Immune cell profiling reveals expanded stem cell-like memory T cells in anti-GAD65-associated neurological syndromes.

Code ↔ Paper

9 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 9 matches

[1] § Results › Compartment-Specific TCR Clonal Expansion in Anti-GAD65 AINS. ↔ R/immune_cell_profiling_antiGAD65_data_analysis_visualization.Rmd, lines 342 to 467 · score 0.70 · regulated genes, fold change, cell clones, expressed genes, log2FC, nonexpanded · Report a mismatch
[2] § Materials and Methods › Quantification and Statistical Analysis. › Bioinformatic Analyses of Single Cell RNA-Sequencing Data. ↔ R/immune_cell_profiling_antiGAD65_QC_preprocessing.R, lines 1 to 64 · score 0.70 · quality control, Filtered feature, Seurat, v5, matrix, RNA · Report a mismatch
[3] § Materials and Methods › Quantification and Statistical Analysis. › Bioinformatic Analyses of Single Cell RNA-Sequencing Data. ↔ R/immune_cell_profiling_antiGAD65_QC_preprocessing.Rmd, lines 38 to 77 · score 0.61 · Filtered feature, quality control, Seurat, matrix, RNA, Cell · Report a mismatch
[4] § Results › Compartment-Specific TCR Clonal Expansion in Anti-GAD65 AINS. ↔ R/immune_cell_profiling_antiGAD65_data_analysis_visualization.R, lines 717 to 759 · score 0.60 · regulated genes, fold change, expressed genes, log2FC, nonexpanded, Pseudobulk · Report a mismatch
[5] § Results › Demographics and Basic Clinical Data. ↔ R/immune_cell_profiling_antiGAD65_data_analysis_visualization.Rmd, lines 268 to 308 · score 0.58 · activated CD4, Granulo, MAIT, Mega, Mono, TCM · Report a mismatch
[6] § Results › Expansion of TSCM in the PB and CSF of Anti-GAD65 AINS Individuals. ↔ R/immune_cell_profiling_antiGAD65_data_analysis_visualization.Rmd, lines 342 to 467 · score 0.58 · fold change, expressed genes, box, DESeq2, log2FC, Pseudobulking · Report a mismatch
[7] § Results › Compartment-Specific TCR Clonal Expansion in Anti-GAD65 AINS. ↔ R/immune_cell_profiling_antiGAD65_data_analysis_visualization.R, lines 1 to 43 · score 0.58 · immune repertoire, PBMC compartment, CSF compartment, anti GAD65, profile, abundant · Report a mismatch
[8] § Results › Demographics and Basic Clinical Data. ↔ R/immune_cell_profiling_antiGAD65_data_analysis_visualization.R, lines 264 to 306 · score 0.57 · activated CD4, Granulo, MAIT, Mega, Mono, TCM · Report a mismatch
[9] § Results › Expansion of TSCM in the PB and CSF of Anti-GAD65 AINS Individuals. ↔ R/immune_cell_profiling_antiGAD65_data_analysis_visualization.R, lines 717 to 759 · score 0.51 · fold change, expressed genes, DESeq2, log2FC, Pseudobulking, Volcano · Report a mismatch

