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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See how to installReview the instructions the agent will receive before you install.
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.
Groupcomparison() or Groupcomparisonlimma().Individualcomparison().gridplot() or individual comparisons as a fingerprint heatmap using fingerprintplot().limma or DESeq2 directly).Seurat or scran instead).SummarizedExperiment object (log2 transformation is performed automatically).SummarizedExperiment with built-in colData) where the row names match the column names of the expression matrix.SummarizedExperiment object.sample_info) match the column names of the expression matrix exactly.Groupcomparisonlimma() instead of Groupcomparison() when dealing with complex experimental designs or when limma's empirical Bayes moderation is preferred.colnames(data) <- rownames(sample_info) before running the analysis.tmod for general gene set enrichment and module visualization.GSVA for gene set variation analysis.clusterProfiler for standard GO/KEGG functional enrichment.No reviews yet. Tell others what you thought.
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