Image processing begins by mapping known physical features on the glass slide surface (fiducials) to set the image coordinate system. The instrument then identifies the upper bound of the tissue section to find the location of the first Z-slice. These pieces of information collectively determine the range of Z-slices to image. This process has been robust to all tested sections and tissue types.

<LinkableSection id={"dapi"} title={"3D DAPI image"}>

The DAPI image process produces a complete 3D morphology image (the `morphology.ome.tif` output file) for each of the stained regions and determines the reference image volume that cells are segmented from and decoded transcripts are assigned to.

First, the lens distortion in internal sensor data is corrected. This is done computationally based on instrument calibration data, which are collected in order to characterize the optical system and are saved on-instrument. Next, the Z-stacks from internal sensor data (0.75 &micro;m step size) are further subsampled to a 3 μm step size. This subsampling step size was determined empirically to be a useful resolution for [cell segmentation](/support/software/xenium-onboard-analysis/3.1/algorithms-overview/segmentation#seg) quality.

Shared image features are then extracted from the regions where FOVs overlap. Feature matching is performed to estimate the offsets between adjoining FOVs. The offsets are used to ensure consistent alignment across the image (global alignment).

Finally, the 3D DAPI image volumes (Z-stacks) generated across FOVs are blended together to construct a stitched volume that is used for nucleus segmentation.

</LinkableSection>

<LinkableSection id={"focus-imgs"} title={"2D projection images"}>

The DAPI and multi-tissue stain image processes produce 2D projections for each image stain (output in the `morphology_focus/` directory). The DAPI focus image is based on the DAPI image captured in cycle 1. The multi-tissue stain images are captured in later cycles as described below.
</LinkableSection>

<LinkableSection id={"focus-multi-tissue"} title={"Multi-tissue stain image acquisition"} nested={true}>

_This algorithm step is only compatible for tissues prepared with the Multi-Tissue Stain Mix following the Cell Segmentation Staining workflow (see [CG000749](/support/in-situ-gene-expression/documentation/steps/assay/xenium-in-situ-gene-expression-with-morphology-based-cell-segmentation-staining-user-guide) or [CG000760](/support/in-situ-gene-expression/documentation/steps/assay/xenium-prime-in-situ-gene-expression-with-optional-cell-segmentation-staining-user-guide))._

Two cycles are added to the on-instrument analysis workflow to image the cell membrane boundary stains and interior cell stains. These occur after the RNA imaging cycles.

- First added cycle captures DAPI and the other three channels in a blank state.
- Second added cycle captures DAPI and the other three channels with their respective stains present.

The DAPI images from these additional two cycles are then aligned to the cycle 1 DAPI image so they are in the same coordinate system. Next, the background "blank" images are subtracted from the stain images for each of the multi-tissue stain channels, which helps to offset the effects of autofluorescence. Background subtraction is only performed for the multi-tissue stain images, not the DAPI image.

In XOA v3.0, background thumbnail images are displayed in the `analysis_summary.html` Image QC gallery. Downsampled images are available in the `aux_outputs/` directory.

</LinkableSection>

<LinkableSection id={"focus-processing"} title={"Focus image processing"} nested={true}>

This algorithm step is based on multi-focus image fusion techniques\* to generate an all-in-focus stitched, 2D projection for each of the stain images (boundary, interior RNA, interior protein; and/or DAPI). It includes the following steps:

- In 64 x 64 pixel patches, the algorithm selects the best in-focus Z-slice from the stain image Z-stack, which has a 0.75 &micro;m step size. This process is applied to every FOV selected for analysis.
- The results are stitched together to generate a 2D projection image for each stain. These are provided in the Xenium Onboard Analysis output bundle as multi-file OME-TIFF files, one per channel, in the `morphology_focus/` directory. It will contain the nuclei DAPI stain image, as well as three additional stain images for Xenium outputs generated with the multimodal cell segmentation assay workflow.

\*Review articles such as this [one](/support/software/xenium-onboard-analysis/3.1/algorithms-overview/image-processing#references) provide some background information about multi-focus image fusion methods.