Paper

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The authors' code

R · 846 lines · 21 KB · MIT · 4 matches

################################################################################
# Immune Cell Profiling in Anti-GAD65 - Data Analysis and Visualization
################################################################################
#
# Author: Sumanta Barman
# Description: Complete R analysis pipeline for immune cell profiling in
# anti-GAD65 encephalitis including single-cell RNA-seq processing,
# cell type annotation, differential abundance, differential
# expression, and immune repertoire analysis.
#
# GitHub: [Your repository URL]
# License: [Your license]
#
################################################################################
################################################################################
# TABLE OF CONTENTS
################################################################################
# 1. Setup and Library Loading
# 2. Data Loading
# 3. Exploratory Visualization
# 4. Cell Type Annotation
# 4.1. SingleR Annotation
# 4.2. Azimuth Annotation
# 4.3. Marker Gene Identification
# 4.4. Manual Annotation
# 5. Differential Cell Abundance Analysis
# 5.1. CSF Compartment
# 5.2. PBMC Compartment
# 6. Differential Gene Expression
# 7. Pseudobulk Volcano Plot
# 8. Session Information
################################################################################
################################################################################
# 1. SETUP AND LIBRARY LOADING
################################################################################
# Set global options
options(
stringsAsFactors = FALSE,
warn = -1 # Suppress warnings
)
# Single-cell analysis packages
library(Seurat)
library(SeuratDisk)
library(tidyverse)
library(ggplot2)
library(gridExtra)
library(DoubletFinder)
library(reticulate)
library(SeuratData)
library(SeuratWrappers)
library(patchwork)
library(harmony)
library(scuttle)
library(sctransform)
library(SingleR)
library(celldex)
library(pheatmap)
library(Azimuth)
library(Signac)
library(dplyr)
library(cowplot)
library(enrichR)
library(rafalib)
library(Matrix)
library(edgeR)
# Differential abundance packages
library(speckle)
library(limma)
# Immune repertoire analysis
library(immunarch)
# Visualization packages
library(EnhancedVolcano)
library(ggrepel)
library(RColorBrewer)
library(ggsci)
library(ggpubr)
library(rstatix)
# Set language
Sys.setenv(lang = "en_US")
cat("Libraries loaded successfully!\n\n")
################################################################################
# 2. DATA LOADING
################################################################################
# Load the integrated Seurat object with singlets
cat("Loading preprocessed Seurat object...\n")
seurat_object <- readRDS(file = "seurat_object_combined_singlets_integrated.rds")
# Display object summary
cat("\nSeurat object loaded:\n")
print(seurat_object)
cat("\n")
################################################################################
# 3. EXPLORATORY VISUALIZATION
################################################################################
cat("Generating UMAP visualizations...\n")
# Visualize different metadata variables on UMAP
DimPlot(
seurat_object,
reduction = "umap.harmony",
group.by = c("sample", "Center", "Disease", "harmony_clusters"),
combine = FALSE,
label.size = 2,
label = TRUE
)
################################################################################
# 4. CELL TYPE ANNOTATION
################################################################################
# ---- 4.1. SingleR Annotation ----
cat("Running SingleR annotation...\n")
# Load reference dataset
ref <- celldex::DatabaseImmuneCellExpressionData()
# Prepare Seurat object for SingleR
seurat_object_singler <- seurat_object
seurat_object_singler.counts <- GetAssayData(
seurat_object_singler,
slot = "counts",
assay = "SCT"
)
# Run SingleR prediction
pred <- SingleR(
test = seurat_object_singler.counts,
ref = ref,
labels = ref$label.fine
)
# Add SingleR labels to metadata
seurat_object_singler$singleR.labels <- pred$labels[
match(rownames([email hidden]), rownames(pred))
]
# Save annotated object
saveRDS(seurat_object_singler, file = "immune_cell_profiling_anti_gad65_singler.rds")
cat("SingleR annotation complete. Object saved.\n\n")
## SingleR Visualization
cat("Visualizing SingleR results...\n")
# Basic UMAP with SingleR labels
p1 <- DimPlot(
seurat_object_singler,
reduction = "umap.harmony",
group.by = "singleR.labels"
)
# UMAP with labels and repelling
p2 <- DimPlot(
seurat_object_singler,
reduction = "umap.harmony",
group.by = "singleR.labels",
label = TRUE,
repel = TRUE,
label.size = 2
)
# Split by disease condition
p3 <- DimPlot(
seurat_object_singler,
reduction = "umap.harmony",
group.by = "singleR.labels",
split.by = "Disease",
label = TRUE,
repel = TRUE,
label.size = 3
)
print(p1 | p2)
print(p3)
# ---- 4.2. Azimuth Annotation ----
cat("\nRunning Azimuth annotation...\n")
# Prepare object for Azimuth
seurat_object_azimuth <- seurat_object
seurat_object_azimuth <- PrepSCTFindMarkers(
seurat_object_azimuth,
assay = "SCT",
verbose = TRUE
)
# Run Azimuth annotation
seurat_object_azimuth <- RunAzimuth(
