Section 6 Top candidate comparisons

6.1 Clustering analysis

6.1.1 Analysis

In R:

library(DT)
library(factoextra)
library(NbClust)
library(cluster)
library(ggplot2)
library(cowplot)

pfpv_data <- read.csv("./data/supplementary_data_5_pfpv_purf_oob_predictions.csv", row.names = 1, check.names = FALSE)
pfpv_data <- pfpv_data[order(-pfpv_data$`OOB score filtered`), ]
pfpv_threshold <- pfpv_data[pfpv_data$antigen_label == 1, ]$`OOB score filtered`[sum(pfpv_data$antigen_label == 1) / 2]
top_pf_pfpv <- rownames(pfpv_data[pfpv_data$species == "pf" & pfpv_data$antigen_label == 0 & pfpv_data$`OOB score filtered` >= pfpv_threshold, ])
top_pv_pfpv <- rownames(pfpv_data[pfpv_data$species == "pv" & pfpv_data$antigen_label == 0 & pfpv_data$`OOB score filtered` >= pfpv_threshold, ])

pfpv_prox <- read.csv("~/Downloads/pfpv_proximity_values.csv", check.names = FALSE)
pfpv_dist <- 1 - pfpv_prox
rownames(pfpv_dist) <- colnames(pfpv_dist)
pfpv_dist <- pfpv_dist[
  rownames(pfpv_dist) %in% c(top_pf_pfpv, top_pv_pfpv),
  colnames(pfpv_dist) %in% c(top_pf_pfpv, top_pv_pfpv)
]
rm(pfpv_prox)

# Gap statistic
gap_stat <- clusGap(pfpv_dist, K.max = 10, hcut, B = 100, hc_method = "ward.D2")
p0_1 <- fviz_gap_stat(gap_stat, maxSE = list(method = "Tibs2001SEmax", SE.factor = 2)) +
  labs(title = "Gap statistic method")

# Silhouette method
p0_2 <- fviz_nbclust(pfpv_dist, k.max = 10, hcut, hc_method = "ward.D2", method = "silhouette") +
  labs(title = "Silhouette method")

# Elbow method
p0_3 <- fviz_nbclust(pfpv_dist, k.max = 10, hcut, hc_method = "ward.D2", method = "wss") +
  geom_vline(xintercept = 3, linetype = 2, color = "steelblue") +
  labs(title = "Elbow method")

save(p0_1, p0_2, p0_3, file = "./rdata/top_candidate_optimal_num_clusters.RData")

library(umap)
library(ggplot2)
library(ggrepel)
library(cowplot)

top_pfpv_hc <- hclust(as.dist(pfpv_dist), method = "ward.D2")
top_pfpv_2_clusters <- cutree(top_pfpv_hc, k = 2)
top_pfpv_3_clusters <- cutree(top_pfpv_hc, k = 3)
top_pfpv_2_clusters[top_pfpv_2_clusters == 1] <- 4
top_pfpv_2_clusters[top_pfpv_2_clusters == 2] <- 3
top_pfpv_2_clusters <- top_pfpv_2_clusters - 2
top_pfpv_3_clusters[top_pfpv_3_clusters == 1] <- 5
top_pfpv_3_clusters[top_pfpv_3_clusters == 2] <- 6
top_pfpv_3_clusters[top_pfpv_3_clusters == 3] <- 4
top_pfpv_3_clusters <- top_pfpv_3_clusters - 3
save(top_pfpv_2_clusters, top_pfpv_3_clusters,
  file = "./rdata/top_candidate_clusters.RData"
)

