pseudobulkdeg
Performs pseudo-bulk differential gene expression analysis using DESeq2 or edgeR. Aggregates single-cell counts to sample-level pseudo-bulk data, then identifies differentially expressed genes between conditions while accounting for biological replicates. Supports complex experimental designs including batch effects, paired samples, and interaction terms.
npx skills add majiayu000/claude-skill-registry --skill pseudobulkdeg-pwwang-immunopipe-2 --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
Implements a workflow to perform pseudo-bulk differential gene expression analysis by aggregating single-cell counts to sample-level pseudo-bulk data, then identifying differentially expressed genes between conditions while accounting for biological replicates. Supports complex experimental designs including batch effects, paired samples, and interaction terms, and automatically runs pathway enrichment analysis on significant markers.
How it works
- Uses a configuration structure to enable the PseudoBulkDEG process and set core parameters.
- Aggregates cells into pseudo-bulk samples according to a specified aggregate_by key and data layer (default counts).
- Chooses a differential expression tool (DESeq2 or edgeR) and builds the design formula based on group_by, ident_1, ident_2, and optional paired_by or mutaters for interactions.
- Supports parallelization via ncores, caching, and optional subset expressions for cell filtering.
- Performs enrichment analysis on significant markers using specified databases and enrichment styles, with optional per-cell-type analysis when each is used.
- Provides a set of example configurations illustrating minimal, batch-corrected, per-cell-type, time-course, and multi-case designs.
When to use it
- For robust differential expression with biological replicates across sample-level comparisons
- When accounting for sample-to-sample variability beyond cell-level analysis
- When performing multiple comparisons across conditions, time points, or treatments
- If batch correction or paired sample designs are needed
- When per-cell-type DE analysis is required
- If publication-quality results and downstream enrichment are desired
What it can touch
- Inputs: SeuratClustering object as the input source (srtobj) via [PseudoBulkDEG.in].
- Processing: PseudoBulkDEG process with configured envs (groups, ident_1/ident_2, aggregation, subset, mutaters, etc.).
- Tools: Differential expression via DESeq2 or edgeR; downstream enrichment via specified dbs; visualization and plots configured under plotting sections.
Caveats
- Requires at least 2-3 biological replicates per condition for reliable design; metadata must include group_by, ident_1, ident_2 levels, and an aggregate_by column that uniquely identifies samples.
- If no significant markers are found, default troubleshooting options are available (adjust sigmarkers, switch tool to edgeR, or relax thresholds).
- Batch effects and paired designs rely on correct metadata (e.g., batch, SubjectID) being present; incorrect naming may lead to inadequate correction.
- Complex designs can require larger sample sizes to maintain power, especially for multi-factor models.
# PseudoBulkDEG Process Configuration ## Purpose Performs pseudo-bulk differential gene expression analysis using DESeq2 or edgeR. Aggregates single-cell counts to sample-level pseudo-bulk data, then identifies differentially expressed genes between conditions while accounting for biological replicates. Supports complex experimental designs including batch effects, paired samples, and interaction terms. Automatically runs pathway enrichment analysis on significant markers. ## When to Use - **Robust differential expression**: When you need statistical rigor with biological replicates - **Sample-level variation**: To account for sample-to-sample variability (not just cell-level) - **Multiple comparisons**: Compare conditions across multiple samples, time points, or treatments - **Batch correction**: Model and adjust for batch effects in differential expression - **Paired samples**: When samples have natural pairing (e.g., same subject across time points) - **Per-cell-type analysis**: Perform DEG separately for each cell type - **Publication-quality results**: DESeq2 is the gold standard for bulk RNA-seq analysis **Contrast with ClusterMarkers**: - `ClusterMarkers`: Cell-level compari
- Purpose
- When to Use
- Configuration Structure
- Process Enablement
- Environment Variables - Core Parameters
- DESeq2 Design Formulas
- Formula Syntax
- Formula Patterns
- Contrast Specification
- Configuration Examples
- Example 1: Minimal Configuration
- Example 2: Disease vs Healthy with Batch Correction
- Example 3: Per Cell Type Analysis
- Example 4: Time Course with Paired Samples
What does the pseudobulkdeg skill do?
Performs pseudo-bulk differential gene expression analysis using DESeq2 or edgeR. Aggregates single-cell counts to sample-level pseudo-bulk data, then identifies differentially expressed genes between conditions while accounting for biological replicates. Supports complex experimental designs including batch effects, paired samples, and interaction terms.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill pseudobulkdeg-pwwang-immunopipe-2 --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.