SCAPE: An AI-Driven Platform for Comprehensive Single-Cell Data Analysis

Abstract

Single-cell RNA sequencing (scRNA-seq) has transformed the study of cellular heterogeneity, but downstream analysis remains fragmented and technically demanding. Current pipelines often lack analytical diversity, require programming expertise, and offer limited integration of advanced methods. To address these challenges, we developed SCAPE, an AI-driven automated and interactive platform that unifies multi-omics, multi-resource, and multi-modal single-cell data analysis. SCAPE provides platform-independent installation, customizable workflows, and integration of R- and Python-based tools. Beyond Seurat and Scanpy, it incorporates modules for transcription factor and pathway inference, pseudotime trajectory reconstruction, spatial transcriptomics deconvolution, and cell-cell communication analysis. To demonstrate SCAPE, we curated a unified atlas of lung cancer progression in human patients and mouse models, spanning primary tumors and metastatic sites. The platform enabled harmonized integration, regulatory program inference, and spatial mapping, revealing conserved epithelial programs that promote metastatic seeding and organ-specific adaptations driven by microenvironments. Collectively, SCAPE offers an accessible and comprehensive framework for single-cell analysis, providing new insights into cancer progression and broad utility across biological systems.