clusters <- top_pfpv_2_clusters
data <- read.csv("./data/supplementary_data_5_pfpv_purf_oob_predictions.csv", row.names = 1, check.names = FALSE)
load("./rdata/pfpv_prox_mds_umap.RData")
umap_df_ <- umap_df
umap_df$label <- data$antigen_label
umap_df <- rbind(umap_df[umap_df$label == 1, ], umap_df[rownames(umap_df) %in% names(clusters), ])
umap_df$group <- ""
umap_df$group[umap_df$label == 1] <- "Known antigen"
umap_df$group[rownames(umap_df) %in% c("PF3D7_0304600.1-p1", "PF3D7_0424100.1-p1", "PF3D7_0206900.1-p1", "PF3D7_0209000.1-p1")] <- "Reference antigen"
umap_df$group[rownames(umap_df) %in% names(clusters)[clusters == 1]] <- "Group 1"
umap_df$group[rownames(umap_df) %in% names(clusters)[clusters == 2]] <- "Group 2"
umap_df_text <- umap_df[umap_df$label == 1, ]
umap_df_text$label_text <- ""
umap_df_text$label_text[rownames(umap_df_text) == "PF3D7_0304600.1-p1"] <- "PfCSP"
umap_df_text$label_text[rownames(umap_df_text) == "PF3D7_0424100.1-p1"] <- "RH5"
umap_df_text$label_text[rownames(umap_df_text) == "PF3D7_0206900.1-p1"] <- "MSP5"
umap_df_text$label_text[rownames(umap_df_text) == "PF3D7_0209000.1-p1"] <- "P230"
umap_df_text$label_text[rownames(umap_df_text) == "PVP01_0835600.1-p1"] <- "PvCSP"
umap_df_text$label_text[rownames(umap_df_text) == "PVP01_0623800.1-p1"] <- "DBP"
umap_df_text$label_text[rownames(umap_df_text) == "PVP01_0728900.1-p1"] <- "MSP1"

umap_p1 <- ggplot() +
  geom_point(data = umap_df_, aes(x = X1, y = X2), color = "grey90", alpha = 0.5) +
  geom_point(
    data = umap_df[umap_df$label == 1, ], aes(x = X1, y = X2), shape = 21, color = "black",
    fill = "#FFFF33", stroke = 0.3, size = 2, alpha = 0.5
  ) +
  geom_point(data = umap_df[umap_df$label == 0, ], aes(x = X1, y = X2, fill = group), shape = 21, alpha = 0.5) +
  geom_text_repel(
    data = umap_df_text, aes(x = X1, y = X2, label = label_text), size = 3.5,
    nudge_x = 1.5, nudge_y = -1, point.padding = 0.1
  ) +
  scale_fill_manual(breaks = c("Group 1", "Group 2"), values = c("#03a1fc", "#984EA3")) +
  theme_bw() +
  theme(
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
    plot.margin = ggplot2::margin(5, 5, 5, 5, "pt"),
    axis.title = element_text(colour = "black"),
    axis.text = element_text(colour = "black"),
    legend.position = "bottom"
  ) +
  guides(fill = guide_legend(title = "")) +
  xlab("Dimension 1") +
  ylab("Dimension 2")

clusters <- top_pfpv_3_clusters
data <- read.csv("./data/supplementary_data_5_pfpv_purf_oob_predictions.csv", row.names = 1, check.names = FALSE)
load("./rdata/pfpv_prox_mds_umap.RData")
umap_df_ <- umap_df
umap_df$label <- data$antigen_label
umap_df <- rbind(umap_df[umap_df$label == 1, ], umap_df[rownames(umap_df) %in% names(clusters), ])
umap_df$group <- ""
umap_df$group[umap_df$label == 1] <- "Known antigen"
umap_df$group[rownames(umap_df) %in% c("PF3D7_0304600.1-p1", "PF3D7_0424100.1-p1", "PF3D7_0206900.1-p1", "PF3D7_0209000.1-p1")] <- "Reference antigen"
umap_df$group[rownames(umap_df) %in% names(clusters)[clusters == 1]] <- "Group 1"
umap_df$group[rownames(umap_df) %in% names(clusters)[clusters == 2]] <- "Group 2"
umap_df$group[rownames(umap_df) %in% names(clusters)[clusters == 3]] <- "Group 3"
umap_df_text <- umap_df[umap_df$label == 1, ]
umap_df_text$label_text <- ""
umap_df_text$label_text[rownames(umap_df_text) == "PF3D7_0304600.1-p1"] <- "PfCSP"
umap_df_text$label_text[rownames(umap_df_text) == "PF3D7_0424100.1-p1"] <- "RH5"
umap_df_text$label_text[rownames(umap_df_text) == "PF3D7_0206900.1-p1"] <- "MSP5"
umap_df_text$label_text[rownames(umap_df_text) == "PF3D7_0209000.1-p1"] <- "P230"
umap_df_text$label_text[rownames(umap_df_text) == "PVP01_0835600.1-p1"] <- "PvCSP"
umap_df_text$label_text[rownames(umap_df_text) == "PVP01_0623800.1-p1"] <- "DBP"
umap_df_text$label_text[rownames(umap_df_text) == "PVP01_0728900.1-p1"] <- "MSP1"