seurat_object_azimuth,
reference = "pbmcref"
)
# Save annotated object
saveRDS(seurat_object_azimuth, file = "immune_cell_profiling_anti_gad65_azimuth.rds")
cat("Azimuth annotation complete. Object saved.\n\n")
## Azimuth Visualization
cat("Visualizing Azimuth results...\n")
# UMAP without legend
p1 <- DimPlot(
seurat_object_azimuth,
reduction = "umap.harmony",
group.by = "predicted.celltype.l2",
label = TRUE,
repel = TRUE,
label.size = 2
) + NoLegend()
# UMAP with legend
p2 <- DimPlot(
seurat_object_azimuth,
reduction = "umap.harmony",
group.by = "predicted.celltype.l2",
label = TRUE,
repel = TRUE,
label.size = 3
)
# Split by disease
p3 <- DimPlot(
seurat_object_azimuth,
reduction = "umap.harmony",
group.by = "predicted.celltype.l2",
split.by = "Disease",
label = TRUE,
repel = TRUE,
label.size = 3
) + NoLegend()
print(p1 | p2)
print(p3)
# ---- 4.3. Marker Gene Identification ----
cat("\nIdentifying cluster marker genes...\n")
# Prepare for marker finding
seurat_object <- PrepSCTFindMarkers(seurat_object, assay = "SCT", verbose = TRUE)
DefaultAssay(seurat_object)
# Set identity to harmony clusters
Idents(seurat_object) <- "harmony_clusters"
# Find markers using Wilcoxon test
markers_genes_RNA_wilcox <- FindAllMarkers(
seurat_object,
test.use = "wilcox",
only.pos = TRUE,
assay = "SCT"
)
# Save markers
write.csv(
markers_genes_RNA_wilcox,
"cluster_markers_wilcox.csv",
row.names = FALSE
)
cat("Marker genes saved to cluster_markers_wilcox.csv\n\n")
# ---- 4.4. Manual Annotation ----
cat("Applying manual cell type annotations...\n")
# Create annotated object
seurat_object_annotated <- seurat_object
# Define cell type labels for each cluster
replacement_names <- c(
"0" = "CD4+ Naive T",
"1" = "CD4+ TCM",
"2" = "CD8+ TEM, MAIT, gDT",
"3" = "NK1",
"4" = "CD8+ TEM",
"5" = "Granulo",
"6" = "cDC2",
"7" = "Activated CD4+ TSCM",
"8" = "CD8+ Naive T",
"9" = "cDC1",
"10" = "BC1",
"12" = "BC2",
"13" = "CD16+ Mono",
"14" = "NK2",
"16" = "Mega",
"17" = "pDC",
"18" = "CD14+ Mono",
"19" = "Memory BC",
"20" = "Plasma",
"21" = "Microglia-like",
"24" = "BC3"
)
# Add annotations to metadata
metadata <- [email hidden]
metadata$annotate <- replacement_names[as.character(metadata$harmony_clusters)]
[email hidden] <- metadata
# Save annotated object
saveRDS(seurat_object_annotated, file = "immune_cell_profiling_anti_gad65_annotated.rds")
cat("Manual annotation complete. Object saved.\n\n")
## Visualization of Annotated Clusters
cat("Visualizing annotated cell types...\n")
# UMAP with cell type labels
p1 <- DimPlot(
seurat_object_annotated,
reduction = "umap.harmony",
group.by = "annotate",
label = TRUE
)
# Split by disease
p2 <- DimPlot(
seurat_object_annotated,
reduction = "umap.harmony",
group.by = "annotate",
split.by = "Disease"
)
# Split by sample type
p3 <- DimPlot(
seurat_object_annotated,
reduction = "umap.harmony",
group.by = "annotate",
split.by = "Sampletype"
)
print(p1)
print(p2)
print(p3)
################################################################################
# 5. DIFFERENTIAL CELL ABUNDANCE ANALYSIS
################################################################################
cat("\n=== DIFFERENTIAL CELL ABUNDANCE ANALYSIS ===\n\n")
# Display cell counts
cat("Cell type distribution:\n")
print(table(seurat_object_annotated$annotate))
cat("\nSample distribution:\n")
print(table(seurat_object_annotated$sample))
# ---- Proportions by Sample Type (CSF vs PBMC) ----
cat("\nAnalyzing proportions by sample type...\n")
props_csf_pbmc <- getTransformedProps(
clusters = seurat_object_annotated$annotate,
sample = seurat_object_annotated$Sampletype
)
# Visualize proportions
p1 <- plotCellTypeProps(
clusters = seurat_object_annotated$annotate,
sample = seurat_object_annotated$Sampletype
) +
theme_bw() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()
) +
ggtitle("Cell Type Proportions by Sample Type")
print(p1)
# ---- Proportions by Disease Status ----
cat("\nAnalyzing proportions by disease status...\n")
props_disease <- getTransformedProps(
clusters = seurat_object_annotated$annotate,
sample = seurat_object_annotated$Disease
)
# Visualize
p1 <- plotCellTypeProps(
clusters = seurat_object_annotated$annotate,
sample = seurat_object_annotated$Disease
) +
theme_bw() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()
) +
ggtitle("Cell Type Proportions by Disease Status")
print(p1)
# ---- Subset CSF and PBMC Compartments ----
cat("\nSubsetting CSF and PBMC compartments...\n")
CSF <- subset(seurat_object_annotated, Sampletype == "CSF")
cat(sprintf("CSF samples: %d cells\n", ncol(CSF)))
PBMC <- subset(seurat_object_annotated, Sampletype == "PBMC")
cat(sprintf("PBMC samples: %d cells\n\n", ncol(PBMC)))
# ---- 5.1. CSF Compartment Analysis ----
cat("=== CSF COMPARTMENT ANALYSIS ===\n")
## Cell Type Proportions in CSF
props_csf <- getTransformedProps(
clusters = CSF$annotate,
sample = CSF$samplename
)
# Save proportions
write.csv(
props_csf,
"cluster_abundances_csf_annotated.csv",
quote = FALSE,
row.names = FALSE
)
cat("CSF proportions saved to cluster_abundances_csf_annotated.csv\n")
# Visualize
p1 <- plotCellTypeProps(
clusters = CSF$annotate,
sample = CSF$samplename
) +