</LinkableSection>

<LinkableSection id={"rca"} title={"RCA product image processing"}>

The goal of RCA product image processing is to detect and filter puncta and correct distortion. Performed for every channel and cycle, the 3D image volumes (Z-stacks) obtained for each FOV are processed to detect the puncta in 3D space that correspond to labeled RCA products. Images are currently captured in four color channels and n cycles depending on the assay.

The RNA fluorescence image is scanned for punctum signals that stand out from the local background. The XYZ coordinates of each punctum are refined by examining local brightness. The signal intensity of the punctum is determined based on a fitted shape. Xenium Analyzer instrument-specific calibrations are made to adjust the optical system’s point spread function (PSF) model. This calibration enables increased detection and localization of puncta in transcript-dense regions and near the edges of FOVs, effectively minimizing FOV-related artifacts like roll-off.

Next, the pipeline filters out puncta that are unlikely to be true transcripts (non-punctate or low quality signals). Similar to DAPI images, curvature distortion is corrected.

<Image src={"https://cdn.10xgenomics.com/image/upload/v1692908783/software-support/Xenium-onboard-analysis/RNAimage.png"} />

</LinkableSection>

<LinkableSection id={"references"} title={"References"}>

- Zafar R, Farid MS, and Khan MH. [Multi-Focus Image Fusion: Algorithms, Evaluation, and a Library](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8321074). J Imaging. 6:60, 2020.

</LinkableSection>

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Image Processing

Image Processing Algorithms

The goal of cell segmentation is to approximate boundaries between cells so that transcripts can be assigned to cells. Downstream, these results will be used to produce a cell-feature matrix, similar to those output by existing single cell and spatial technologies.

These segmentation algorithms are supported for the following assays depending on whether Cell Segmentation Staining is used (algorithms apply for both Xenium v1 and Xenium Prime chemistries):

| Xenium In Situ assay | Nucleus segmentation | Cell segmentation |
| -------------------------------------------- | -------------------- | ----------------- |
| Gene Expression + Cell Segmentation Staining | DAPI-based | Multimodal |
| Gene Expression | DAPI-based | Nuclear expansion |

<LinkableSection id={"seg-nucleus"} title={"Nucleus segmentation"}>

The first step is to detect the locations of nuclei using the DAPI images and a custom neural network for nucleus segmentation. The neural network is trained on thousands of manually labeled image patches covering multiple tissue types. Any nucleus that has 95% or more of its pixel intensity lower than an intensity threshold of 100 photoelectrons will be removed.

</LinkableSection>

<LinkableSection id={"seg"} title={"Cell segmentation"}>
</LinkableSection>

<LinkableSection id={"seg-multimodal"} title={"Multimodal"} nested={true}>

_This algorithm step is only compatible for tissues prepared with the Multi-Tissue Stain Mix following the Cell Segmentation Staining workflow (see [CG000749](/support/in-situ-gene-expression/documentation/steps/assay/xenium-in-situ-gene-expression-with-morphology-based-cell-segmentation-staining-user-guide) or [CG000760](/support/in-situ-gene-expression/documentation/steps/assay/xenium-prime-in-situ-gene-expression-with-optional-cell-segmentation-staining-user-guide))._ Read the [Xenium In Situ Multimodal Cell Segmentation: Workflow and Data Highlights Technical Note](/support/in-situ-gene-expression/documentation/steps/assay/cell-segmentation-tech-note) for more details.

The multimodal cell segmentation algorithm uses custom deep learning models to analyze the multi-channel stain images. The models are trained on 10x Genomics Xenium In Situ data, which include a range of [tissue types](/support/in-situ-gene-expression/documentation/steps/experimental-design-and-planning/multimodal-cell-segmentation-validated-tissue-list) and preparations (fresh frozen, FFPE).

After nucleus segmentation with DAPI, the algorithm uses three methods to segment cells.

