CAR-T Target Assessment Workflow - coding
Giorgia Moranzoni
2026-02-06
1 Introduction
Chimeric Antigen Receptor T-cell (CAR-T) therapy represents a revolutionary approach to cancer treatment, where a patient’s T-cells are genetically modified to recognize and attack cancer cells. The success of CAR-T therapy critically depends on identifying suitable target antigens—proteins expressed on cancer cells that can serve as targets for the engineered T-cells.
This vignette exemplifies a comprehensive computational workflow for CAR-T target assessment at the isoform level. We demonstrate this workflow using ERBB2 (HER2/neu) as a case study, systematically evaluating isoforms based on subcellular localization, membrane topology, epitope retention, and expression patterns. ERBB” is a receptor tyrosine kinase overexpressed in approximately 25-30% of breast cancers Slamon, D.J. et al. and an established target for monoclonal antibody therapy (trastuzumab) Yoon, J. et al..
2 Phase 1: Isoform Discovery
2.1 Step 1: Retrieve Isoform Sequences
Download protein sequences for all HER2 isoforms from Ensembl (GRCh38, release 115). We retrieve sequences for all protein-coding transcripts to ensure comprehensive coverage.
# Connect to Ensembl
ensembl <- useEnsembl(biomart = "genes", dataset = "hsapiens_gene_ensembl")
# Get protein-coding ERBB2 transcripts
get_gene_transcripts <- function(gene_symbol, mart) {
getBM(
attributes = c("ensembl_gene_id", "ensembl_transcript_id",
"transcript_biotype", "transcript_length"),
filters = "hgnc_symbol",
values = gene_symbol,
mart = mart
) %>%
filter(transcript_biotype == "protein_coding") %>%
arrange(desc(transcript_length))
}
her2_transcripts <- get_gene_transcripts("ERBB2", ensembl)
# Retrieve protein sequences
get_protein_sequences <- function(transcript_ids, mart) {
seqs <- getBM(
attributes = c("ensembl_transcript_id", "peptide"),
filters = "ensembl_transcript_id",
values = transcript_ids,
mart = mart
)
sequences <- AAStringSet(seqs$peptide)
names(sequences) <- seqs$ensembl_transcript_id
sequences[width(sequences) > 0] # Remove empty sequences
}
her2_sequences_all <- get_protein_sequences(her2_transcripts$ensembl_transcript_id, ensembl)
print(her2_sequences_all)## AAStringSet object of length 24:
## width seq names
## [1] 102 MKLRLPASPETHLDMLRHLYQGC...NYALAVLDNGDPLNNTTPVTGA ENST00000578709
## [2] 1226 MKLRLPASPETHLDMLRHLYQGC...STFKGTPTAENPEYLGLDVPV* ENST00000584601
## [3] 177 XPCHPECQPQNGSVTCFGPEADQ...KETELRKVKGIWIPDGENVKIP ENST00000582818
## [4] 1056 MELAALCRWGLLLALLPPGAAST...PAPGAGGMVHHRHRSSSTRNM* ENST00000584450
## [5] 604 MKLRLPASPETHLDMLRHLYQGC...IWKFPDEEGACQPCPINCTHS* ENST00000584014
## ... ... ...
## [20] 1267 MELAALCRWGLLLALLPPGAAST...STFKGTPTAENPEYLGLDVPV* ENST00000959774
## [21] 1194 MELAALCRWGLLLALLPPGAAST...STFKGTPTAENPEYLGLDVPV* ENST00000938923
## [22] 1137 MELAALCRWGLLLALLPPGAAST...STFKGTPTAENPEYLGLDVPV* ENST00000938925
## [23] 1290 MELAALCRWGLLLALLPPGAAST...STFKGTPTAENPEYLGLDVPV* ENST00000938924
## [24] 1140 MELAALCRWGLLLALLPPGAAST...STFKGTPTAENPEYLGLDVPV* ENST000009597752.2 Step 2: Download Expression Data
We use pre-processed TCGA/GTEx expression data from Xena Browser. This dataset provides transcript-level expression across cancer and normal tissues. Note: Ensembl’s September 2025 update added ~120,000 new transcripts not yet reflected in available expression databases, so some isoforms are absent from the expression data.
