| name | bulkrna-trajblend |
| description | Bulk-to-single-cell trajectory interpolation — uses VAE and GNN to bridge bulk RNA-seq with single-cell reference data, generating synthetic single-cell profiles and embedding bulk samples into developmental trajectories. |
| version | 0.3.0 |
| author | OmicsClaw |
| license | MIT |
| tags | ["bulkrna","trajectory","interpolation","VAE","GNN","deconvolution","single-cell"] |
| requires | ["numpy","pandas","matplotlib","scipy","scikit-learn"] |
| optional_requires | ["torch","anndata"] |
| metadata | {"omicsclaw":{"domain":"bulkrna","emoji":"🔀","trigger_keywords":["trajblend","trajectory","bulk to single cell","interpolation","bulk2single","VAE","deconvolution trajectory"],"allowed_extra_flags":["--n-epochs","--reference"],"legacy_aliases":["bulk-trajblend"],"saves_h5ad":false}} |
Bulk RNA-seq Trajectory Interpolation (BulkTrajBlend-style)
Bridges bulk RNA-seq data with single-cell reference to interpolate missing cell states and embed bulk samples into developmental trajectories. Implements a simplified BulkTrajBlend-inspired approach using variational autoencoders (VAE) for synthetic cell generation and nearest-neighbor trajectory mapping.
Core Capabilities
- Estimate cell type fractions from bulk RNA-seq via deconvolution
- Generate synthetic single-cell profiles weighted by estimated fractions (VAE-inspired)
- Map bulk samples onto scRNA-seq trajectory embedding (PCA/UMAP)
- Pseudotime estimation for bulk samples based on nearest reference cells
- Visualization: trajectory plots with bulk-injected positions, fraction heatmaps
Why This Exists
- Without it: Bulk RNA-seq data cannot be placed on developmental trajectories — users must generate new scRNA-seq data.
- With it: Existing bulk datasets gain trajectory context by leveraging available single-cell references.
- Reference: Inspired by
BulkTrajBlend (omicverse), Bulk2Single, and related deconvolution-trajectory methods.
Algorithm / Methodology
Cell Fraction Estimation
- NNLS-based deconvolution against scRNA-seq reference signatures
- Alternative: pre-computed fractions from external tools (CIBERSORTx, etc.)
Synthetic Cell Generation
- Weighted sampling of reference scRNA-seq cells according to estimated fractions
- Optional Gaussian noise injection to model biological variability
- Fractions serve as mixing weights for trajectory interpolation
Trajectory Mapping
- Bulk samples projected onto reference PCA/UMAP embedding
- K-nearest reference cells used to estimate pseudotime
- Confidence intervals based on neighbor pseudotime variance
Input Formats
| Format | Extension | Description |
|---|
| Bulk expression | .csv, .tsv | Genes × samples count matrix |
| scRNA-seq reference | .h5ad, .csv | AnnData or matrix with cell type labels |
CLI Reference
python omicsclaw.py run bulkrna-trajblend --demo
python omicsclaw.py run bulkrna-trajblend --input bulk_counts.csv \
--reference scref.h5ad --output results/
