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benchmark_interpretation.R
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source("utils.R")
# CTCL
# Load and format Kasumi results
misty.results <- read_rds("CTCLct400.rds")
sm.repr <- sm_labels(misty.results, 0.4, 0.9)
sm.repr.ext <- sm_labels(misty.results, 0.4, 0.9, freq = FALSE)
freq_all_clusters <- sapply(sm.repr.ext,
function(x) x %>%
select(-id, -x, -y) %>%
colSums)
# Compile cluster stats
cluster_stats <- data.frame(samples = rowSums(freq_all_clusters > 0))
cluster_stats$names <- sm.repr.ext[[1]] %>%
select(-id, -x, -y) %>%
names
cluster_stats$windows <- rowSums(freq_all_clusters)
cluster_stats$selected <- cluster_stats$names %in% names(sm.repr)
# Number of samples, pop size per class, number of draws
expected_samples_for_x_windows <- function(x, repet = 50000){
X <- rmvhyper(repet, sapply(sm.repr.ext, nrow), x)
X <- rowSums(X > 0)
return(quantile(x = X, probs = c(0.1,0.5,0.9)))
}
with_seed(
42,
expected_samples_per_windows <- sapply(min(cluster_stats$windows):max(cluster_stats$windows),
expected_samples_for_x_windows)
)
expected_samples_per_windows <- data.frame(t(expected_samples_per_windows))
expected_samples_per_windows$windows <- min(cluster_stats$windows):max(cluster_stats$windows)
expected_samples_per_windows$selected <- "Expectation"
lymph <- read_csv("data/LymphomaCODEX/single_cells.csv")
spots <- lymph %>%
filter(Groups %in% c(1, 2)) %>%
pull(Spots) %>%
unique()
outcome <- lymph %>%
filter(Groups %in% c(1, 2)) %>%
group_by(Spots, Patients) %>%
summarize(Groups = Groups[1], .groups = "drop")
freq.expr <- sm.repr %>%
left_join(outcome %>%
mutate(Spots = as.character(Spots)), by = c("id" = "Spots")) %>%
rename(target = Groups) %>%
mutate(target = as.factor(make.names(target))) %>%
select(-id, -Patients)
mds.expr <- freq.expr %>%
select(-target) %>%
dist %>%
cmdscale(eig=TRUE, k=2)
mds.expr <- data.frame(MDS1 = mds.expr$points[,1], MDS2 = mds.expr$points[,2], target = freq.expr$target)
gp <- (ggplot(mds.expr, aes(x = MDS1, y = MDS2, color = target)) +
geom_point(size = 2) +
theme_minimal() +
ggtitle("MDS for CTCL samples") +
xlab("MDS1") +
ylab("MDS2") +
scale_color_manual(values = c("#008837", "#7b3294")))
ggsave("CTCLct400_mds.pdf", gp, height = 5, width = 5)
reliance <- model_reliance(freq.expr)
ggsave("CTCLreliance_interpretation.pdf", last_plot())
cluster_stats$names <- str_remove(string = cluster_stats$names, pattern = "...")
cluster_stats <- cluster_stats %>%
left_join(reliance, by = join_by(names == Cluster))
# Label clusters only if they have a significant sMR
cluster_stats$label <- ifelse(abs(cluster_stats$sMR) > 1, cluster_stats$names, NA)
# and if they have an unusual number of samples compared to their number of windows
lowsamples <- (cluster_stats %>%
left_join(expected_samples_per_windows, by = "windows") %>%
mutate(lowsamples = X10. > samples))$lowsamples
cluster_stats$label <- ifelse(lowsamples, cluster_stats$label, NA)
# Plot and save
gp <- ggplot(cluster_stats, aes(y = samples, x = windows, color = sMR)) +
geom_point() +
geom_hline(yintercept = 5, lty = 2, color = "gray50") +
theme_classic() +
geom_line(data = expected_samples_per_windows,
aes(y = X50., x = windows), color = "gray50") +
geom_line(data = expected_samples_per_windows,
aes(y = X90., x = windows), color = "gray75", lty = 2) +
geom_line(data = expected_samples_per_windows,
aes(y = X10., x = windows), color = "gray75", lty = 2) +
geom_text_repel(aes(label = label)) +
ggtitle("Frequency of window clusters") +
xlab("Number of windows") +
ylab("Number of samples") +
scale_color_steps2(low = "#008837", mid = "white", high = "#7b3294", na.value="gray75")
ggsave("CTCLct400_cluster_frequency.pdf", gp, height = 5, width = 5)
# For all the selected clusters
# (Non-randomly spread across samples and useful for classification)
contig_scores <- cluster_stats$label %>%
na.omit %>%
