Welcome to Xenium_Benchmarking documentation!

This is a ReadTheDoc for public repository to reproduce the analysis presented in the study available here:

Marco Salas et al. Optimizing Xenium In Situ data utility by quality assessment and best practice analysis workflows , 2024.

Abstract of the study

The Xenium In Situ platform is a new spatial transcriptomics product commercialized by 10X Genomics capable of mapping hundreds of genes in situ at a subcellular resolution. Given the multitude of commercially available spatial transcriptomics technologies, recommendations in choice of platform and analysis guidelines are increasingly important. Herein, we explore 25 Xenium datasets generated from multiple tissues and species comparing scalability, resolution, data quality, capacities and limitations with eight other spatially resolved transcriptomics technologies and commercial platforms. In addition, we benchmark the performance of multiple open source computational tools, when applied to Xenium datasets, in tasks including preprocessing, cell segmentation, selection of spatially variable features and domain identification. This study serves as the first independent analysis of the performance of Xenium, and provides best practices and recommendations for analysis of such datasets.

Contents:

• xb package
  • xb.neighborhood module
  • xb.plotting module
  • xb.calculating module
  • xb.comparing module
  • xb.domain_identification module
  • xb.formatting module
  • xb.preprocessing module
  • xb.simulating module
  • xb.Spage_main module
• END-TO-END PIPELINE
  • Purpose of the notebook
  • Import packages
  • 0. Defining input parameters
  • 1. Format Xenium output to AnnData
  • 2. Resegmentation using Baysor (Alternative to #1)
  • 3. Preprocess and cluster cells AnnData
  • 4. Domain identification (use any of the following algorithms in 4.1-3)
  • 5 Imputation using SpaGE
  • 6.Identifying Spatially variabile genes (SVF) by Squidpy’s Moran I
  • 7. Defining neighborhood enrichment of celltypes using Squidpy