# File paths
expr_file <- "data/TcgaTargetGtex_rsem_isoform_tpm.gz"
filtered_file <- "results/her2_isoforms_filtered.txt"
output_rds <- "results/her2_expr_filtered.rds"
# HER2 transcripts to extract
her2_transcripts <- names(her2_sequences_all)
# Filter expression file line-by-line (efficient for large files)
con <- gzfile(expr_file, "r")
header <- readLines(con, n = 1)
writeLines(header, filtered_file)
line_count <- 0
matched_count <- 0
while (TRUE) {
lines <- readLines(con, n = 1000)
if (length(lines) == 0) break
line_count <- line_count + length(lines)
# Extract HER2 transcript lines
pattern <- paste0("^(", paste(her2_transcripts, collapse = "|"), ")(\\.|\\t)")
matching_lines <- grep(pattern, lines, value = TRUE)
if (length(matching_lines) > 0) {
write(matching_lines, file = filtered_file, append = TRUE)
matched_count <- matched_count + length(matching_lines)
}
}
close(con)
# Read filtered data and save as RDS
her2_expr <- read_tsv(filtered_file, show_col_types = FALSE)
saveRDS(her2_expr, output_rds)2.3 Step 3: Identify Available Transcripts
Identify which HER2 isoforms have expression data and filter sequences accordingly.
# Load pre-filtered expression data
her2_expr <- readRDS("../../results/HER2_analysis/her2_expr_filtered.rds")
# Extract transcript IDs (remove version numbers)
her2_available_transcripts <- her2_expr %>%
mutate(transcript_clean = str_remove(sample, "\\..*")) %>%
pull(transcript_clean) %>%
unique()
# Filter sequences to those with expression data
sequence_names_clean <- str_remove(names(her2_sequences_all), "\\..*")
keep_indices <- sequence_names_clean %in% her2_available_transcripts
her2_sequences_filtered <- her2_sequences_all[keep_indices]
cat("Sequences with expression data:", length(her2_sequences_filtered), "\n")## Sequences with expression data: 102.4 Step 4: Export for External Tools
Save sequences in FASTA format for external prediction tools (DeepLoc2 for subcellular localization, DeepTMHMM for membrane topology).
fasta_file <- "../../data/her2_isoforms_filtered.fasta"
writeXStringSet(her2_sequences_filtered, filepath = fasta_file)
cat("✓ FASTA saved:", fasta_file, "\n\n")
cat("Upload to:\n")
cat(" • DeepLoc2: https://services.healthtech.dtu.dk/services/DeepLoc-2.1/\n")
cat(" • DeepTMHMM: https://dtu.biolib.com/DeepTMHMM\n")3 Phase 2: Structural Characterization
3.1 Step 5: Align Isoforms with MAFFT
Align sequences using MAFFT with the --localpair
algorithm, which is optimized for sequences with large
insertions/deletions from alternative splicing. This ensures proper
alignment of conserved functional domains.
temp_input <- tempfile(fileext = ".fasta")
temp_output <- tempfile(fileext = ".fasta")
writeXStringSet(her2_sequences_filtered, temp_input)
# MAFFT alignment optimized for isoforms
system2("mafft",
args = c("--localpair", "--maxiterate", "1000", "--quiet", temp_input),
stdout = temp_output)
her2_aligned <- readAAStringSet(temp_output)
unlink(c(temp_input, temp_output))
cat("Alignment width:", unique(width(her2_aligned)), "positions\n")## Alignment width: 1294 positions3.2 Step 6: Import and Process Predictions
Import external prediction results and perform integrated analysis: classify isoforms by subcellular localization, map membrane topology to aligned sequences, and identify therapeutic epitope retention across all isoforms.