theme_bw() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()
) +
ggtitle("CSF: Cell Type Proportions")
print(p1)
## Statistical Testing for CSF
cat("\nPerforming statistical tests for CSF...\n")
df_csf <- read.csv("cluster_abundances_csf_annotated.csv")
# Reshape data for statistical testing
df_transposed_csf <- df_csf %>%
pivot_wider(
names_from = Counts.clusters,
values_from = Proportions.Freq,
id_cols = Counts.sample
)
# Add group labels
df_transposed_csf$Group <- ifelse(
grepl("^control_", df_transposed_csf$Counts.sample),
"control",
"GAD"
)
# Prepare data for volcano plot
df_processed_csf <- df_transposed_csf %>%
select(-Counts.sample) %>%
select(Group, everything())
# Save processed data
write.csv(
df_processed_csf,
"GAD_vs_control_csf_processed.csv",
row.names = FALSE
)
## CSF Volcano Plot
cat("Generating CSF volcano plot...\n")
GAD <- df_processed_csf[df_processed_csf$Group == "GAD", -1]
control <- df_processed_csf[df_processed_csf$Group == "control", -1]
results_log2FC_csf <- data.frame(
Cluster = colnames(GAD),
Log2FC = NA,
Log10pvalue = NA
)
for (i in 1:ncol(GAD)) {
x <- GAD[, i]
y <- control[, i]
# Calculate fold change
Log2FC <- log2(median(x) / median(y))
# Calculate p-value
pvalue <- wilcox.test(x, y)$p.value
Log10pvalue <- -log10(pvalue)
results_log2FC_csf[i, 2] <- Log2FC
results_log2FC_csf[i, 3] <- Log10pvalue
}
# Remove NA and infinite values
results_log2FC_csf <- results_log2FC_csf[
complete.cases(results_log2FC_csf$Log2FC, results_log2FC_csf$Log10pvalue) &
!is.infinite(results_log2FC_csf$Log2FC) &
!is.infinite(results_log2FC_csf$Log10pvalue),
]
# Save results
write.csv(results_log2FC_csf, "GAD_vs_control_CSF_Log2FC.csv", row.names = FALSE)
cat("CSF results saved to GAD_vs_control_CSF_Log2FC.csv\n")
# Create volcano plot
p <- ggplot(
data = results_log2FC_csf,
aes(x = Log2FC, y = Log10pvalue, label = Cluster)
) +
geom_point(shape = 16, size = 4, aes(color = Cluster, fill = Cluster)) +
theme_minimal() +
geom_vline(xintercept = c(-0.485, 0.485), col = "red", linetype = "dashed") +
geom_hline(yintercept = -log10(0.05), col = "blue", linetype = "dashed") +
theme(
axis.line = element_line(color = "black"),
axis.ticks = element_line(color = "black")
) +
geom_text_repel(aes(label = Cluster), max.overlaps = 20) +
labs(
x = "Log2 fold change",
y = "-Log10 p value",
title = "CSF: GAD vs Control"
)
print(p)
# ---- 5.2. PBMC Compartment Analysis ----
cat("\n=== PBMC COMPARTMENT ANALYSIS ===\n")
## Cell Type Proportions in PBMC
props_pbmc <- getTransformedProps(
clusters = PBMC$annotate,
sample = PBMC$samplename
)
# Save proportions
write.csv(
props_pbmc,
"cluster_abundances_pbmc_annotated.csv",
quote = FALSE,
row.names = FALSE
)
cat("PBMC proportions saved to cluster_abundances_pbmc_annotated.csv\n")
# Visualize
p1 <- plotCellTypeProps(
clusters = PBMC$annotate,
sample = PBMC$samplename
) +
theme_bw() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()
) +
ggtitle("PBMC: Cell Type Proportions")
print(p1)
## Statistical Testing for PBMC
cat("\nPerforming statistical tests for PBMC...\n")
df_pbmc <- read.csv("cluster_abundances_pbmc_annotated.csv")
# Reshape data
df_transposed_pbmc <- df_pbmc %>%
pivot_wider(
names_from = Counts.clusters,
values_from = Proportions.Freq,
id_cols = Counts.sample
)
# Add group labels
df_transposed_pbmc$Group <- ifelse(
grepl("^control_", df_transposed_pbmc$Counts.sample),
"control",
"GAD"
)
# Prepare data
df_processed_pbmc <- df_transposed_pbmc %>%
select(-Counts.sample) %>%
select(Group, everything())
# Save processed data
write.csv(
df_processed_pbmc,
"GAD_vs_control_pbmc_processed.csv",
row.names = FALSE
)
## PBMC Volcano Plot
cat("Generating PBMC volcano plot...\n")
GAD_pbmc <- df_processed_pbmc[df_processed_pbmc$Group == "GAD", -1]
control_pbmc <- df_processed_pbmc[df_processed_pbmc$Group == "control", -1]
results_log2FC_pbmc <- data.frame(
Cluster = colnames(GAD_pbmc),
Log2FC = NA,
Log10pvalue = NA
)
for (i in 1:ncol(GAD_pbmc)) {
x <- GAD_pbmc[, i]
y <- control_pbmc[, i]
Log2FC <- log2(median(x) / median(y))
pvalue <- wilcox.test(x, y)$p.value
Log10pvalue <- -log10(pvalue)
results_log2FC_pbmc[i, 2] <- Log2FC
results_log2FC_pbmc[i, 3] <- Log10pvalue
}
# Clean results
results_log2FC_pbmc <- results_log2FC_pbmc[
complete.cases(results_log2FC_pbmc$Log2FC, results_log2FC_pbmc$Log10pvalue) &
!is.infinite(results_log2FC_pbmc$Log2FC) &
!is.infinite(results_log2FC_pbmc$Log10pvalue),
]
# Save results
write.csv(results_log2FC_pbmc, "GAD_vs_control_PBMC_Log2FC.csv", row.names = FALSE)
cat("PBMC results saved to GAD_vs_control_PBMC_Log2FC.csv\n")
# Create volcano plot
p <- ggplot(
data = results_log2FC_pbmc,
aes(x = Log2FC, y = Log10pvalue, label = Cluster)
) +
geom_point(shape = 16, size = 4, aes(color = Cluster, fill = Cluster)) +
theme_minimal() +
geom_vline(xintercept = c(-0.485, 0.485), col = "red", linetype = "dashed") +
geom_hline(yintercept = -log10(0.05), col = "blue", linetype = "dashed") +
theme(
axis.line = element_line(color = "black"),
axis.ticks = element_line(color = "black")
) +
geom_text_repel(aes(label = Cluster), max.overlaps = 20) +
labs(
x = "Log2 fold change",
y = "-Log10 p value",
title = "PBMC: GAD vs Control"
)
print(p)
