Bulk RNA-seq deconvolution with Bulk2Single

What problem does it solves? Extracting single-cell resolution information from bulk RNA-seq data is a key challenge in genomics. This Skill automates the deconvolution process, generating synthetic single-cell profiles and benchmarking them against reference atlases to provide deeper insights.

Core Features & Use Cases

• Cell Fraction Estimation: Accurately estimate cell-type fractions from bulk RNA-seq using integrated TAPE estimators.
• Synthetic Cell Generation: Generate high-quality synthetic single-cell datasets using a beta-VAE model.
• Quality Control & Benchmarking: Filter generated cells and benchmark their cluster proportions and correlations against reference scRNA-seq data.
• Use Case: Transform bulk tumor RNA-seq data into synthetic single-cell profiles, estimate the proportions of different immune and tumor cell types, and then compare these generated cells to a known single-cell atlas for validation.

Quick Start

Deconvolute my bulk RNA-seq data into synthetic single cells using Bulk2Single, then estimate cell fractions and compare generated cells to a reference atlas.