# ===== Import DeepLoc2 Results =====
deeploc_results <- read_csv("../../results/HER2_analysis/HER2_deeploc21_filtered.csv",
show_col_types = FALSE)
deeploc_filtered <- deeploc_results %>%
mutate(transcript_clean = str_remove(Protein_ID, "\\..*")) %>%
filter(transcript_clean %in% her2_available_transcripts)
# ===== Import DeepTMHMM Results =====
parse_deeptmhmm <- function(file_path) {
lines <- readLines(file_path)
results <- list()
i <- 1
while (i <= length(lines)) {
if (startsWith(lines[i], ">")) {
transcript_id <- str_extract(lines[i], "(?<=>)[^ ]+")
sequence <- lines[i + 1]
topology <- lines[i + 2]
results[[transcript_id]] <- list(
transcript_id = transcript_id,
sequence = sequence,
topology = topology
)
i <- i + 3
} else {
i <- i + 1
}
}
return(results)
}
deeptmhmm_results <- parse_deeptmhmm(
"../../results/HER2_analysis/HER2_deeptmhmm_predicted_topologies_filtered.3line"
)
# ===== Classify Isoforms by Localization =====
transcript_classification <- deeploc_filtered %>%
dplyr::select(Protein_ID, `Cell membrane`, Extracellular) %>%
mutate(
transcript_clean = str_remove(Protein_ID, "\\..*"),
classification = case_when(
`Cell membrane` > Extracellular ~ "Membrane-bound",
Extracellular > `Cell membrane` ~ "Secreted",
TRUE ~ "Ambiguous"
)
)
classification_colors <- c(
"Membrane-bound" = "#E74C3C",
"Secreted" = "#3498DB",
"Ambiguous" = "#95A5A6"
)
# ===== Map Topology to Alignment =====
map_topology_to_alignment <- function(deeptmhmm_results, aligned_sequences) {
map_dfr(names(deeptmhmm_results), function(tid) {
topology <- deeptmhmm_results[[tid]]$topology
aligned_seq <- as.character(aligned_sequences[tid])
topology_chars <- strsplit(topology, "")[[1]]
aligned_chars <- strsplit(aligned_seq, "")[[1]]
aligned_topology <- character(length(aligned_chars))
# Transfer topology to aligned positions
original_pos <- 1
for (aligned_pos in 1:length(aligned_chars)) {
if (aligned_chars[aligned_pos] == "-") {
aligned_topology[aligned_pos] <- "-"
} else {
if (original_pos <= length(topology_chars)) {
aligned_topology[aligned_pos] <- topology_chars[original_pos]
}
original_pos <- original_pos + 1
}
}
data.frame(
position = 1:length(aligned_chars),
aa = aligned_chars,
topology = aligned_topology,
transcript_id = tid
) %>%
mutate(
topology_type = case_when(
topology == "S" ~ "Signal peptide",
topology == "O" ~ "Extracellular",
topology == "M" ~ "Transmembrane",
topology == "I" ~ "Intracellular",
topology == "-" ~ "Alignment gap",
TRUE ~ "Unknown"
)
)
})
}
aligned_segments <- map_topology_to_alignment(deeptmhmm_results, her2_aligned)
# ===== Map Epitopes Across Isoforms =====
# Therapeutic antibody epitope positions in canonical HER2
epitope_positions <- list(
P = list(c(257, 267), c(308, 318)), # Pertuzumab
T = list(c(580, 582), c(593, 595), c(610, 625)) # Trastuzumab
)
map_epitopes_from_alignment <- function(aligned_seqs, canonical_name, epitope_pos) {
canonical_seq <- as.character(aligned_seqs[[canonical_name]])
epitope_map_list <- list()
for(seq_name in names(aligned_seqs)) {
isoform_seq <- as.character(aligned_seqs[[seq_name]])
canonical_ungapped_pos <- 0
epitope_positions_aligned <- list()
for(i in 1:nchar(canonical_seq)) {
canon_char <- substr(canonical_seq, i, i)
iso_char <- substr(isoform_seq, i, i)
if(canon_char != "-") canonical_ungapped_pos <- canonical_ungapped_pos + 1
# Check if position is in epitope and isoform has residue
for(epitope_name in names(epitope_pos)) {
for(region in epitope_pos[[epitope_name]]) {
if(canonical_ungapped_pos >= region[1] &&
canonical_ungapped_pos <= region[2] &&
iso_char != "-") {
epitope_positions_aligned[[length(epitope_positions_aligned) + 1]] <- data.frame(
epitope = epitope_name,
position = i,
canonical_pos = canonical_ungapped_pos
)
}
}
}
}
if(length(epitope_positions_aligned) > 0) {
epitope_map_list[[seq_name]] <- bind_rows(epitope_positions_aligned)
}
}
bind_rows(epitope_map_list, .id = "transcript_id")
}
epitope_mapping <- map_epitopes_from_alignment(
aligned_seqs = her2_aligned,
canonical_name = "ENST00000269571",
epitope_pos = epitope_positions
)
# Summarize epitope retention
epitope_summary <- epitope_mapping %>%
group_by(transcript_id, epitope) %>%
summarise(n_positions = n(), .groups = "drop") %>%
pivot_wider(names_from = epitope, values_from = n_positions, values_fill = 0)
print(epitope_summary)## # A tibble: 9 × 3
## transcript_id P T
## <chr> <int> <int>
## 1 ENST00000269571 22 22
## 2 ENST00000445658 11 22
## 3 ENST00000578199 22 22
## 4 ENST00000578502 22 22
## 5 ENST00000580074 0 10
## 6 ENST00000582818 0 22
## 7 ENST00000584014 22 22
## 8 ENST00000584450 22 22
## 9 ENST00000584601 22 223.3 Step 7: Visualize Integrated Results
Create comprehensive visualization combining subcellular localization (DeepLoc2), membrane topology (DeepTMHMM), and epitope locations in a single integrated figure.
