The Xenium Analyzer run is complete and it is time to start the in-depth analysis of your data. This article will provide guidance to aid you in the next steps of your journey as you learn how to analyze your in situ data.

This guide will provide information on resources useful for performing certain tasks, including:

• Data visualization with Xenium Explorer
• Alternative cell segmentation tools
• Analysis with community-developed tools

Examining the quality of your data is always a good place to start in your analysis journey. We have documentation on checking data quality on our support site here.

Xenium Analyzer generates files in open formats that can be opened by Xenium Explorer. It is a desktop application that allows interactive in situ data visualization, including the morphology images, RNA transcripts, and segmented cells from tissue macrostructure to subcellular resolution. You can pinpoint the locations of specific transcripts, examine cell segmentation, import cell typing results, align and import post-Xenium H&E and immunofluorescence images, and compare gene expression profiles and cell type proportions across tissue regions. Download Xenium Explorer here and access all tutorials here. Below are some helpful tutorials:

• Checking Xenium Data Quality
• Post-Xenium H&E and IF Image Alignment in Xenium Explorer
• Selecting and Exporting Multiple Region of Interest
• Saving and Sharing Results

A critical step in Xenium data analysis is the identification of nucleus and cell borders. Cell segmentation is part of the standard Xenium Onboard Analysis pipeline where cell segmentation is inferred based on the images collected during sample processing. For reference, Xenium Analyzer’s cell segmentation algorithm is described in this Overview of Xenium Algorithms. You might be interested in exploring different cell segmentation tools as alternative approaches.

Xenium Ranger analysis pipelines aim to reanalyze Onboard Analysis data to generate a bundle that can be viewed in Xenium Explorer. If you would like to resegment Xenium data using the latest algorithm, you can use Xenium Ranger resegment. Xenium Ranger also allows you to tweak segmentation parameters such as the nuclear expansion distance.

If Xenium cell segmentation is not suitable for your experiment, you might be interested in exploring different cell segmentation tools as alternative approaches. Xenium Ranger import-segmentation enables you to use segmentation results from third-party tools to assign transcripts to cells and produce a Xenium output bundle that can be visualized with Xenium Explorer.

Baysor and Cellpose are two commonly used community-developed cell segmentation tools. Baysor optimizes cell segmentation based on transcriptional composition. It also has the capability to utilize stains such as DAPI as a prior. We have a tutorial here that demonstrates how to use this tool on Xenium Analyzer results. Cellpose uses a generalist algorithm and includes pretrained models for 2D or 3D, nucleus and/or cell segmentation. We have an Analysis Guide tutorial on how to use this tool on Xenium Analyzer results here.

You may wish to perform an analysis that is beyond what is currently supported by the 10x Genomics Xenium Analyzer platform. Luckily there is a robust software developer environment and a rapidly evolving set of community-developed tools that support the analysis of 10x Genomics Xenium Analyzer data. These tools require some programming knowledge, such as R and Python. Here is a short list of popular Xenium-compatible community-developed tools and some of the analyses they support:

Community-developed tools written in R

• Seurat: QC, cropping images and cell boundaries, and unsupervised clustering
• Giotto: add statistics, normalize expression, calculate high variable features, dimension reduction, clustering, and subcellular visualization
• Voyager: QC, spatial autocorrelation of QC metrics, Moran’s I (correlation coefficient, measures how one object is similar to others surrounding it), dimension reduction, differential expression, and local spatial statistics of marker genes

Community-developed tools written in Python

• stLearn: normalization, gridding, permutation test for high co-expression, cell-cell interactions, clustering, and pseudo-time-space spatial trajectory analysis
• Squidpy: QC, dimension reduction, spatial statistics, neighbors enrichment analysis, and compute Moran’s I score
• SpatialData: An open and universal framework for processing spatial omics data. Integrate post-Xenium images via coordinate transformations, integrate multi-omics datasets including Xenium and Visium, and annotate regions of interest.

In addition, there are other community-developed tools for spatial transcriptome analysis that are not Xenium-centric:

Spatial transcriptome analysis tools

• RCTD (R): cell type identification, cell type-specific differential expression analysis, sample aggregation, and batch correction analysis
• MERINGUE (R): encode spatial relationship, identify spatial expression heterogeneity, identify gene expression patterns indicative of cell-cell communication with the capability to accommodate 2D and 3D spatial data
• SCIMAP (Python): build neighborhood graft, identify recurrent cellular neighborhood, cell type calling, adding ROI, and Napari visualization

Image viewers and other data conversion tools

• Fiji (Java): image processing package, and facilitate scientific image analysis
• QuPath (Java): image annotation, auto and manual cell detection and classification, and density maps. QuPath may also be used to convert post-Xenium H&E and IF images for Xenium Explorer compatibility
• Napari (Python): image processing, segmentation, and tracking
• spatialdata_xenium_explorer (Python): a vignette showing bidirectional interoperability capabilities of Xenium Explorer and SpatialData. transfer alignment of post-Xenium images and coordinate selections between Xenium Explorer and SpatialData

Check out our datasets page and publications using 10x Genomics’ Xenium Analyzer.

The first publication using the Xenium Analyzer is an excellent starting point if you are interested in seeing one example of a Xenium Analyzer analysis workflow: High resolution mapping of the tumor microenvironment using integrated single-cell, spatial and in situ analysis. The dataset in this publication is showcased in this web demo: FFPE Human Breast.

Also check out some of our additional preview datasets with Xenium Explorer web demos:

• FFPE Human Lung showcasing multimodal cell segmentation
• FFPE Human Pancreas showcasing alignment with post-Xenium H&E and IF
• Fresh Frozen Mouse Brain showcasing a mouse brain with custom gene groups

The list of tools above is not a comprehensive summary. The landscape of Xenium data analysis is rapidly evolving. New and exciting in situ spatial transcriptome analysis tools, articles, and resources continue to be announced as the field grows. We hope this article has inspired you on your Xenium analysis journey by identifying some sites to see along the way.