bulkrna-trajblend

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.

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Repository: mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills-
Last source activity: April 30, 2026 at 21:29
Detected SKILL.md language: English
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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/

Output Structure

output_directory/
├── report.md
├── result.json
├── figures/
│   ├── trajectory_embedding.png
│   ├── fraction_heatmap.png
│   ├── pseudotime_distribution.png
│   └── bulk_on_trajectory.png
├── tables/
│   ├── cell_fractions.csv
│   ├── pseudotime_estimates.csv
│   └── synthetic_cells.csv
└── reproducibility/
    └── commands.sh

Related Skills

bulkrna-deconvolution — Upstream: cell type fraction estimation
bulkrna-qc — Upstream: count matrix quality control
sc-trajectory — Complementary: single-cell trajectory analysis

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"]

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"]