# Define isoform display order
transcript_order <- c(
"ENST00000580074",
"ENST00000578709",
"ENST00000584601",
"ENST00000584450",
"ENST00000584014",
"ENST00000582818",
"ENST00000578502", # Longest
"ENST00000578199", # Secreted
"ENST00000445658", # Truncated
"ENST00000269571" # Canonical
)
# ===== DeepLoc2 Heatmap =====
localization_cols <- c("Cell membrane", "Cytoplasm", "Endoplasmic reticulum",
"Extracellular", "Golgi apparatus", "Lysosome/Vacuole",
"Mitochondrion", "Nucleus", "Peroxisome", "Plastid")
plot_data_combined <- deeploc_filtered %>%
mutate(Protein_ID = factor(Protein_ID, levels = transcript_order)) %>%
dplyr::select(Protein_ID, all_of(localization_cols)) %>%
pivot_longer(cols = -Protein_ID, names_to = "localization", values_to = "probability")
deeploc_heatmap <- ggplot(plot_data_combined,
aes(x = localization, y = Protein_ID, fill = probability)) +
geom_tile(color = "white", linewidth = 0.5) +
scale_fill_gradientn(
colors = c("#4A90B8", "#5E9DC3", "#FFFACD", "#F26D5E", "#D73027"),
values = c(0, 0.2, 0.5, 0.8, 1),
limits = c(0, 1)
) +
labs(title = "HER2 Isoforms", x = NULL, y = NULL, fill = "Probability") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1, size = 16),
axis.text.y = element_text(size = 16, family = "mono"),
plot.title = element_text(size = 18, face = "bold"),
legend.text = element_text(size = 10),
legend.title = element_text(size = 16),
legend.position = "top",
panel.grid = element_blank()
)
# ===== Topology Plot with Epitopes =====
plot_topology_with_epitopes <- function(aligned_segments, epitope_data, transcript_order) {
topology_colors <- c(
"Signal peptide" = "#C41C24",
"Extracellular" = "#FFB20F",
"Transmembrane" = "#18848C",
"Intracellular" = "#96BDC6",
"Alignment gap" = "#EDE7E3"
)
epitope_colors <- c("P" = "#55a630", "T" = "#ff5d8f")
aligned_segments <- aligned_segments %>%
mutate(transcript_id = factor(transcript_id, levels = transcript_order))
# Base topology plot
p <- ggplot(aligned_segments, aes(x = position, y = transcript_id, fill = topology_type)) +
geom_tile(height = 0.8, width = 1) +
scale_fill_manual(values = topology_colors, name = "Topology") +
scale_x_continuous(expand = c(0, 0), breaks = seq(0, max(aligned_segments$position), 400)) +
labs(x = "Amino acid position (aligned)", y = NULL) +
theme_minimal() +
theme(
axis.text.y = element_blank(),
axis.text.x = element_text(size = 16),
axis.title.x = element_text(size = 18),
legend.position = "right",
legend.text = element_text(size = 16),
legend.title = element_text(size = 18),
panel.grid = element_blank()
)
# Add epitope overlay
if(!is.null(epitope_data) && nrow(epitope_data) > 0) {
epitope_data <- epitope_data %>%
mutate(transcript_id = factor(transcript_id, levels = transcript_order))
p <- p +
new_scale_fill() +
geom_tile(data = epitope_data,
aes(x = position, y = transcript_id, fill = epitope),
height = 0.8, width = 1, alpha = 0.9, inherit.aes = FALSE) +
scale_fill_manual(
values = epitope_colors,
name = "Epitope",
labels = c("P" = "Pertuzumab", "T" = "Trastuzumab")
)
}
return(p)
}
topology_plot <- plot_topology_with_epitopes(aligned_segments, epitope_mapping, transcript_order)
# ===== Combined Plot =====
combined_plot <- deeploc_heatmap + topology_plot +
plot_layout(widths = c(1, 1.5))
print(combined_plot)
4 Phase 3: Expression Analysis
4.1 Step 8: Prepare Expression Data
Filter to targetable isoforms (membrane-bound with retained epitopes) and merge with tissue annotations from TCGA/GTEx.