<Image src={"https://cdn.10xgenomics.com/image/upload/v1709342519/software-support/Xenium-onboard-analysis/multimodal_seg_stains.jpg"} />
 

The segmentation results are prioritized in this order for each cell:

- **Segment cells based on their cell boundary stain:** The inferred segmentation from this method should be closest to the true cell membrane boundary. It uses cell-surface marker antibodies to target epithelial markers (ATP1A1, E-Cadherin) and immune markers (pan-lymphocyte: CD45). This method can split nuclei, define cells missing a nucleus, and identify multinucleate cells. Nuclei that overlap with anucleate cells are assigned to the cell.
- **Segment cells based on expansion from the nucleus to the cell interior stain edge:** This method includes both a deep learning model and a nuclear expansion method using the interior stain to infer cell boundaries. It uses the interior stain (18S rRNA marker) and the DAPI stain for nuclei. The XOA v3.0 cell segmentation algorithm does not currently use the interior protein stain (alphaSMA/Vimentin), as the 18S marker has been sufficient for most tissue types. The inferred cell outline may look irregular in cases where boundaries between cell interior stains are challenging to identify (i.e., for dense tissue types).
- **Nuclear expansion:** For cases where cells that do not have boundary or interior stains, segment cells with a nuclear (DAPI) expansion distance of 5 &micro;m or until another cell boundary is encountered (described more [below](/support/software/xenium-onboard-analysis/3.1/algorithms-overview/segmentation#seg-nucleus-expansion)).

The `analysis_summary.html` and `metrics_summary.csv` outputs will provide metrics for the proportion of cells segmented with each method (described [here](/support/software/xenium-onboard-analysis/3.1/analysis/xoa-output-analysis-summary#seg-metrics)).

</LinkableSection>

<LinkableSection id={"seg-nucleus-expansion"} title={"Nuclear expansion"} nested={true}>

_This algorithm step is used in DAPI-only workflows or for cells that do not have boundary or interior stains (see algorithm order in section above)._

Once the locations of nuclei in the sample have been identified by the model, a heuristic cell boundary expansion step is performed. The nucleus boundaries are expanded by 5 &micro;m or until they encounter another cell boundary in X-Y. If cell boundaries overlap during expansion, they are resolved using an algorithm that is conceptually similar to Voronoi tessellation.

<SupportCallout level="info">
<Text as={"span"}>In XOA v1.0 - 1.9, the default expansion distance was 15 µm. It is reduced to 5 µm in v2.0 and later.</Text>
</SupportCallout>
 

</LinkableSection>

<LinkableSection id={"seg-mask-polygon"} title={"Segmentation mask and polygon outputs"}>

Xenium cell segmentation takes into account the 3D output from the [DAPI image processing step](/support/software/xenium-onboard-analysis/3.1/algorithms-overview/image-processing#dapi) for all Z-slices for better accuracy, as well as the 2D focus image outputs if the multimodal cell segmentation workflow is used. The pipeline ultimately produces a flattened 2D segmentation mask for ease of use.

The nuclear boundaries are consolidated to form non-overlapping 2D objects when projected in X-Y. Since the segmentation mask is 2D, transcripts are assigned to 2D shapes based on their X and Y coordinates.

Nucleus and cell boundary polygons are also provided in the outputs. They are approximations of the segmentation masks and are used for efficient visualization of nucleus and cell segmentation in Xenium Explorer and other analysis software. Cell and nucleus mask and polygon data are stored in the `cells.zarr.zip` output file.

You can also use XOA outputs to explore other segmentation methods developed in the community. Analysis Guides on this topic can be found on the [10x website](/analysis-guides). These third-party segmentation results can be viewed in Xenium Explorer with updated XOA metrics and outputs using [Xenium Ranger's import-segmentation pipeline](/support/software/xenium-ranger/latest).

</LinkableSection>

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Segmentation

Nucleus and Cell Segmentation Algorithms

This page describes the algorithms underlying the processing and analysis steps that occur on the Xenium Analyzer.

<LinkableSection id={"overview-workflow"} title={"Workflow overview"}>

The Xenium workflow begins with sample preparation. Fresh frozen (FF) or formalin-fixed paraffin-embedded (FFPE) samples are mounted on Xenium slides. The samples are fixed and permeabilized (FF samples) or deparaffinized and decrosslinked (FFPE samples). Then, probe hybridization, ligation, and rolling circle amplification (RCA) are performed following either the Xenium In Situ or Xenium Prime In Situ Gene Expression assay user guides.

