Bulk RNA-seq

Run differential expression on bulk RNA-seq count matrices with TMM normalization and edgeR-style statistical testing.

Research question

Which genes are differentially expressed between experimental groups, and what are the effect sizes and significance levels after appropriate normalization?

Who this is for

Labs running routine bulk RNA-seq DE without maintaining local edgeR/DESeq2 environments
Cancer center cores processing tumor vs. normal or treatment arm comparisons
Translational teams needing reproducible bulk pipelines with logged parameters

Data requirements

Data	Required	Purpose
Raw or normalized count matrix	Yes	DE input
Sample metadata with group labels	Yes	Contrast definition
Gene identifiers	Yes	Row names compatible with reference annotations
Experimental design / contrasts	Yes	Specify comparison groups

Bulk analysis is available via the analysis API; guided UI workflows for bulk are expanding. Single-cell and spatial modalities have full guided UI coverage today.

Workflow

Upload counts + design → Define contrasts → Bulk DE (API) → Review results → Enrichment → Interpret

Step 1 — Prepare count data

Upload a count matrix with samples as columns and genes as rows. Save sample metadata with condition and batch columns on the study Data page. Define contrasts matching your experimental design.

Supported ingestion paths include CSV/TSV count tables and converted pipeline outputs.

Step 2 — Run bulk differential expression

Dispatch via the analysis API:

curl -X POST http://localhost:8001/run/bulk \
  -H "Content-Type: application/json" \
  -d '{
    "study_id": "your-study-id",
    "dataset_id": "your-dataset-id",
    "contrast": "treated_vs_control",
    "parameters": {}
  }'

Poll job status:

curl http://localhost:8001/jobs/{run_id}

The bulk pipeline applies TMM normalization and edgeR-style differential expression with FDR and log-fold-change filtering.

Step 3 — Review DE results

Examine ranked gene tables with log fold change, p-values, and FDR. Volcano and MA-style visualizations are available through completed run artifacts and the Explore/Analyze views when wired to bulk outputs.

Step 4 — Pathway enrichment

Run enrichment on significant gene lists using the standard ORA pipeline against GO biological process terms.

Step 5 — Interpret and report

Use AI interpretation on completed DE and enrichment runs for plain-language summaries. Export methods text and run manifest from the Runs page for manuscript preparation.

Expected outputs

Normalized expression estimates
Differential expression table with statistics per contrast
Filtered significant gene lists
GO enrichment results for up/down-regulated genes
Run record with pipeline version and parameters

Typical analyses

Analysis	Contrast	Question
Tumor vs. normal	Primary tumor vs. adjacent normal	What genes are dysregulated in tumor tissue?
Treatment response	Post vs. pre treatment	Which pathways shift after drug exposure?
Genotype comparison	Mutant vs. wild-type	What is the transcriptional consequence of the alteration?
Time course	Timepoint B vs. A	How does expression change over the time series?

Planned UI extensions

Guided bulk workflows following the freeCount-inspired module structure (differential analysis, network analysis, set operations, functional analysis) are on the product roadmap. The analysis API is available today for programmatic and core-facility use.

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