# Targetable isoforms based on topology and epitope analysis
targetable_isoforms <- c("ENST00000269571", "ENST00000584601", "ENST00000584450",
"ENST00000584014", "ENST00000578502", "ENST00000578199",
"ENST00000445658", "ENST00000582818")
# Load tissue annotations
phenotype <- read_tsv("../../data/HER2_TCGA_GTEX_category_2026.txt", show_col_types = FALSE) %>%
separate(TCGA_GTEX_main_category,
into = c("dataset", "tissue_or_disease"),
sep = " ", extra = "merge") %>%
mutate(tissue_or_disease = tolower(tissue_or_disease))
# Transform and filter expression data
her2_expr_long <- her2_expr %>%
pivot_longer(cols = -sample, names_to = "sample_id", values_to = "log2_tpm001") %>%
dplyr::rename(transcript_id = sample) %>%
mutate(
transcript_clean = str_remove(transcript_id, "\\..*"),
# Convert log2(TPM + 0.001) to log2(TPM + 1)
tpm = 2^log2_tpm001 - 0.001,
tpm = pmax(tpm, 0),
log2_tpm_plus1 = log2(tpm + 1)
) %>%
filter(transcript_clean %in% targetable_isoforms)
# Merge with phenotype and classification
her2_expr_final <- her2_expr_long %>%
left_join(phenotype, by = c("sample_id" = "sample")) %>%
left_join(transcript_classification, by = "transcript_clean") %>%
filter(!is.na(dataset))4.2 Step 9: Visualize Expression Patterns
Compare expression across normal tissues (GTEx) and cancer types (TCGA) to assess tumor selectivity and on-target/off-tumor toxicity risk.
# Categorize normal tissues by clinical importance
tissue_categories <- list(
"Critical" = c("brain", "blood", "blood vessel", "heart", "liver", "lung", "nerve"),
"Important" = c("pancreas", "kidney", "stomach", "colon", "small intestine", "bladder"),
"Others" = c("spleen", "uterus", "ovary", "testis", "prostate", "breast", "skin")
)
tissue_category_df <- data.frame(
tissue_or_disease = unlist(tissue_categories),
category = rep(names(tissue_categories), sapply(tissue_categories, length))
) %>%
mutate(category = factor(category, levels = c("Critical", "Important", "Others")))
# ===== GTEx Data =====
gtex_data <- her2_expr_final %>%
filter(dataset == "GTEX") %>%
left_join(tissue_category_df, by = "tissue_or_disease") %>%
filter(!is.na(category)) %>%
arrange(category, transcript_clean) %>%
group_by(category) %>%
mutate(position_within_cat = as.numeric(factor(transcript_clean))) %>%
ungroup() %>%
mutate(
gap_offset = case_when(
category == "Critical" ~ 0,
category == "Important" ~ 9,
category == "Others" ~ 18
),
x_position = gap_offset + position_within_cat
)
gtex_data <- gtex_data %>%
mutate(
group_label = paste0(category, "_", transcript_clean),
)
# ===== TCGA Data =====
tcga_cancers <- c("breast invasive carcinoma", "stomach adenocarcinoma", "glioblastoma multiforme")
tcga_data <- her2_expr_final %>%
filter(dataset == "TCGA", tissue_or_disease %in% tcga_cancers) %>%
arrange(tissue_or_disease, transcript_clean) %>%
group_by(tissue_or_disease) %>%
mutate(position_within_disease = as.numeric(factor(transcript_clean))) %>%
ungroup() %>%
mutate(
gap_offset = case_when(
tissue_or_disease == "breast invasive carcinoma" ~ 0,
tissue_or_disease == "stomach adenocarcinoma" ~ 9,
tissue_or_disease == "glioblastoma multiforme" ~ 18
),
x_position = gap_offset + position_within_disease + 27 # Add offset to place after GTEx (26 + 1 gap)
)
tcga_data <- tcga_data %>%
mutate(
group_label = paste0(tissue_or_disease, "_", transcript_clean),
)
# ===== Combined Visualization =====
# Combine datasets
combined_data <- bind_rows(
gtex_data %>%
