Proceedings Presentation: Refinement Strategies for Tangram for Reliable Single-Cell to Spatial Mapping
Confirmed Presenter: Merle Stahl, Data Science in Systems Biology, TUM School of Life Sciences
Track: RegSys: Regulatory and Systems Genomics
Room: 11BC
Format: In person
Moderator(s): Anthony Mathelier
Authors List: Show
- Merle Stahl, Merle Stahl, Data Science in Systems Biology
- Lena J. Straßer, Lena J. Straßer, Data Science in Systems Biology
- Chit Tong Lio, Chit Tong Lio, Data Science in Systems Biology
- Judith Bernett, Judith Bernett, Data Science in Systems Biology
- Richard Röttger, Richard Röttger, Department of Mathematics and Computer Science
- Markus List, Markus List, Data Science in Systems Biology
Presentation Overview:Show
Motivation: 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 trade-offs.
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.
Results: 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.
Availability: The refinements for Tangram and our benchmarking pipeline are available at https://github.
com/daisybio/Tangram_Refinement_Strategies.