################################################################################
# 6. DIFFERENTIAL GENE EXPRESSION
################################################################################
cat("\n=== DIFFERENTIAL GENE EXPRESSION ANALYSIS ===\n")
# Set identity to disease
Idents(seurat_object_annotated) <- seurat_object_annotated$Disease
# Find DEGs
cat("Finding differentially expressed genes...\n")
dge_control_gad <- FindMarkers(
seurat_object_annotated,
ident.1 = "GAD",
ident.2 = "control",
test.use = "wilcox",
logfc.threshold = 0.2,
assay = "SCT"
)
# Save results
write.csv(
dge_control_gad,
"DEGs_GAD_vs_control_all_clusters.csv",
row.names = TRUE
)
cat("DEG results saved to DEGs_GAD_vs_control_all_clusters.csv\n")
## Volcano Plot: All Clusters
cat("Generating volcano plot for all clusters...\n")
df_dge <- read.csv("DEGs_GAD_vs_control_all_clusters.csv", row.names = 1)
df_dge$gene_symbol <- rownames(df_dge)
vp <- EnhancedVolcano(
df_dge,
x = "avg_log2FC",
y = "p_val_adj",
lab = df_dge$gene_symbol,
pCutoff = 1e-20,
FCcutoff = 1.5,
legendLabels = c(
"Not significant",
"Log2FC",
"p-value",
"p-value and Log2FC"
),
legendPosition = "right",
pointSize = 4.0,
labSize = 5.0,
labCol = "black",
labFace = "bold",
drawConnectors = TRUE,
legendLabSize = 16,
legendIconSize = 4.0,
title = "GAD versus Control (All Clusters)"
)
print(vp)
################################################################################
# 7. PSEUDOBULK VOLCANO PLOT
################################################################################
cat("\n=== PSEUDOBULK DIFFERENTIAL EXPRESSION ANALYSIS ===\n")
# ---- Configuration ----
LOG2FC_THRESHOLD <- 0.585 # ~1.5-fold change
PADJ_THRESHOLD <- 0.05
N_TOP_GENES <- 10 # Number of top genes to label
INPUT_FILE <- "pseudobulk_deseq2_GAD_CSF_expanded_vs_nonexpanded.csv"
# ---- Load differential expression results ----
cat(sprintf("Loading results from %s...\n", INPUT_FILE))
de_results <- read.csv(INPUT_FILE, row.names = 1) %>%
rownames_to_column(var = "gene") %>%
as_tibble()
cat(sprintf("Loaded %d genes\n", nrow(de_results)))
# ---- Define color scheme for significance ----
color_scheme <- case_when(
de_results$log2FoldChange <= -LOG2FC_THRESHOLD & de_results$padj <= PADJ_THRESHOLD ~ "dodgerblue3",
de_results$log2FoldChange >= LOG2FC_THRESHOLD & de_results$padj <= PADJ_THRESHOLD ~ "chartreuse3",
TRUE ~ "grey"
)
# Replace NA values with grey
color_scheme[is.na(color_scheme)] <- "grey"
# Create named vector for legend
names(color_scheme)[color_scheme == "chartreuse3"] <- "Up-regulated"
names(color_scheme)[color_scheme == "dodgerblue3"] <- "Down-regulated"
names(color_scheme)[color_scheme == "grey"] <- "Not significant"
# ---- Identify top differentially expressed genes ----
cat("Identifying top differentially expressed genes...\n")
# Top up-regulated genes
top_upregulated <- de_results %>%
filter(log2FoldChange >= LOG2FC_THRESHOLD, padj <= PADJ_THRESHOLD) %>%
arrange(padj, desc(log2FoldChange)) %>%
head(N_TOP_GENES)
# Top down-regulated genes
top_downregulated <- de_results %>%
filter(log2FoldChange <= -LOG2FC_THRESHOLD, padj <= PADJ_THRESHOLD) %>%
arrange(padj, log2FoldChange) %>%
head(N_TOP_GENES)
# Combine top genes for labeling
genes_to_label <- c(top_upregulated$gene, top_downregulated$gene)
cat(sprintf("\nIdentified %d up-regulated and %d down-regulated genes\n",
nrow(top_upregulated), nrow(top_downregulated)))
cat("Top labeled genes:", paste(genes_to_label, collapse = ", "), "\n\n")
# ---- Generate volcano plot ----
cat("Generating enhanced volcano plot...\n")
volcano_plot <- EnhancedVolcano(
de_results,
lab = de_results$gene,
x = "log2FoldChange",
y = "padj",
# Significance thresholds
pCutoff = PADJ_THRESHOLD,
FCcutoff = LOG2FC_THRESHOLD,
# Point styling
pointSize = 3,
colAlpha = 0.6,
colCustom = color_scheme,
shape = 16,
# Axis labels
xlab = bquote(~Log[2]~ "fold change"),
ylab = bquote(~-Log[10]~ "adjusted p-value"),
# Gene labels
selectLab = genes_to_label,
boxedLabels = TRUE,
labSize = 3.5,
labFace = "bold",
drawConnectors = TRUE,
widthConnectors = 0.5,
lengthConnectors = unit(0.01, "npc"),
arrowheads = FALSE,
maxoverlapsConnectors = Inf,
max.overlaps = Inf,
# Plot annotations
title = "Differential Expression: Expanded vs Non-Expanded Clones",
subtitle = "GAD Patient CSF T cells (Pseudobulk Analysis)",
caption = bquote(~Log[2]~ "FC cutoff: ±" * .(LOG2FC_THRESHOLD) *
"; Adjusted p-value cutoff: " * .(PADJ_THRESHOLD)),
# Legend
legendPosition = "right",
legendLabSize = 12,
legendIconSize = 4.0
)
print(volcano_plot)
# ---- Summary statistics ----
summary_stats <- de_results %>%
mutate(
regulation = case_when(
log2FoldChange >= LOG2FC_THRESHOLD & padj <= PADJ_THRESHOLD ~ "Up-regulated",
log2FoldChange <= -LOG2FC_THRESHOLD & padj <= PADJ_THRESHOLD ~ "Down-regulated",
TRUE ~ "Not significant"
)
) %>%
count(regulation)
cat("\n=== Differential Expression Summary ===\n")
print(summary_stats)
################################################################################
# 8. SESSION INFORMATION
################################################################################
cat("\n=== SESSION INFORMATION ===\n")
sessionInfo()
cat("\n=== ANALYSIS COMPLETE ===\n")
cat("All results have been saved to the working directory.\n")

