BloodGen3Module
Workflows
Standard Workflow
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