dplyr::select(transcript_clean, log2_tpm_plus1, classification,
dataset, group_label, x_position),
tcga_data %>%
dplyr::select(transcript_clean, log2_tpm_plus1, classification,
dataset, group_label, x_position)
)
expression_plot <- ggplot(combined_data,
aes(x = x_position,
y = log2_tpm_plus1,
fill = classification,
group = interaction(dataset, group_label))) +
geom_boxplot(outlier.shape = NA) +
scale_fill_manual(values = classification_colors, name = "Localization") +
scale_x_continuous(
breaks = combined_data %>%
distinct(x_position, group_label) %>%
pull(x_position),
labels = combined_data %>%
distinct(x_position, group_label) %>%
pull(group_label)
) +
coord_cartesian(ylim = c(0, 7.5)) +
geom_vline(xintercept = 26.5, linetype = "dashed", color = "gray50", linewidth = 0.5) +
# Add dataset labels at the top
annotate("text", x = 13, y = max(combined_data$log2_tpm_plus1) * 1.05,
label = "GTEx (Normal Tissues)", fontface = "bold", size = 4) +
annotate("text", x = 40, y = max(combined_data$log2_tpm_plus1) * 1.05,
label = "TCGA (Cancer Types)", fontface = "bold", size = 4) +
labs(
title = "HER2 Isoform Expression Across Normal Tissues and Cancer Types",
subtitle = "GTEx tissues grouped by essentiality, followed by TCGA cancer types",
x = "Tissue/Cancer Type_Isoform",
y = "Expression (log2(TPM + 1))"
) +
theme_minimal() +
theme(
plot.margin = margin(t = 20, r = 10, b = 10, l = 40),
axis.text.x = element_text(angle = 45, hjust = 1, size = 7),
axis.text.y = element_text(size = 10),
axis.title = element_text(size = 12),
plot.title = element_text(size = 14, face = "bold"),
plot.subtitle = element_text(size = 11),
legend.position = "right",
legend.text = element_text(size = 10)
)
print(expression_plot)
5 Session Info
## R version 4.5.1 (2025-06-13)
## Platform: aarch64-apple-darwin20
## Running under: macOS Tahoe 26.2
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.1
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## time zone: Europe/Copenhagen
## tzcode source: internal
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] lubridate_1.9.4 forcats_1.0.1 stringr_1.6.0
## [4] dplyr_1.1.4 purrr_1.2.0 readr_2.1.6
## [7] tidyr_1.3.2 tibble_3.3.0 ggplot2_4.0.1
## [10] tidyverse_2.0.0 patchwork_1.3.2 ggnewscale_0.5.2
## [13] Biostrings_2.78.0 Seqinfo_1.0.0 XVector_0.50.0
## [16] IRanges_2.44.0 S4Vectors_0.48.0 BiocGenerics_0.56.0
## [19] generics_0.1.4 biomaRt_2.66.0
##
## loaded via a namespace (and not attached):
## [1] KEGGREST_1.50.0 gtable_0.3.6 xfun_0.55
## [4] bslib_0.9.0 httr2_1.2.2 Biobase_2.70.0
## [7] tzdb_0.5.0 vctrs_0.6.5 tools_4.5.1
## [10] parallel_4.5.1 curl_7.0.0 AnnotationDbi_1.72.0
## [13] RSQLite_2.4.5 blob_1.3.0 pkgconfig_2.0.3
## [16] dbplyr_2.5.1 RColorBrewer_1.1-3 S7_0.2.1
## [19] lifecycle_1.0.4 compiler_4.5.1 farver_2.1.2
## [22] progress_1.2.3 htmltools_0.5.9 sass_0.4.10
## [25] yaml_2.3.12 pillar_1.11.1 crayon_1.5.3
## [28] jquerylib_0.1.4 cachem_1.1.0 tidyselect_1.2.1
## [31] digest_0.6.39 stringi_1.8.7 labeling_0.4.3
## [34] fastmap_1.2.0 grid_4.5.1 cli_3.6.5
## [37] magrittr_2.0.4 utf8_1.2.6 withr_3.0.2
## [40] prettyunits_1.2.0 filelock_1.0.3 scales_1.4.0
## [43] rappdirs_0.3.3 bit64_4.6.0-1 timechange_0.3.0
## [46] rmarkdown_2.30 httr_1.4.7 bit_4.6.0
## [49] otel_0.2.0 png_0.1-8 hms_1.1.4
## [52] memoise_2.0.1 evaluate_1.0.5 knitr_1.51
## [55] BiocFileCache_3.0.0 rlang_1.1.6 glue_1.8.0
## [58] DBI_1.2.3 xml2_1.5.1 vroom_1.6.7
## [61] rstudioapi_0.17.1 jsonlite_2.0.0 R6_2.6.1