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bulkrna-trajblend

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Load when placing bulk RNA-seq samples on a single-cell reference's pseudotime axis (NNLS deconvolution + nearest-neighbour mapping). Skip for plain cell-type proportions (use bulkrna-deconvolution alone) or for native single-cell trajectory inference (use sc-pseudotime).

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bulkrna-trajblend

When to use

Run when you have a bulk RNA-seq cohort and a single-cell reference with pre-computed pseudotime, and you want each bulk sample placed on that pseudotime axis. The current implementation is a sklearn-based NNLS-plus-nearest-neighbour pipeline (PCA → kNN against ref); it does not use VAE / GNN despite the skill name's connotation.

Inputs & Outputs

InputFormatRequired
Bulk count matrix.csv / .tsv (gene × sample)yes (or --demo)
sc reference--reference .h5ad or .csv (must include pseudotime in obs for h5ad)yes (or --demo)
--n-epochsintdefault 50 — currently a no-op (the script's argparse help text already says "unused in fallback"); kept for forward-compat with a future training path
OutputPathNotes
Cell-type fractionstables/cell_fractions.csvsample × cell_type proportions (NNLS weights)
Pseudotime estimatestables/pseudotime_estimates.csvper sample: pseudotime mean / std / mean_neighbor_dist / pc1 / pc2
Figuresfigures/*.pngtrajectory projection on reference UMAP/PCA
Reportreport.md + result.jsonalways

Flow

  1. Load bulk counts + sc reference.
  2. Find common genes between bulk and reference (bulkrna_trajblend.py:117 raises ValueError if < 50 genes overlap).
  3. Run NNLS deconvolution to estimate per-sample cell-type fractions.
  4. Project bulk samples into the reference's PCA space; use sklearn.neighbors.NearestNeighbors to find each bulk sample's k nearest reference cells.
  5. Estimate per-sample pseudotime as mean (and std) of the neighbour set's reference pseudotime values.
  6. Render trajectory figures; emit fractions + pseudotime tables.

Gotchas

  • Gene-namespace mismatch hard-fails at 50. bulkrna_trajblend.py:117 raises if fewer than 50 gene IDs overlap between bulk and reference. Most common cause: bulk uses Ensembl IDs while sc reference uses HGNC symbols. Pre-run bulkrna-geneid-mapping to harmonise.
  • --n-epochs is currently a no-op. The argparse help text at :354 reads "VAE epochs (unused in fallback)" — there is no VAE / GNN code path in this version (the script imports only sklearn, numpy, pandas). The flag is preserved as a forward-compat hook; passing any value has no effect on output. Do not report results as "VAE+GNN-derived" until that code lands.
  • Pseudotime placement is a kNN average, not a likelihood-based fit. Each bulk sample's pseudotime is the mean of its k nearest reference cells' pseudotimes — it doesn't carry uncertainty in the way a probabilistic model would. Use pseudotime_std and mean_neighbor_dist (per bulkrna_trajblend.py:184-188) as crude confidence proxies; large neighbour distances mean the bulk sample doesn't cleanly resemble any reference cell.
  • Reference pseudotime values must be supplied externally. This skill consumes pseudotime; it does not compute it. Run sc-pseudotime on the reference first (or use a published pre-pseudotimed reference) so the input AnnData has the relevant obs column.

Key CLI

python omicsclaw.py run bulkrna-trajblend --demo
python omicsclaw.py run bulkrna-trajblend \
  --input bulk_counts.csv --reference scref.h5ad --output results/

See also

  • references/parameters.md — every CLI flag and tuning hint
  • references/methodology.md — NNLS deconvolution + kNN pseudotime mapping; the future-training path the --n-epochs flag anticipates
  • references/output_contract.md — exact output directory layout
  • Adjacent skills: bulkrna-deconvolution (parallel: same NNLS proportions, no pseudotime placement), bulkrna-geneid-mapping (run upstream to harmonise gene IDs), sc-pseudotime (run upstream on the reference to populate the pseudotime column this skill consumes)