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bioconductor-bloodgen3module

The BloodGen3Module package provides functions for R user performing module repertoire analyses and generating fingerprint representations. Functions can perform group comparison or individual sample analysis and visualization by fingerprint grid plot or fingerprint heatmap. Module repertoire analyses typically involve determining the percentage of the constitutive genes for each module that are significantly increased or decreased. As we describe in details;https://www.biorxiv.org/content/10.11

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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

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