Refinement strategies for Tangram for reliable single-cell to spatial mapping

TL;DR

This paper improves the Tangram tool for mapping single-cell RNA data to spatial locations, making it more reliable and consistent.

Contribution

The paper introduces refinements to Tangram, including gene subset training and regularization, to enhance mapping consistency.

Findings

Mapping quality is influenced by gene expression sparsity, and using an informative gene subset improves results.

Refinements like cell filtering and regularization enhance gene expression prediction and cell mapping.

The improved pipeline and benchmarking framework help in reliable spatial projections and imputation.

Abstract

Single-cell RNA sequencing (scRNA-seq) provides comprehensive gene expression data at a single-cell level but lacks spatial context. In contrast, spatial transcriptomics captures both spatial and transcriptional information but is limited by resolution, sensitivity, or feasibility. No single technology combines both the high spatial resolution and deep transcriptomic profiling at the single-cell level without tradeoffs. Spatial mapping tools that integrate scRNA-seq and spatial transcriptomics data are crucial to bridge this gap. However, we found that Tangram, one of the most prominent spatial mapping tools, provides inconsistent results over repeated runs.

We refine Tangram to achieve more consistent cell mappings and investigate the challenges that arise from data characteristics. We find that the mapping quality depends on the gene expression sparsity. To address this, we (1) train the model on an informative gene subset, (2) apply cell filtering, (3) introduce several forms of regularization, and (4) incorporate neighborhood information. Evaluations on real and simulated mouse datasets demonstrate that this approach improves both gene expression prediction and cell mapping. Consistent cell mapping strengthens the reliability of the projection of cell annotations and features into space, gene imputation, and correction of low-quality measurements. Our pipeline, which includes gene set and hyperparameter selection, can serve as guidance for applying Tangram on other datasets, while our benchmarking framework with data simulation and inconsistency metrics is useful for evaluating other tools or Tangram modifications.

The refinements for Tangram and our benchmarking pipeline are available at https://github.com/daisybio/Tangram_Refinement_Strategies.

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