sapply(function(x) na.omit(moran_score(sm.repr.ext, paste0("...", x), stride = 200, pval = TRUE)), simplify = FALSE)
contig_clusters <- lapply(names(contig_scores), function(name) {
rep(name, length(na.omit(unlist(contig_scores[[name]])))/3)
})
contiguity_df <- data.frame(clusters = rep(unlist(contig_clusters), 2))
unlist_scores <- na.omit(unlist(contig_scores))
contiguity_df$scores <- c(unlist_scores[3*1:(length(unlist_scores)/3) - 1], unlist_scores[3*1:(length(unlist_scores)/3) - 2])
contiguity_df$type <- rep(c("Expected", "Observed"), each = length(unlist(contig_clusters)))
contiguity_pval <- unlist_scores[3*1:(length(unlist_scores)/3)]
pval_df <- data.frame(pval = contiguity_pval, clusters = unlist(contig_clusters))
pval_df <- pval_df %>% group_by(clusters) %>%
mutate(cor_p = p.adjust(pval, "bonferroni")) %>%
summarize(mincorp = min(cor_p)) %>%
mutate(mincorp = p.adjust(mincorp, "BH"))
pval_df$text <- paste("p =", signif(pval_df$mincorp, 3))
# Define the color mapping and the plot scales
color_mapping <- c("Expected" = "#CCCCCC", "Observed" = "#8FA1CC")
xmin <- min(contiguity_df$scores)
xmax <- max(contiguity_df$scores) + 0.2
gp <- ggplot(contiguity_df, aes(x = clusters, y = scores)) +
geom_violin(aes(fill = type), draw_quantiles = c(0.25, 0.5, 0.75), scale = "width") +
scale_fill_manual(values = color_mapping) +
theme_classic() +
xlab("Clusters") + ylab("Moran's I") +
coord_flip() +
ylim(xmin,xmax) +
geom_text(aes(label = text), data = pval_df, size = 3, y = xmax - 0.1) +
guides(fill = guide_legend("I values", reverse = TRUE))
ggsave("CTCLct400_cluster_moran_signif.pdf", gp, height = 5, width = 5)
# IMC
misty.results <- read_rds("BCexpr200.rds")
bmeta <- read_csv("data/BCIMC/Basel_PatientMetadata.csv")
with_seed(
1,
cores <- bmeta %>%
filter(
diseasestatus == "tumor",
response %in% c("Sensitive", "Resistant"),
clinical_type == "HR+HER2-", Subtype == "PR+ER+"
) %>%
group_by(response) %>%
slice_sample(n = 15) %>%
pull(core)
)
resp <- bmeta %>%
filter(core %in% cores) %>%
select(core, response)
sm.repr <- sm_labels(misty.results, 0.3, 0.8)
sm.repr.ext <- sm_labels(misty.results, 0.3, 0.8, freq = FALSE)
freq.sm <- sm.repr %>%
left_join(resp, by = c("id" = "core")) %>%
rename(target = response) %>%
mutate(target = as.factor(make.names(target))) %>%
select(-id)
mds.sm <- freq.sm %>%
select(-target) %>%
dist %>%
cmdscale(eig=TRUE, k=2)
mds.sm <- data.frame(MDS1 = mds.sm$points[,1], MDS2 = mds.sm$points[,2], target = freq.sm$target)
gp <- (ggplot(mds.sm, aes(x = MDS1, y = MDS2, color = target)) +
geom_point(size = 2) +
theme_minimal() +
ggtitle("MDS for BC samples") +
xlab("MDS1") +
ylab("MDS2") +
scale_color_manual(values = c("#008837", "#7b3294")))
ggsave("BCexpr200_mds.pdf", gp, height = 5, width = 5)
freq_all_clusters <- sapply(sm.repr.ext,
function(x) x %>%
select(-id, -x, -y) %>%
colSums)
reliance <- model_reliance(freq.sm)
ggsave("BCreliance_interpretation.pdf", last_plot())
# Compile cluster stats
cluster_stats <- data.frame(samples = rowSums(freq_all_clusters > 0))
cluster_stats$names <- sm.repr.ext[[1]] %>%
select(-id, -x, -y) %>%
names
cluster_stats$windows <- rowSums(freq_all_clusters)
cluster_stats$selected <- cluster_stats$names %in% names(sm.repr)
# Number of samples, pop size per class, number of draws
expected_samples_for_x_windows <- function(x, repet = 50000){
X <- rmvhyper(repet, sapply(sm.repr.ext, nrow), x)
X <- rowSums(X > 0)
return(quantile(x = X, probs = c(0.1,0.5,0.9)))
}
with_seed(
42,
expected_samples_per_windows <- sapply(min(cluster_stats$windows):max(cluster_stats$windows),
expected_samples_for_x_windows)
)
expected_samples_per_windows <- data.frame(t(expected_samples_per_windows))
expected_samples_per_windows$windows <- min(cluster_stats$windows):max(cluster_stats$windows)
expected_samples_per_windows$selected <- "Expectation"
cluster_stats$names <- str_remove(string = cluster_stats$names, pattern = "...")
