped Zarr format contains segmentation masks and boundaries for nuclei and cells. These segmentation masks are used for assigning transcripts to cells. The boundary polygons are approximations of the segmentation masks, and are provided for efficient visualization of cell segmentation in Xenium Explorer and other analysis software. See Overview of Xenium Zarr Output Files for file specifications.

The nucleus_boundaries.csv.gz and cell_boundaries.csv.gz are the CSV representation of the nucleus and cell boundaries, respectively. Each row represents a vertex in the boundary polygon of one cell. The boundary points for each cell appear in clockwise order, and the first and the last points are duplicates to indicate a closed polygon. Both files contain the following columns:

Column NameDescription
cell_idUnique ID of the cell, consisting of a cell prefix and dataset suffix
vertex_xX-coordinate of the boundary point in µm
vertex_yY-coordinate of the boundary point in µm

Nucleus and cell boundary information are also provided in Parquet format (nucleus_boundaries.parquet and cell_boundaries.parquet) to enable faster loading and reading of data.

The transcripts file (transcripts.csv.gz) in gzipped CSV format contains data to evaluate transcript quality and localization. The file contains one row for each decoded transcript, with the following columns:

Column NameDescription
transcript_idUnique ID of the transcript
cell_idUnique ID of the cell, consisting of a cell prefix and dataset suffix
overlaps_nucleusBinary value to indicate if the transcript falls within the segmented nucleus of the cell (1) or not (0)
feature_nameGene or control name
x_locationX location in µm
y_locationY location in µm
z_locationZ location in µm
qvPhred-scaled quality value (Q-Score) estimating the probability of incorrect call
fov_nameName of the field of view (FOV) that the transcript falls within
nucleus_distanceThe distance between the transcript and the cell centroid in µm based on segmentation mask boundaries. Transcripts localized within the nucleus have a distance of 0.0 µm.

Transcript information is also provided in:

The Xenium onboard analysis pipeline outputs a cell-feature matrix (cell_feature_matrix) in three file formats: the Market Exchange Format (MEX), the Hierarchical Data Format (HDF5), and the Zarr format. The matrices only include transcripts that pass the default quality value (Q-Score) threshold of Q20.

Each matrix in the cell_feature_matrix/ folder is stored in the MEX format for sparse matrices. It also contains gzipped TSV files with feature and barcode sequences corresponding to row and column indices respectively. The cell_feature_matrix/features.tsv.gz file contains a list of pre-designed panel genes (and any custom add-on genes), negative controls, unassigned codewords, and deprecated codewords (learn more about control and codeword categories on the Algorithms page).

Column NumberDescription
1Ensembl ID for panel and add-on genes
2Gene name for panel and add-on genes
3Feature type (Gene Expression, Negative Control Codeword, Negative Control Probe, Unassigned Codeword, Deprecated Codeword).

The cell-feature matrix is also provided in:

  • HDF5 format (cell_feature_matrix.h5), a binary format that compresses and accesses data more efficiently than text formats such as MEX and is useful when analyzing large datasets. H5 files are supported in both R and Python.
  • Zipped Zarr format (cell_feature_matrix.zarr.zip). This file can be read by Xenium Explorer. See Overview of Xenium Zarr Output Files for file specifications.

The Xenium onboard analysis pipeline outputs key metrics in text format as metrics_summary.csv. This file contains metrics that are useful for assessing decoding and cell segmentation quality.

The Xenium onboard analysis pipeline outputs an analysis/ directory with subdirectories containing several CSV files, which store the automated secondary analysis results. A subset of these results is used to render the Analysis tab in the Analysis summary file. The subdirectories correspond to:

  • Clustering (clustering/) with graph-based and K-means results. Graph-based clustering (under graphclust) is run once as it does not require a pre-specified number of clusters. K-means (under kmeans) is run for K=2..N where K corresponds to the number clusters, and N=10 by default. Each value of K has its own results directory.
  • Differential Expression (diffexp/) with graph-based and K-means results. Under each of the subdirectories are the differential_expression.csv files, which contain the list of cluster-specific features that are differentially expressed in each cluster relative to all the other clusters.
  • Principal Component Analysis (pca/) which contains a total of five files listing the features used in the dimension reduction i.e., to reduce the feature space. These results are used to perform clustering.
  • UMAP (umap/) contains the Uniform Manifold Approximation and Projection results.

The secondary analysis results are also saved as a zipped Zarr file (analysis.zarr.zip), which can be read by Xenium Explorer for data visualization. See Overview of Xenium Zarr Output Files for file specifications.

The gene_panel.json file is a copy of the gene panel file used in the experiment on the Xenium Analyzer instrument.

The JSON schema contains metadata and payload objects. The payload object contains the following:

ObjectDescription
chemistryVersion of Xenium In Situ assay chemistry (i.e., "v1").
customerCustomer contact information derived from design or 10x cloud if the Xenium Panel Designer was used.
designerWhen and who created the design.
panelInformation about the panel design, including name, ID, and total number of targets.
spec_versionVersion of panel JSON file format.
targetsInformation about each target (gene, control) in the panel, including gene identifiers and gene coverage (also referred to as the number of probe sets). The latter may be useful for assessing per-gene sensitivity.

Python or other tools can be used to parse the JSON file. Here is example Python code to extract gene name, Ensembl ID, and gene coverage information for each gene target in a given panel:

# Import Python libraries # Example with Python v3.12, pandas v2.1.1 import json import pandas as pd # Open JSON file f = open('gene_panel.json') # Edit file name here # Return JSON object as a dictionary data = json.load(f) # Create lists to store extracted information gene = [] ensembl = [] cov = [] # Iterate through the JSON list to extract information for i in data['payload']['targets']: if (i['type']['descriptor'] == "gene"): # Only collect info for genes, not controls gene_name = i['type']['data']['name'] ensembl_id = i['type']['data']['id'] coverage = str(i['info']['gene_coverage']) gene.append(gene_name) ensembl.append(ensembl_id) cov.append(coverage) # Create output CSV file out_df = pd.DataFrame(list(zip(gene, ensembl, cov)), columns=['Gene name', 'Ensembl ID', 'Gene coverage']) out_df.to_csv('my_panel_gene_info.csv', index=False) # Close file f.close()

The following are provided in aux_outputs/ (see release notes for updates):

  • The morphology_fov_locations.json file (previously called fov_locations.json in v1.6 and earlier) contains the field of view (FOV) name, height, width, and XY positions in the space of the region of interest's (ROI) morphology image. This is the same space used to compute transcript and cell locations and the units are in microns. The FOVs have 3,520 rows and 2,960 columns with 128 pixels of overlap on each edge (this may change in future versions of the Xenium platform). The position information is useful for determining where FOV boundaries are to assess transcript deduplication and any FOV edge effects.
  • The overview_scan_fov_locations.json file contains the FOV name, height, width, and approximate XY positions in the space of the overview scan image. This is the space that contains all the ROIs and the units are in pixels. The accuracy of the ROI coordinates have a 5 - 10 µm error. This position information is useful for approximating where multiple ROIs are located on an overview scan image.
  • The per_cycle_channel_images/ directory contains downsampled 2D RNA images (maximum intensity projection) from each cycle and channel. These images may be helpful for troubleshooting analysis summary alerts or unexpected metrics and analysis results.
  • The overview_scan.png is the full-resolution (1672 x 3498 pixels) image of the entire sample on the slide.