Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
Department of Statistics, Stanford University, Stanford, CA, USA.
Nat Methods. 2024 Mar;21(3):444-454. doi: 10.1038/s41592-024-02184-y. Epub 2024 Feb 12.
Whole-transcriptome spatial profiling of genes at single-cell resolution remains a challenge. To address this limitation, spatial gene expression prediction methods have been developed to infer the spatial expression of unmeasured transcripts, but the quality of these predictions can vary greatly. Here we present Transcript Imputation with Spatial Single-cell Uncertainty Estimation (TISSUE) as a general framework for estimating uncertainty for spatial gene expression predictions and providing uncertainty-aware methods for downstream inference. Leveraging conformal inference, TISSUE provides well-calibrated prediction intervals for predicted expression values across 11 benchmark datasets. Moreover, it consistently reduces the false discovery rate for differential gene expression analysis, improves clustering and visualization of predicted spatial transcriptomics and improves the performance of supervised learning models trained on predicted gene expression profiles. Applying TISSUE to a MERFISH spatial transcriptomics dataset of the adult mouse subventricular zone, we identified subtypes within the neural stem cell lineage and developed subtype-specific regional classifiers.
全转录组单细胞分辨率的基因空间分析仍然是一个挑战。为了解决这一限制，已经开发了空间基因表达预测方法来推断未测量转录本的空间表达，但这些预测的质量可能有很大差异。在这里，我们提出了 Transcript Imputation with Spatial Single-cell Uncertainty Estimation (TISSUE)，作为一种用于估计空间基因表达预测不确定性的通用框架，并提供了不确定性感知的下游推理方法。利用一致推断，TISSUE 为 11 个基准数据集的预测表达值提供了校准良好的预测区间。此外，它还可以一致降低差异基因表达分析的假发现率，改善预测空间转录组学的聚类和可视化，并提高基于预测基因表达谱训练的监督学习模型的性能。将 TISSUE 应用于成年小鼠脑室下区的 MERFISH 空间转录组学数据集，我们在神经干细胞谱系中鉴定出了亚型，并开发了亚型特异性的区域分类器。
