# Bloodgen3module

> BloodGen3Module

- Skill: `biomate-ai/bloodgen3module` (Agent Skill)
- Install (CLI): `npx skillmds@latest add biomate-ai/bloodgen3module`
- Raw SKILL.md: https://api.skillmd.com/api/skills/biomate-ai/bloodgen3module/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: biomate-ai (https://skillmd.com/u/biomate-ai)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/biomate-ai/bloodgen3module

---


# BloodGen3Module

## Workflows

### Standard Workflow

```r
library(BloodGen3Module)
library(ExperimentHub)
library(SummarizedExperiment)

# 1. Load expression data from ExperimentHub
dat <- ExperimentHub()
res <- query(dat, "GSE13015")
GSE13015 <- res[["EH5429"]]

# 2. Perform group comparison analysis using t-test
Group_df <- Groupcomparison(
  GSE13015,
  sample_info = NULL,
  FC = 1.5,
  pval = 0.1,
  FDR = TRUE,
  Group_column = "Group_test",
  Test_group = "Sepsis",
  Ref_group = "Control",
  SummarizedExperiment = TRUE
)

# 3. Perform group comparison analysis using limma
Group_limma <- Groupcomparisonlimma(
  GSE13015,
  sample_info = NULL,
  FC = 1.5,
  pval = 0.1,
  FDR = TRUE,
  Group_column = "Group_test",
  Test_group = "Sepsis",
  Ref_group = "Control",
  SummarizedExperiment = TRUE
)

# 4. Generate fingerprint grid plot
gridplot(
  Group_df,
  cutoff = 15,
  Ref_group = "Control",
  filename = tempfile()
)

# 5. Perform individual single sample analysis
Individual_df <- Individualcomparison(
  GSE13015,
  sample_info = NULL,
  FC = 1.5,
  DIFF = 10,
  Group_column = "Group_test",
  Ref_group = "Control",
  SummarizedExperiment = TRUE
)

# 6. Generate individual fingerprint heatmap
fingerprintplot(
  Individual_df,
  sample_info = NULL,
  cutoff = 15,
  rowSplit = TRUE,
  Group_column = "Group_test",
  show_ref_group = FALSE,
  Ref_group = "Control",
  Aggregate = "A28",
  filename = tempfile(),
  height = NULL,
  width = NULL
)
```
**Input/Output Note:** Inputs a `SummarizedExperiment` or expression matrix with sample annotations; outputs data frames of module-level percentages and generates PDF visualizations of fingerprint grids and heatmaps.

## When to Use
- To perform blood transcriptional module repertoire analysis on gene expression data.
- To determine the percentage of constitutive genes for each module that are significantly increased or decreased between groups using `Groupcomparison()` or `Groupcomparisonlimma()`.
- To perform individual sample-level modular analysis in reference to a control group using `Individualcomparison()`.
- To visualize group comparison results as a fingerprint grid using `gridplot()` or individual comparisons as a fingerprint heatmap using `fingerprintplot()`.

## When NOT to Use
- For general-purpose differential gene expression analysis without modular grouping (use `limma` or `DESeq2` directly).
- For single-cell RNA-seq data requiring cell-type specific clustering and marker identification (use `Seurat` or `scran` instead).

## Data Requirements
- A normalized, non-log2-transformed expression data matrix or a `SummarizedExperiment` object (log2 transformation is performed automatically).
- A sample annotation table (if not using a `SummarizedExperiment` with built-in colData) where the row names match the column names of the expression matrix.

## Key Parameters
- **FC** (1.5): Fold change threshold for determining significant changes in gene expression.
- **pval** (0.1): P-value threshold for statistical significance.
- **FDR** (TRUE): Logical indicating whether to apply False Discovery Rate correction.
- **Group_column** ("Group_test"): Column name in sample metadata containing the group classifications.
- **Test_group** ("Sepsis"): Name of the group to be tested.
- **Ref_group** ("Control"): Name of the reference group.
- **SummarizedExperiment** (TRUE): Logical indicating if the input is a `SummarizedExperiment` object.
- **cutoff** (15): Percentage threshold for module visualization in grid and fingerprint plots.

## Best Practices
- Ensure the input expression matrix is **not** log2-transformed, as the package functions perform log2 transformation internally.
- Verify that the row names of the sample information table (`sample_info`) match the column names of the expression matrix exactly.
- Use `Groupcomparisonlimma()` instead of `Groupcomparison()` when dealing with complex experimental designs or when limma's empirical Bayes moderation is preferred.

## Common Pitfalls
- *Pitfall*: Inputting pre-log-transformed data, leading to incorrect fold change calculations. *Fix*: Provide raw or normalized linear-scale counts/intensities.
- *Pitfall*: Mismatched sample names between expression matrix columns and annotation row names. *Fix*: Align the names using `colnames(data) <- rownames(sample_info)` before running the analysis.

## Alternatives
- `tmod` for general gene set enrichment and module visualization.
- `GSVA` for gene set variation analysis.
- `clusterProfiler` for standard GO/KEGG functional enrichment.

## Citations
- Rinchai D, et al. (2021). "BloodGen3Module: blood transcriptional module repertoire analysis and visualization using R." *Bioinformatics*. doi:10.1093/bioinformatics/btab121.

## References
- Homepage: https://bioconductor.org/packages/bloodgen3module
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/bloodgen3module/inst/doc/BloodGen3Module.html