cluster_stats <- cluster_stats %>%
left_join(reliance, by = join_by(names == Cluster))
# Label clusters only if they have a significant sMR
cluster_stats$label <- ifelse(abs(cluster_stats$sMR) > 1, cluster_stats$names, NA)
# and if they have an unusual number of samples compared to their number of windows
lowsamples <- (cluster_stats %>%
left_join(expected_samples_per_windows, by = "windows") %>%
mutate(lowsamples = X10. > samples))$lowsamples
cluster_stats$label <- ifelse(lowsamples, cluster_stats$label, NA)
# Plot and save
gp <- ggplot(cluster_stats, aes(y = samples, x = windows, color = sMR)) +
geom_point() +
geom_hline(yintercept = 5, lty = 2, color = "gray50") +
theme_classic() +
geom_line(data = expected_samples_per_windows,
aes(y = X50., x = windows), color = "gray50") +
geom_line(data = expected_samples_per_windows,
aes(y = X90., x = windows), color = "gray75", lty = 2) +
geom_line(data = expected_samples_per_windows,
aes(y = X10., x = windows), color = "gray75", lty = 2) +
geom_text_repel(aes(label = label)) +
ggtitle("Frequency of window clusters") +
xlab("Number of windows") +
ylab("Number of samples") +
scale_color_steps2(low = "#008837", mid = "white", high = "#7b3294", na.value="gray75")
ggsave("BCexpr200_cluster_frequency.pdf", gp, height = 5, width = 5)
# For all the selected clusters
# (Non-randomly spread across samples and useful for classification)
contig_scores <- cluster_stats$label %>%
na.omit %>%
sapply(function(x) na.omit(moran_score(sm.repr.ext, paste0("...", x), stride = 150, pval = TRUE)), simplify = FALSE)
contig_clusters <- lapply(names(contig_scores), function(name) {
rep(name, length(na.omit(unlist(contig_scores[[name]])))/3)
})
contiguity_df <- data.frame(clusters = rep(unlist(contig_clusters), 2))
unlist_scores <- na.omit(unlist(contig_scores))
contiguity_df$scores <- c(unlist_scores[3*1:(length(unlist_scores)/3) - 1], unlist_scores[3*1:(length(unlist_scores)/3) - 2])
contiguity_df$type <- rep(c("Expected", "Observed"), each = length(unlist(contig_clusters)))
contiguity_pval <- unlist_scores[3*1:(length(unlist_scores)/3)]
pval_df <- data.frame(pval = contiguity_pval, clusters = unlist(contig_clusters))
pval_df <- pval_df %>% group_by(clusters) %>%
mutate(cor_p = p.adjust(pval, "bonferroni")) %>%
summarize(mincorp = min(cor_p)) %>%
mutate(mincorp = p.adjust(mincorp, "BH"))
pval_df$text <- paste("p =", signif(pval_df$mincorp, 3))
# Define the color mapping and the plot scales
color_mapping <- c("Expected" = "#CCCCCC", "Observed" = "#8FA1CC")
xmin <- min(contiguity_df$scores)
xmax <- max(contiguity_df$scores) + 0.2
gp <- ggplot(contiguity_df, aes(x = clusters, y = scores)) +
geom_violin(aes(fill = type), draw_quantiles = c(0.25, 0.5, 0.75), scale = "width") +
scale_fill_manual(values = color_mapping) +
theme_classic() +
xlab("Clusters") + ylab("Moran's I") +
coord_flip() +
ylim(xmin,xmax) +
geom_text(aes(label = text), data = pval_df, size = 3, y = xmax - 0.1) +
guides(fill = guide_legend("I values", reverse = TRUE))
ggsave("BCexpr200_cluster_moran_signif.pdf", gp, height = 5, width = 5)