umap_p2 <- ggplot() +
  geom_point(data = umap_df_, aes(x = X1, y = X2), color = "grey90", alpha = 0.5) +
  geom_point(
    data = umap_df[umap_df$label == 1, ], aes(x = X1, y = X2), shape = 21, color = "black",
    fill = "#FFFF33", stroke = 0.3, size = 2, alpha = 0.5
  ) +
  geom_point(data = umap_df[umap_df$label == 0, ], aes(x = X1, y = X2, fill = group), shape = 21, alpha = 0.5) +
  geom_text_repel(
    data = umap_df_text, aes(x = X1, y = X2, label = label_text), size = 3.5,
    nudge_x = 1.5, nudge_y = -1, point.padding = 0.1
  ) +
  scale_fill_manual(
    breaks = c("Group 1", "Group 2", "Group 3"),
    values = c("#03a1fc", "#984EA3", "#FF7F00")
  ) +
  theme_bw() +
  theme(
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
    plot.margin = ggplot2::margin(5, 5, 5, 5, "pt"),
    axis.title = element_text(colour = "black"),
    axis.text = element_text(colour = "black"),
    legend.position = "bottom"
  ) +
  guides(fill = guide_legend(title = "")) +
  xlab("Dimension 1") +
  ylab("Dimension 2")

umap_p_combined <- plot_grid(umap_p1, umap_p2, nrow = 1, labels = c("a", "b"))
umap_p_combined

load(file = "./rdata/top_candidate_clusters.RData")
species <- c(rep("pf", 145), rep("pv", 45))
cat("Two clusters\n")

## Two clusters

table(top_pfpv_2_clusters, species)

##                    species
## top_pfpv_2_clusters  pf  pv
##                   1   0  35
##                   2 145  10

cat("Three clusters\n")

## Three clusters

table(top_pfpv_3_clusters, species)

##                    species
## top_pfpv_3_clusters  pf  pv
##                   1   0  35
##                   2 107   9
##                   3  38   1

protein_id_1 <- names(top_pfpv_3_clusters)[top_pfpv_3_clusters == 1]
protein_id_2 <- names(top_pfpv_3_clusters)[top_pfpv_3_clusters == 2]
protein_id_3 <- names(top_pfpv_3_clusters)[top_pfpv_3_clusters == 3]
save(protein_id_1, protein_id_2, protein_id_3, file = "./rdata/clustering_groups.RData")

gene_accession_1 <- str_replace_all(protein_id_1, "\\.[1-9]-p1", "")
gene_accession_2 <- str_replace_all(protein_id_2, "\\.[1-9]-p1", "")
gene_accession_3 <- str_replace_all(protein_id_3, "\\.[1-9]-p1", "")

write.table(data.frame("acc" = gene_accession_1),
  sep = ", ", row.names = FALSE, col.names = FALSE,
  file = "./other_data/top_candidates_gene_accession_1.csv"
)
write.table(data.frame("acc" = gene_accession_2),
  sep = ", ", row.names = FALSE, col.names = FALSE,
  file = "./other_data/top_candidates_gene_accession_2.csv"
)
write.table(data.frame("acc" = gene_accession_3),
  sep = ", ", row.names = FALSE, col.names = FALSE,
  file = "./other_data/top_candidates_gene_accession_3.csv"
)

6.2 Gene ontology analysis

6.2.1 Analysis

In Bash:

python ./other_data/goea.py -s "./other_data/top_candidates_gene_accession_1.csv" \
                            -p "./other_data/PlasmoDB-62_pfpv_GO.gaf" \
                            -n "./other_data/go-basic.obo" \
                            -o "./other_data/goea_result_1.xlsx"
                            
python ./other_data/goea.py -s "./other_data/top_candidates_gene_accession_2.csv" \
                            -p "./other_data/PlasmoDB-62_pfpv_GO.gaf" \
                            -n "./other_data/go-basic.obo" \
                            -o "./other_data/goea_result_2.xlsx"
                            
python ./other_data/goea.py -s "./other_data/top_candidates_gene_accession_3.csv" \
                            -p "./other_data/PlasmoDB-62_pfpv_GO.gaf" \
                            -n "./other_data/go-basic.obo" \
                            -o "./other_data/goea_result_3.xlsx"