immune_cell_profiling_antiGAD65_data_analysis_visualization.R at commit 7a2bdda, under MIT · at the source

Overview

Authors: Sumanta Barman¹, Saskia Räuber¹, Katharina Eisenhut^2,3, Daniela Esser⁴, Martijn van Duijn⁵, Madeleine Scharf⁶, Marisol Herrera-Rivero⁷, Paul Disse¹, Lara-Maria Preuth¹, Valeria Gulyaeva¹, Ilja Schwan¹, Eliza vom Stein¹, Marius Jonas¹, Duygu Pul¹, Michael Heming⁸, Louisa Müller-Miny⁸, Manuela Paunovic⁵, Christine Strippel⁸, Ebru Haholu^2,3,9, Jan Bartosch^2,3,9

and 26 other authors

Elisabeth Kaufmann¹⁰, Justina Dargvainiene⁴, Sabine Kahl^11,12,13, Marius Ringelstein^1,14, Eric Bindels¹⁵, Heinz Wiendl⁸, Nikolas H Stoecklein¹⁶, Johannes Fischer¹⁷, Norbert Goebels¹, Lars Komorowski⁶, Michael Roden^11,12,13, Andrea Rossi¹⁸, Monika Stoll¹⁹, Albert J Becker²⁰, Motaz Hamed²¹, Christian G Bien²², Romana Höftberger²³, Jan Bauer²⁴, Sven G Meuth¹, Maarten J Titulaer⁵, Frank Leypoldt^4,25, Gerd Meyer zu Hörste⁸, Franziska Thaler^2,3, Nico Melzer¹, in cooperation with the Erasmus Medical Center-AIE Study Group, the German Network for Research on Autoimmune Encephalitis

25 affiliations

Institutions: Düsseldorf University Hospital (Germany); Heinrich Heine University Düsseldorf (Germany); Institut für Klinische Neuroimmunologie (Germany); Ludwig-Maximilians-Universität München (Germany); University Hospital Schleswig-Holstein (Germany); Erasmus MC (Netherlands); Euroimmun Medizinische Labordiagnostika (Germany) (Germany); University of Münster (Germany); University Hospital Münster (Germany); Deutsches Diabetes-Zentrum e.V. (Germany); German Center for Diabetes Research (Germany); Leibniz Institute of Environmental Medicine (Germany); University Hospital Bonn (Germany); Bielefeld University (Germany); Medical University of Vienna (Austria)

