INSTINCT: Multi-sample integration of spatial chromatin accessibility sequencing data via stochastic domain translation

Table of Contents

• Installation
• Tutorial 1: Analyze MISAR-seq mouse brain (MB) dataset
  • Integrating the four S1 slices from MISAR-seq MB dataset
  • Integrating two vertically adjacent slices from the same developmental stage
  • Cross-sample annotation
  • Gene ontology (GO) analysis
  • Expression enrichment analysis
  • Partitioned heritability analysis
• Tutorial 2: Analyze spatial ATAC-RNA-seq mouse brain (MB) dataset
  • Integrating three slices from spatial ATAC-RNA-seq MB dataset
  • Spatial domain identification and UMAP visualization
  • Find DEGs
• Tutorial 3: Analyze spatial-ATAC-seq mouse embryo (ME) dataset
  • Integrating the three slices from the spatial-ATAC-seq ME dataset
  • Spatial domain identification and UMAP visualization
  • Find cluster-specific peaks across the three slices
  • Motif enrichment analysis
• Tutorial 4: Analyze spatial ATAC mouse embryo (ME) dataset
  • Integrating the three S1 slices from spatial ATAC ME dataset
  • Integrating all six slices from spatial ATAC ME dataset
• Tutorial 5: Analyze 10x Visium dorsolateral prefrontal cortex (DLPFC) dataset
  • Integrating samples form DLPFC dataset
  • Spatial domain identification and UMAP visualization

News

2024-09-05: INSTINCT is released and souce code is available!

INSTINCT is now available on GitHub 2024-09-05

Overview

Recent advances in spatial epigenomic techniques have given rise to spatial assay for transposase-accessible chromatin sequencing (spATAC-seq) data, enabling the characterization of epigenomic heterogeneity and spatial information simultaneously. Integrative analysis of multiple spATAC-seq samples, for which no method has been developed, allows for effective identification and elimination of unwanted non-biological factors within the data, enabling comprehensive exploration of tissue structures and providing a more complete epigenomic landscape that facilitates the discovery of biological implications and aids in the study of regulatory processes. In this article, we present INSTINCT, a method for multi-sample INtegration of Spatial chromaTIN accessibility sequencing data via stochastiC domain Translation. INSTINCT can efficiently handle the high dimensionality of spATAC-seq data and effectively eliminate the complex noise and batch effects of samples from different conditions through a stochastic domain translation procedure. We demonstrate the superiority and robustness of INSTINCT in integrating spATAC-seq data across multiple simulated scenarios and real datasets. Additionally, we highlight the advantages of INSTINCT in spatial domain identification, visualization, spot-type annotation, and various downstream analyses, including expression enrichment analysis and partitioned heritability analysis.

Citation

Yuyao Liu, Zhen Li, Xiaoyang Chen, Xuejian Cui, Zijing Gao and Rui Jiang. “INSTINCT: Multi-sample integration of spatial chromatin accessibility sequencing data via stochastic domain translation.” Preprint at bioRxiv https://doi.org/10.1101/2024.05.26.595944 (2024).