6.2.2 Plotting

In R:

library(ggplot2)
library(gridExtra)
library(shadowtext)
library(ggforce)
library(reshape)
library(cowplot)
library(scales)

process_goea_res <- function(file_name) {
  data <- read.csv(file_name, header = TRUE, fill = TRUE, quote = '\"', stringsAsFactors = FALSE)
  # calculate percentage from the ratio column
  data$num_genes <- apply(data, 1, function(x) as.integer(unlist(strsplit(x["ratio_in_study"], "/"))[1]))
  # select for enrichment (e) data rows
  data <- data[data$enrichment == "e", ]

  # select for biological process (BP) data rows
  res_bp <- data[data$NS == "BP", ]
  res_bp$`-Log10FDR` <- -log10(res_bp$p_fdr_bh)
  res_bp <- res_bp[order(res_bp$`-Log10FDR`), ]
  res_bp$group <- rep("Biological process", nrow(res_bp))

  # select for cellular component (CC) data rows
  res_cc <- data[data$NS == "CC", ]
  res_cc$`-Log10FDR` <- -log10(res_cc$p_fdr_bh)
  res_cc <- res_cc[order(res_cc$`-Log10FDR`), ]
  res_cc$group <- rep("Cellular component", nrow(res_cc))

  # select for molecular function (MF) data rows
  res_mf <- data[data$NS == "MF", ]
  res_mf$`-Log10FDR` <- -log10(res_mf$p_fdr_bh)
  res_mf <- res_mf[order(res_mf$`-Log10FDR`), ]
  res_mf$group <- rep("Molecular function", nrow(res_mf))

  ds <- do.call(rbind, list(res_mf, res_cc, res_bp))
  ds$name <- factor(ds$name, levels = ds$name)
  ds$group <- factor(ds$group, levels = c("Biological process", "Cellular component", "Molecular function"))

  return(ds)
}

ds_1 <- process_goea_res("./other_data/goea_result_1.csv")
ds_2 <- process_goea_res("./other_data/goea_result_2.csv")
ds_3 <- process_goea_res("./other_data/goea_result_3.csv")

p1 <- ggplot(ds_1, aes(x = name, y = `-Log10FDR`)) +
  geom_col(width = 0.05) +
  geom_point(size = 5, shape = 21, color = "black", fill = "grey20") +
  geom_text(aes(label = sprintf("%.0f", num_genes)), nudge_y = 0, size = 2.5, color = "white") +
  scale_x_discrete(labels = label_wrap(50)) +
  coord_flip() +
  ggforce::facet_col(facets = vars(group), scales = "free_y", space = "free") +
  theme_bw() +
  theme(
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
    panel.spacing = unit(0, "pt"),
    strip.background = element_blank(),
    panel.border = element_rect(colour = "black", size = 0.5),
    plot.title = element_text(hjust = 0.5),
    plot.margin = ggplot2::margin(5, 5, 85, 5, "pt"),
    legend.text = element_text(colour = "black", ),
    axis.title.x = element_text(colour = "black", ),
    axis.title.y = element_text(colour = "black", ),
    axis.text.x = element_text(colour = "black", ),
    axis.text.y = element_text(colour = "black", )
  ) +
  xlab("") +
  ylab(expression("-Log"[10] * "FDR")) +
  ggtitle("Group 1 (35 candidates)")