Once the sample has been prepared, imaging is performed in cycles on the Xenium Analyzer. During each cycle, fluorescently labeled probes for detecting RNA target sequences and other reagents are automatically cycled in, imaged, and removed. The internal image sensor captures data across multiple Z-planes with a 0.75 &micro;m step size across the entire tissue thickness for every field of view (FOV) in the user-selected region (see Region Selection Guidelines in the [Xenium Analyzer instrument user guide](/support/in-situ-gene-expression/documentation/instruments/xenium-analyzer/xenium-analyzer-user-guide)). Image data are captured for multiple fluorescence channels in every cycle, which are processed and stitched to build a spatial map of the transcripts across the selected tissue section.

<Image src={"https://cdn.10xgenomics.com/image/upload/v1717125490/software-support/Xenium-onboard-analysis/InSitu_workflow_combined.png"} />
 

Thus, over the course of a run, the Xenium Analyzer’s internal image sensor collects 3D volumes across: **1) multiple FOVs, 2) multiple fluorescence channels, and 3) multiple cycles of chemistry and imaging.** This produces terabytes of internal sensor data that are processed efficiently and analyzed across all cycle-channels to decode transcripts. Once transcripts are decoded, downstream analysis and visualization of Xenium raw data output &mdash; the spatial map of transcripts &mdash; can proceed.

</LinkableSection>

<LinkableSection id={"overview-pipe"} title={"Onboard analysis pipeline"}>

The Xenium Onboard Analysis (XOA) pipeline supports the analysis of Xenium In Situ Gene Expression data. The **image processing** and **segmentation** steps of the XOA pipeline depend on the assay workflow used to prepare samples. The final steps of both pipelines include **RCA product image registration, decoding, deduplication, secondary analysis**, and generation of **Xenium Onboard Analysis output data**. Quality scores (Q-Scores) are estimated using controls for calibration (see [Controls](/support/software/xenium-onboard-analysis/3.1/algorithms-overview/xoa-algorithms#qvs)).

The high-level pipeline steps for each workflow are shown in the diagrams below:

- (**Left**) Following the _Xenium In Situ Gene Expression - Probe Hybridization, Ligation & Amplification User Guide_
 ([CG000582](/support/in-situ-gene-expression/documentation/steps/assay/xenium-in-situ-gene-expression-probe-hybridization-ligation-amplification))
- (**Right**) Following the _Xenium In Situ Gene Expression with Cell Segmentation Staining User Guide_ ([CG000749](/support/in-situ-gene-expression/documentation/steps/assay/xenium-in-situ-gene-expression-with-morphology-based-cell-segmentation-staining-user-guide)) or _Xenium Prime In Situ Gene Expression with optional Cell Segmentation Staining User Guide_ ([CG000760](/support/in-situ-gene-expression/documentation/steps/assay/xenium-prime-in-situ-gene-expression-with-optional-cell-segmentation-staining-user-guide))

<Image src={"https://cdn.10xgenomics.com/image/upload/v1710275988/software-support/Xenium-onboard-analysis/algo_comparisons.png"} />
 

</LinkableSection>

<LinkableSection id={"image-processing"} title={"Image processing"}>

The image processing steps performed by the pipeline across cycles include acquisition of:

- **DAPI images:** DAPI (Xenium Nuclei Staining Buffer) is a blue fluorescent DNA stain for visualizing nuclear DNA in fresh and fixed cells. In the Xenium workflow, DAPI staining is used to locate nuclei, inform cell segmentation, and produce a 3D tissue morphology image. DAPI images are captured across all FOVs in the first cycle.
- **Multi-tissue stain images:** The Xenium Multi-Tissue Stain Mix contains stains for visualizing the cell membrane and cytoplasmic regions of cells. The boundary and interior RNA stains, in addition to the DAPI nuclei stain, are used to inform cell segmentation for the multimodal cell segmentation assay workflow.
- **RCA product images:** For each cycle, images are captured in multiple color channels across all FOVs. Punctate fluorescent signals (puncta) are detected and filtered, and image distortion is corrected.

