# A joint model of unpaired data from scRNA-seq and spatial transcriptomics for imputing missing gene expression measurements

arXiv: https://arxiv.org/abs/1905.02269 · HTML: https://mlsanity.com/paper/1905.02269/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 May 2019.

## Built on by (most cited first, 10 of 10)

- [Single Cells Are Spatial Tokens: Transformers for Spatial Transcriptomic Data Imputation](https://mlsanity.com/paper/2302.03038/) (2023)
- [stMCDI: Masked Conditional Diffusion Model with Graph Neural Network for Spatial Transcriptomics Data Imputation](https://mlsanity.com/paper/2403.10863/) (2024)
- [PH2ST:ST-Prompt Guided Histological Hypergraph Learning for Spatial Gene Expression Prediction](https://mlsanity.com/paper/2503.16816/) (2025)
- [Completing Spatial Transcriptomics Data for Gene Expression Prediction Benchmarking](https://mlsanity.com/paper/2505.02980/) (2025)
- [Nonnegative spatial factorization](https://mlsanity.com/paper/2110.06122/) (2021)
- [Emerging Artificial Intelligence Applications in Spatial Transcriptomics Analysis](https://mlsanity.com/paper/2203.09664/) (2022)
- [Benchmarking LLM-based agents for single-cell omics analysis](https://mlsanity.com/paper/2508.13201/) (2025)
- [Distance-Preserving Representations for Genomic Spatial Reconstruction](https://mlsanity.com/paper/2408.00911/) (2024)
- [Unleashing the power of computational insights in revealing the complexity of biological systems in the new era of spatial multi-omics](https://mlsanity.com/paper/2509.13376/) (2025)
- [CASPER: Cross-modal Alignment of Spatial and single-cell Profiles for Expression Recovery](https://mlsanity.com/paper/2511.15139/) (2025)

## Abstract (arXiv)

Spatial studies of transcriptome provide biologists with gene expression maps of heterogeneous and complex tissues. However, most experimental protocols for spatial transcriptomics suffer from the need to select beforehand a small fraction of genes to be quantified over the entire transcriptome. Standard single-cell RNA sequencing (scRNA-seq) is more prevalent, easier to implement and can in principle capture any gene but cannot recover the spatial location of the cells. In this manuscript, we focus on the problem of imputation of missing genes in spatial transcriptomic data based on (unpaired) standard scRNA-seq data from the same biological tissue. Building upon domain adaptation work, we propose gimVI, a deep generative model for the integration of spatial transcriptomic data and scRNA-seq data that can be used to impute missing genes. After describing our generative model and an inference procedure for it, we compare gimVI to alternative methods from computational biology or domain adaptation on real datasets and outperform Seurat Anchors, Liger and CORAL to impute held-out genes.