p2 <- ggplot(ds_2, aes(x = name, y = `-Log10FDR`)) +
  geom_col(width = 0.05) +
  geom_point(size = 5, shape = 21, color = "black", fill = "grey20") +
  geom_text(aes(label = sprintf("%.0f", num_genes)), nudge_y = 0, size = 2.5, color = "white") +
  scale_x_discrete(labels = label_wrap(50)) +
  coord_flip() +
  ggforce::facet_col(facets = vars(group), scales = "free_y", space = "free") +
  theme_bw() +
  theme(
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
    panel.spacing = unit(0, "pt"),
    strip.background = element_blank(),
    panel.border = element_rect(colour = "black", size = 0.5),
    plot.title = element_text(hjust = 0.5),
    plot.margin = ggplot2::margin(5, 0, 5, 5, "pt"),
    legend.text = element_text(colour = "black", ),
    axis.title.x = element_text(colour = "black", ),
    axis.title.y = element_text(colour = "black", ),
    axis.text.x = element_text(colour = "black", ),
    axis.text.y = element_text(colour = "black", )
  ) +
  xlab("") +
  ylab(expression("-Log"[10] * "FDR")) +
  ggtitle("Group 2 (116 candidates)")

p3 <- ggplot(ds_3, aes(x = name, y = `-Log10FDR`)) +
  geom_col(width = 0.05) +
  geom_point(size = 5, shape = 21, color = "black", fill = "grey20") +
  geom_text(aes(label = sprintf("%.0f", num_genes)), nudge_y = 0, size = 2.5, color = "white") +
  scale_x_discrete(labels = label_wrap(50)) +
  coord_flip() +
  ggforce::facet_col(facets = vars(group), scales = "free_y", space = "free") +
  theme_bw() +
  theme(
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
    panel.spacing = unit(0, "pt"),
    strip.background = element_blank(),
    panel.border = element_rect(colour = "black", size = 0.5),
    plot.title = element_text(hjust = 0.5),
    plot.margin = ggplot2::margin(5, 5, 5, 0, "pt"),
    legend.text = element_text(colour = "black", ),
    axis.title.x = element_text(colour = "black", ),
    axis.title.y = element_text(colour = "black", ),
    axis.text.x = element_text(colour = "black", ),
    axis.text.y = element_text(colour = "black", )
  ) +
  xlab("") +
  ylab(expression("-Log"[10] * "FDR")) +
  ggtitle("Group 3 (39 candidates)")

Final plot

p_combined <- plot_grid(p1, p2, p3, nrow = 1, rel_widths = c(0.3, 0.3, 0.3), labels = c("a", "b", "c"))
p_combined

6.3 Candidate antigen characterization

6.3.1 Processing table

In R:

library(tibble)

prediction <- read.csv("./data/supplementary_data_5_pfpv_purf_oob_predictions.csv", row.names = 1, check.names = FALSE)
prediction <- prediction[order(-prediction$`OOB score filtered`), ]
threshold <- prediction[prediction$antigen_label == 1, ]$`OOB score filtered`[sum(prediction$antigen_label == 1) / 2]
labeled_pos <- rownames(prediction)[prediction$antigen_label == 1]
top_pfpv <- rownames(prediction[prediction$antigen_label == 0 & prediction$`OOB score filtered` >= threshold, ])

pfpv_prox <- read.csv("~/Downloads/pfpv_proximity_values.csv", check.names = FALSE)
pfpv_dist <- 1 - pfpv_prox
rm(pfpv_prox)
rownames(pfpv_dist) <- colnames(pfpv_dist)
pfpv_dist <- pfpv_dist[, labeled_pos]

ds <- prediction[c(labeled_pos, top_pfpv), "OOB score filtered", drop = FALSE]
ds <- rownames_to_column(ds, "Protein ID")
colnames(ds)[2] <- "Score"

# Add clustering groups
load(file = "./rdata/clustering_groups.RData")
ds$Group <- ""
ds$Group[ds$`Protein ID` %in% protein_id_1] <- "Group 1"
ds$Group[ds$`Protein ID` %in% protein_id_2] <- "Group 2"
ds$Group[ds$`Protein ID` %in% protein_id_3] <- "Group 3"
ds$Group[ds$`Protein ID` %in% labeled_pos] <- "Known antigen"
ds$Group[ds$`Protein ID` %in% c(
  "PF3D7_0304600.1-p1", "PF3D7_0424100.1-p1",
  "PF3D7_0206900.1-p1", "PF3D7_0209000.1-p1",
  "PVP01_0835600.1-p1", "PVP01_0623800.1-p1",
  "PVP01_0728900.1-p1"
)] <- "Reference antigen"