Click the link below to read about the algorithms used to process these images.

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</LinkableSection>

<LinkableSection id={"seg"} title={"Nucleus and cell segmentation"}>

Nucleus segmentation occurs between cycles, while cell segmentation occurs after the imaging cycles have completed.

- In the _Xenium In Situ Gene Expression_ assay, DAPI images are used to infer nucleus boundaries using a machine learning algorithm; cell boundaries are then inferred by isotropic nuclear expansion.
- In the _Xenium In Situ Gene Expression with Cell Segmentation Staining_ and _Xenium Prime In Situ Gene Expression with optional Cell Segmentation Staining_ assays, multi-tissue stains and multimodal cell segmentation methods are used to determine cell boundaries.

Click the link below to read about the algorithms used to segment nuclei and cells.

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</LinkableSection>

<LinkableSection id={"registration"} title={"RCA product image registration"}>

For each FOV, puncta across channels and cycles must be aligned to reduce differences in image offset, rotation, and magnification. This is important for accurate transcript decoding. The localized puncta from each channel and cycle are registered so that puncta corresponding to the same original RNA molecule are aligned tightly in 3D. Nonlinear transformations are fitted such that all the puncta are aligned to the reference morphology image.

</LinkableSection>

<LinkableSection id={"decode"} title={"Transcript decoding"}>

In order to proceed from puncta to transcripts, decoding must be performed. The Xenium codebook contains a collection of codewords that are assigned to genes in a given [gene panel](/support/in-situ-gene-expression/documentation/steps/panel-design). The pipeline uses the `gene_panel.json` to specify a given gene name to an indexed codeword. Each codeword is defined based on a pattern of fluorescent signal intensities recorded across channels and cycles (see diagram on [panel concepts and terms page](/support/software/xenium-panel-designer/latest/tutorials/xenium-panel-design-concepts-terms#codewords)). Some codewords are reserved for negative controls.

When selecting the codeword assigned to each gene, the Xenium panel design process attempts to keep the predicted transcript density in each cycle-channel in each known cell type as uniform as possible, using single cell reference cell type data. In particular, high expression genes that are coexpressed in the same cell type will be assigned codewords in different cycle-channels as much as possible (more details on the [Getting Started with Xenium Panels](/support/software/xenium-panel-designer/latest/tutorials/create-single-cell-reference) page).

The fluorescent signals from all channels and cycles are compared to the codebook using a global (across all FOVs) maximum likelihood approach based on probabilistic modeling. The decoding process itself does not make per cycle-channel calls. Instead, it performs a filtering step to find codewords that are plausible matches for the puncta signal trace. It assigns probabilities to each candidate codeword based on all the intensity information at the spatial location and chooses the most probable. This approach considers attributes such as puncta location, their color and cycle of detections, and signal intensities. Each transcript is imaged in a bright state five times across all cycle-channels (n cycles x 4 channels).

</LinkableSection>

<LinkableSection id={"qvs"} title={"Quality scores and controls"} nested={true}>

**The cell-feature matrix and Xenium Analyzer’s secondary analyses only include transcripts with a Q-Score ≥ 20.** Final Q-Scores are reported in the [transcript data output files](/support/software/xenium-onboard-analysis/3.1/analysis/xoa-output-understanding-outputs#transcript-file).

A [Phred-style calibrated quality score](https://en.wikipedia.org/wiki/Phred_quality_score) is assigned to each decoded transcript to signify the confidence in the decoded transcript identity. The quality score is derived from the likelihood of the maximum likelihood codeword (i.e., the codeword that best explains the observed data), compared to the likelihood of other sub-optimal codewords. This yields a raw Q-Score. Codewords are then mapped to targets using the gene panel information.