Journal: Proceedings of the National Academy of Sciences of the United States of America, volume 123, issue 13, article e2514753123

Dates: received 8 June 2025; accepted 10 February 2026; published online 25 March 2026; in print 31 March 2026

Type: Research article · Language: English

License: CC BY-NC-ND

Identifiers: DOI 10.1073/pnas.2514753123 · PMID 41880578 · PMCID PMC13038060 · OpenAlex W7140298212

Open access: hybrid, a free copy (OpenAlex)

Status: code verified (how the code is found and verified)

Categories: genetics / omics (modality), human (organism), other condition (population), cellular / molecular (subfield)

Keywords: anti-GAD65 autoimmune neurological syndromes, single cell transcriptomics, immune repertoire profiling

MeSH: Autoantibodies*, Autoimmune Diseases of the Nervous System*, Glutamate Decarboxylase*, Memory T Cells*, Adult, CD8-Positive T-Lymphocytes, Female, Humans, Leukocytes, Mononuclear, Male, Middle Aged, Single-Cell Analysis, Stem Cells (* major topic)

Topic: Autoimmune Neurological Disorders and Treatments (Clinical Neurology, Medicine), according to OpenAlex

Funding: Fritz Thyssen Stiftung (Thyssen Foundation) (10.23.1.015MN); HHU | Medizinische Fakultät, Heinrich-Heine-Universität Düsseldorf (2022-04); Deutsche Forschungsgemeinschaft (ERARE18-202 UltraAIE); EpilepsieNL (19-18); Stichting Dioraphte (2001 0403); Else Kröner-Fresenius-Stiftung (2023_EKMS.05); Austrian Science Fund FWF (P 34864); LMU excellent (AOST 867603-4); ZonMw (ERARE-JTC2018 202: UltraAIE); Bundesministerium für Forschung, Technologie und Raumfahrt (01GM1908A and 01GM2208A); Friedrich-Baur-Stiftung (54/23)

Citations: cited by 1 paper (Europe PMC); 63 references in the paper

Notices: A comment on this paper has been published (42114000, from Europe PMC)

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

Its files are read in the Code ↔ Paper reader above, with 9 matches between paragraphs and lines of code.

sumanta-barman/immune-cell-profiling-anti-gad65

License: MIT

State: the link answers, verified on 30 September 2026

Evidence: files inventoried

Commit: 7a2bddafab023cd5287cb38983ec7dd31c4c7324, 11 March 2026

Languages: Jupyter (5), R (4), Shell (1)

Size: 16 files, 10 scripts

Software Heritage: not archived

Found in: “Data, Materials, and Software Availability”

Holds: README, license file, 7 notebooks

Not found: CITATION.cff, environment file, tests, continuous integration, documentation

Tools: NumPy (5 files), Scanpy (5 files), ggplot2 (4 files), Harmony (4 files), Matplotlib (4 files), patchwork (4 files), reticulate (4 files), Seurat (4 files), tidyverse (4 files), pandas (3 files), anndata (2 files), cowplot (2 files), edgeR (2 files), ggpubr (2 files), limma (2 files), pheatmap (2 files), rstatix (2 files), seaborn (2 files)

Availability: 1 check, the latest on 30 September 2026: the link answers

30 September 2026: the link answers

12 files

Bash/immune_cell_profiling_antiGAD65_SHM_preprocessing.sh, Shell, 275 lines
Notebook/immune_cell_profiling_antiGAD65_BCR_preprocessing .ipynb, Jupyter, 333 lines
Notebook/immune_cell_profiling_antiGAD65_Bcell_BCR_visualization.ipynb, Jupyter, 652 lines
Notebook/immune_cell_profiling_antiGAD65_TCR_preprocessing.ipynb, Jupyter, 327 lines
Notebook/immune_cell_profiling_antiGAD65_Tcell_TCR_visualization.ipynb, Jupyter, 666 lines
Notebook/immune_cell_profiling_antiGAD65_pseudobulk_preprocessing.ipynb, Jupyter, 231 lines
R/immune_cell_profiling_antiGAD65_QC_preprocessing.R, R, 809 lines, 1 match
R/immune_cell_profiling_antiGAD65_QC_preprocessing.Rmd, R, 833 lines, 1 match
R/immune_cell_profiling_antiGAD65_data_analysis_visualization.R, R, 846 lines, 4 matches
R/immune_cell_profiling_antiGAD65_data_analysis_visualization.Rmd, R, 873 lines, 3 matches
LICENSE, License, 21 lines
README.md, Text, 23 lines

At the source: github.com/sumanta-barman/immune-cell-profiling-anti-gad65

The paper's code and data availability statement is in the Data section.