# Add gene products
gene_products <- rbind(
  read.csv("./other_data/pf3d7_gene_products_v62.csv"),
  read.csv("./other_data/pvp01_gene_products_v62.csv")
)
colnames(gene_products) <- c("Protein ID", "Gene product")
ds <- merge(x = ds, y = gene_products, by = "Protein ID", all.x = TRUE)

# Add closest reference antigen and the distance
pfpv_ad_ctrl_res <- read.csv("./other_data/pfpv_ad_ctrl_res.csv", check.names = FALSE, row.names = 1)
ds$`Closest known antigen` <- ""
ds$`Known antigen source` <- "Intersect"
ds$`Closest distance` <- -1
for (i in 1:nrow(ds)) {
  prot <- ds$`Protein ID`[i]
  tmp <- pfpv_dist[prot, ]
  ds[i, "Closest known antigen"] <- names(tmp)[which.min(tmp)]
  ds[i, "Closest known antigen"] <- paste0(
    ds[i, "Closest known antigen"], " (",
    gene_products[
      gene_products$`Protein ID` == ds[i, "Closest known antigen"],
      "Gene product"
    ], ")"
  )
  if (names(tmp)[which.min(tmp)] %in% c(
    "PF3D7_0304600.1-p1", "PF3D7_0424100.1-p1",
    "PF3D7_0206900.1-p1", "PF3D7_0209000.1-p1",
    "PVP01_0835600.1-p1", "PVP01_0623800.1-p1",
    "PVP01_0728900.1-p1"
  )) {
    ds[i, "Known antigen source"] <- "Reference"
  } else {
    ds[i, "Known antigen source"] <- pfpv_ad_ctrl_res[names(tmp)[which.min(tmp)], "source"]
  }
  ds[i, "Closest distance"] <- tmp[which.min(tmp)]
}

# Sort based on scores
ds <- ds[order(-ds$Score), ]
write.csv(ds, file = "./data/supplementary_data_6_antigen_characterization.csv", row.names = FALSE)

sessionInfo()

## R version 4.2.3 (2023-03-15)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur ... 10.16
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] tibble_3.2.1     scales_1.2.1     reshape_0.8.9    ggforce_0.4.1   
##  [5] shadowtext_0.1.2 gridExtra_2.3    ggrepel_0.9.3    umap_0.2.10.0   
##  [9] cowplot_1.1.1    cluster_2.1.4    NbClust_3.0.1    factoextra_1.0.7
## [13] ggplot2_3.4.2    DT_0.27         
## 
## loaded via a namespace (and not attached):
##  [1] Rcpp_1.0.10       lattice_0.21-8    png_0.1-8         digest_0.6.31    
##  [5] utf8_1.2.3        RSpectra_0.16-1   R6_2.5.1          plyr_1.8.8       
##  [9] evaluate_0.21     highr_0.10        pillar_1.9.0      rlang_1.1.1      
## [13] rstudioapi_0.14   jquerylib_0.1.4   R.utils_2.12.2    R.oo_1.25.0      
## [17] Matrix_1.5-4      reticulate_1.28   rmarkdown_2.21    styler_1.9.1     
## [21] htmlwidgets_1.6.2 polyclip_1.10-4   munsell_0.5.0     compiler_4.2.3   
## [25] xfun_0.39         pkgconfig_2.0.3   askpass_1.1       htmltools_0.5.5  
## [29] openssl_2.0.6     tidyselect_1.2.0  bookdown_0.34     codetools_0.2-19 
## [33] fansi_1.0.4       dplyr_1.1.2       withr_2.5.0       MASS_7.3-60      
## [37] R.methodsS3_1.8.2 grid_4.2.3        jsonlite_1.8.4    gtable_0.3.3     
## [41] lifecycle_1.0.3   magrittr_2.0.3    cli_3.6.1         cachem_1.0.8     
## [45] farver_2.1.1      bslib_0.4.2       generics_0.1.3    vctrs_0.6.2      
## [49] tools_4.2.3       R.cache_0.16.0    glue_1.6.2        tweenr_2.0.2     
## [53] purrr_1.0.1       fastmap_1.1.1     yaml_2.3.7        colorspace_2.1-0 
## [57] knitr_1.42        sass_0.4.6