Final Q-Scores are obtained first by putting the full range of raw Q-Scores into bins. Then, the raw Q-Scores in each bin are calibrated by the proportion of "Negative Control Codewords" in the bin. A final Q-Score is assigned to each raw Q-Score bin, to ensure that Q-Scores in each bin are correctly calibrated. Control codewords are built into the process to ensure that the final Q-Scores are accurately calibrated.

- **Negative control codewords** are codewords in the codebook that do not have any probes matching that code. They are chosen to meet the same requirements as regular codewords and can be used to assess the specificity of the decoding algorithm.
- **Negative control probes** are probes that exist in the panels but target non-biological sequences. They can be used to assess the specificity of the assay.
- **Genomic control probes** are probes that are designed to bind to intergenic genomic DNA but not to any transcript sequence present in the tissue. They are present in the Xenium Prime assay.
- **Unassigned codewords** are unused codewords. There is no probe in a particular gene panel that will generate the codeword.
- The **Deprecated codewords** category is assigned to codewords that are not used in the Xenium Onboard Analysis pipeline.

</LinkableSection>

<LinkableSection id={"dedup"} title={"Deduplication"}>

After decoding, the results are combined from all FOVs. Duplicate decoded transcripts in overlapping regions between adjoining FOVs are reconciled into one transcript, based on a nearest neighbor analysis that considers transcript identity and Q-Score. The deduplicated transcripts are assigned global coordinates based on the reference morphology image obtained from the [DAPI image processing step](/support/software/xenium-onboard-analysis/3.1/algorithms-overview/image-processing#dapi) (`morphology.ome.tif`).

Decoded and deduplicated transcripts in 3D with Q-Scores ≥ 20 are assigned to directly overlapping segmented cells to produce a cell-feature matrix. This matrix can be analyzed using conventional and novel single cell and spatial analysis approaches, facilitating [integration](/analysis-guides/continuing-your-journey-after-xenium-analyzer) with existing single cell and spatial datasets.

</LinkableSection>

<LinkableSection id={"secondary-analysis"} title={"Secondary analysis"}>

Xenium’s onboard analysis uses the same algorithms as [Cell Ranger for single cell gene expression](/support/software/cell-ranger/latest/algorithms-overview/cr-gex-algorithm#secondary-analysis) for performing secondary analysis on the cell-feature matrix (PCA, UMAP, graph-based and K-means clustering, differential expression analysis). t-SNE projection is not supported in Xenium onboard analysis.

</LinkableSection>

Xenium Algorithms

An introduction to the algorithms for the Xenium In Situ Gene Expression product.

Overview of Xenium Algorithms

Overview

Getting Started

Analysis

Algorithms

Example Datasets

Release Notes

Advanced

Xenium Analyzer

Xenium Explorer

Xenium Ranger

Analysis Guides

Xenium Onboard Analysis 3.1

Xenium Onboard Analysis

A table of Xenium onboard analysis pipeline output files.

Outputs at a Glance

Overview of all Xenium onboard analysis pipeline output files.

Understanding Xenium Outputs

Overview of the interactive Xenium analysis summary output file metrics and plots.

Analysis Summary Overview

Troubleshooting guide for the Xenium analysis summary alerts.

Analysis Summary Alerts

Storing Xenium raw data for reanalysis or grant requirements.

Archiving Xenium Data

The Xenium In Situ software suite is a set of software applications for analyzing and visualizing in situ gene expression data produced by the Xenium Analyzer. The Xenium Onboard Analysis pipeline enables simultaneous on-instrument data collection and analysis.

What is Xenium Onboard Analysis?

Learn about Xenium Onboard Analysis outputs, algorithms, and software compatibility.

Getting Started with Xenium Onboard Analysis

New to Xenium Onboard Analysis?

View the latest updates to Xenium Onboard Analysis algorithms.

View the latest updates to Xenium Onboard Analysis.

Powerful in situ profiling platform using curated panels, powerful visualization software, and easy-to-follow workflow.

Visualize data from Xenium In Situ solutions.

Reanalyze data from Xenium In Situ solutions.

Browse Q&As from the 10x Genomics support team.

A collection of introductions, tutorials, and blogs for data analysis beyond 10x Genomics software.