Tracing map

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What the map holds:

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10 scripts, each with its path and the digest of its content;
9 matches between paragraphs of the paper and lines of the code (method lexical-v1);
neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

geo:GSE138266, at NCBI GEO; found in “Data, Materials, and Software Availability”

Code and data availability statement

The paper has a code and data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

it points to a dataset: NCBI GEO GSE138266
it points to the authors' code: sumanta-barman/immune-cell-profiling-anti-gad65
it says that the data are available on request

Read it in the paper: doi.org/10.1073/pnas.2514753123.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 46 authors, 3 keywords, 13 MeSH terms, 11 funders, 61 references, 1 integrity notice.

Cite

This paper

Barman, S., Räuber, S., Eisenhut, K., Esser, D., van Duijn, M., Scharf, M., Herrera-Rivero, M., Disse, P., Preuth, L.-M., Gulyaeva, V., Schwan, I., vom Stein, E., Jonas, M., Pul, D., Heming, M., Müller-Miny, L., Paunovic, M., Strippel, C., Haholu, E., . . . the German Network for Research on Autoimmune Encephalitis. (2026). Immune cell profiling reveals expanded stem cell-like memory T cells in anti-GAD65-associated neurological syndromes. Proceedings of the National Academy of Sciences of the United States of America, 123(13), e2514753123. https://doi.org/10.1073/pnas.2514753123

BibTeX

@article{barman2026immune, author = {Barman, Sumanta and Räuber, Saskia and Eisenhut, Katharina and Esser, Daniela and van Duijn, Martijn and Scharf, Madeleine and Herrera-Rivero, Marisol and Disse, Paul and Preuth, Lara-Maria and Gulyaeva, Valeria and Schwan, Ilja and vom Stein, Eliza and Jonas, Marius and Pul, Duygu and Heming, Michael and Müller-Miny, Louisa and Paunovic, Manuela and Strippel, Christine and Haholu, Ebru and Bartosch, Jan and Kaufmann, Elisabeth and Dargvainiene, Justina and Kahl, Sabine and Ringelstein, Marius and Bindels, Eric and Wiendl, Heinz and Stoecklein, Nikolas H and Fischer, Johannes and Goebels, Norbert and Komorowski, Lars and Roden, Michael and Rossi, Andrea and Stoll, Monika and Becker, Albert J and Hamed, Motaz and Bien, Christian G and Höftberger, Romana and Bauer, Jan and Meuth, Sven G and Titulaer, Maarten J and Leypoldt, Frank and zu Hörste, Gerd Meyer and Thaler, Franziska and Melzer, Nico and {in cooperation with the Erasmus Medical Center-AIE Study Group} and {the German Network for Research on Autoimmune Encephalitis}}, title = {{Immune cell profiling reveals expanded stem cell-like memory T cells in anti-GAD65-associated neurological syndromes}}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, year = {2026}, month = mar, volume = {123}, number = {13}, pages = {e2514753123}, publisher = {National Academy of Sciences}, issn = {0027-8424}, doi = {10.1073/pnas.2514753123}, url = {https://doi.org/10.1073/pnas.2514753123}, pmid = {41880578}, pmcid = {PMC13038060} }

RIS

TY - JOUR AU - Barman, Sumanta AU - Räuber, Saskia AU - Eisenhut, Katharina AU - Esser, Daniela AU - van Duijn, Martijn AU - Scharf, Madeleine AU - Herrera-Rivero, Marisol AU - Disse, Paul AU - Preuth, Lara-Maria AU - Gulyaeva, Valeria AU - Schwan, Ilja AU - vom Stein, Eliza AU - Jonas, Marius AU - Pul, Duygu AU - Heming, Michael AU - Müller-Miny, Louisa AU - Paunovic, Manuela AU - Strippel, Christine AU - Haholu, Ebru AU - Bartosch, Jan AU - Kaufmann, Elisabeth AU - Dargvainiene, Justina AU - Kahl, Sabine AU - Ringelstein, Marius AU - Bindels, Eric AU - Wiendl, Heinz AU - Stoecklein, Nikolas H AU - Fischer, Johannes AU - Goebels, Norbert AU - Komorowski, Lars AU - Roden, Michael AU - Rossi, Andrea AU - Stoll, Monika AU - Becker, Albert J AU - Hamed, Motaz AU - Bien, Christian G AU - Höftberger, Romana AU - Bauer, Jan AU - Meuth, Sven G AU - Titulaer, Maarten J AU - Leypoldt, Frank AU - zu Hörste, Gerd Meyer AU - Thaler, Franziska AU - Melzer, Nico AU - in cooperation with the Erasmus Medical Center-AIE Study Group AU - the German Network for Research on Autoimmune Encephalitis TI - Immune cell profiling reveals expanded stem cell-like memory T cells in anti-GAD65-associated neurological syndromes T2 - Proceedings of the National Academy of Sciences of the United States of America J2 - Proc Natl Acad Sci U S A PY - 2026 DA - 2026/03/25 VL - 123 IS - 13 SP - e2514753123 SN - 0027-8424 PB - National Academy of Sciences DO - 10.1073/pnas.2514753123 UR - https://doi.org/10.1073/pnas.2514753123 LA - en ER -

CSL-JSON

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