Figure 3. H3K27m3 Naïve and Primed-specific bivalency groups
Last updated: 2021-09-10
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Summary
This is the supplementary notebook for figure 3.
H3K27m3 groups
H3K27m3 group selection
k27_groups <- split(genes, genes$k27_bivalency_grp)
k27_groups_loci <- lapply(k27_groups, make_gr_from_table)H3K27m3 alluvial plot
gr_annot <-
genes %>% dplyr::select(name, seqnames, start, end, strand,
H3K4m3_DS_Pr_vs_Ni_log2FoldChange,
H3K27m3_DS_Pr_vs_Ni_log2FoldChange,
H2Aub_DS_Pr_vs_Ni_log2FoldChange,
H3K4m3_DS_Pr_vs_Ni_padj,
H3K27m3_DS_Pr_vs_Ni_padj,
H2Aub_DS_Pr_vs_Ni_padj
)
gr_annot <- gr_annot %>% mutate(
k4 = factor(case_when(
H3K4m3_DS_Pr_vs_Ni_padj < pval_cutoff & H3K4m3_DS_Pr_vs_Ni_log2FoldChange > fc_cutoff ~ "Pr >> Ni",
H3K4m3_DS_Pr_vs_Ni_padj < pval_cutoff & H3K4m3_DS_Pr_vs_Ni_log2FoldChange < -fc_cutoff ~ "Ni >> Pr",
TRUE ~ "unchanged")
),
k27 = factor(case_when(
H3K27m3_DS_Pr_vs_Ni_padj < pval_cutoff & H3K27m3_DS_Pr_vs_Ni_log2FoldChange > fc_cutoff ~ "Pr >> Ni",
H3K27m3_DS_Pr_vs_Ni_padj < pval_cutoff & H3K27m3_DS_Pr_vs_Ni_log2FoldChange < -fc_cutoff ~ "Ni >> Pr",
TRUE ~ "unchanged")
),
ub = factor(case_when(
H2Aub_DS_Pr_vs_Ni_padj < pval_cutoff & H2Aub_DS_Pr_vs_Ni_log2FoldChange > fc_cutoff ~ "Pr >> Ni",
H2Aub_DS_Pr_vs_Ni_padj < pval_cutoff & H2Aub_DS_Pr_vs_Ni_log2FoldChange < -fc_cutoff ~ "Ni >> Pr",
TRUE ~ "unchanged")
))
counts_table <- gr_annot %>% dplyr::count(k4, k27, ub)
ggplot(counts_table, aes(y = n, axis1 = k4, axis2 = k27, axis3 = ub, fill = k27)) +
geom_alluvium(width = 1 / 12) +
geom_stratum(width = 1 / 12, color = "black") +
geom_text(stat = "stratum", aes(label = after_stat(stratum)), nudge_x = -0.08, angle = 90, size = 3) +
scale_fill_manual(values = c("#278b8b", "#f44b34", "#cccccc")) +
theme_default() +
scale_x_discrete(limits = c("H3K4m3", "H3K27m3", "H2AUb"), expand = c(.05, .05)) 
H3K27m3 group heatmap panels
H3K27m3 and H2AUb
plot_bw_heatmap_panel(
c(bwfiles$k27[c(1, 3)], bwfiles$ub[c(1, 3)]),
list(k27_groups_loci$Pr_higher_than_Ni,
k27_groups_loci$Ni_higher_than_Pr,
k27_groups_loci$Always_up
),
c("H3K27m3_Ni", "H3K27m3_Pr", "H2Aub_Ni", "H2Aub_Pr"),
c("Primed >> Naive", "Naive >> Primed", "Always up"),
global_scale = TRUE,
proportional = TRUE,
mode = "center"
)
Plot the second part of the heatmap panel.
plot_bw_heatmap_panel(
c(bwfiles$k27[c(1, 3)], bwfiles$ub[c(1, 3)]),
list(k27_groups_loci$Pr_higher_than_Ni, k27_groups_loci$K4_only),
c("H3K27m3_Ni", "H3K27m3_Pr", "H2Aub_Ni", "H2Aub_Pr"),
c("Primed >> Naive", "Rest"),
global_scale = TRUE,
proportional = TRUE,
mode = "center",
zmin = 0,
zmax = 23
)
H3K4m3
Plot the H3K4m3 part, sorted by the same reference
plot_bw_heatmap_panel(
c(bwfiles$k27[1], bwfiles$k4[c(1, 3)]),
list(k27_groups_loci$Pr_higher_than_Ni,
k27_groups_loci$Ni_higher_than_Pr,
k27_groups_loci$Always_up
),
c("H3K27m3_Ni", "H3K4m3_Ni", "H3K4m3_Pr"),
c("Primed >> Naive", "Naive >> Primed", "Always up"),
global_scale = TRUE,
proportional = TRUE,
mode = "center",
zmin = 0,
zmax = 188
)
plot_bw_heatmap_panel(
c(bwfiles$k27[1], bwfiles$k4[c(1, 3)]),
list(k27_groups_loci$Pr_higher_than_Ni, k27_groups_loci$K4_only),
c("H3K27m3_Ni", "H3K4m3_Ni", "H2Aub_Pr"),
c("Primed >> Naive", "Rest"),
global_scale = TRUE,
proportional = TRUE,
mode = "center"
)
RNA-seq ratios at H3K27m3 groups
H327m3 Naive >> Primed
df <-
genes[, c(
"name",
"RNASeq_DS_Pr_vs_Ni_log2FoldChange",
"RNASeq_DS_EZH2i_vs_Ni_log2FoldChange",
"RNASeq_DS_EZH2i_vs_Pr_log2FoldChange"
)]
colnames(df) <- c("name", "Primed vs Naive", "EZH2i vs Naive", "EZH2i vs Primed")
df_k27_up <- df[df$name %in% k27_groups_loci$Ni_higher_than_Pr$name, ]
df_long <- df %>% pivot_longer(!name, names_to = "group", values_to = "fc")
df_long_k27 <- df_k27_up %>% pivot_longer(!name, names_to = "group", values_to = "fc")
df_long$group <- factor(df_long$group, levels = c("Primed vs Naive", "EZH2i vs Naive", "EZH2i vs Primed"))
df_long_k27$group <- factor(df_long_k27$group, levels = c("Primed vs Naive", "EZH2i vs Naive", "EZH2i vs Primed"))
# Stats subset vs global
ni_pr_test <- wilcox.test(df_k27_up[, "Primed vs Naive"],
df[!df$name %in% df_k27_up$name , "Primed vs Naive"], na.rm = T)
ni_pr_effect <- cohen.d(df_k27_up[, "Primed vs Naive"],
df[!df$name %in% df_k27_up$name , "Primed vs Naive"], na.rm = T)
col <- "EZH2i vs Naive"
ni_ezh2i_test <- wilcox.test(df_k27_up[, col],
df[!df$name %in% df_k27_up$name , col], na.rm = T)
ni_ezh2i_effect <- cohen.d(df_k27_up[, col],
df[!df$name %in% df_k27_up$name , col], na.rm = T)
col <- "EZH2i vs Primed"
pr_ezh2i_test <- wilcox.test(df_k27_up[, col],
df[!df$name %in% df_k27_up$name , col], na.rm = T)
pr_ezh2i_effect <- cohen.d(df_k27_up[, col],
df[!df$name %in% df_k27_up$name , col], na.rm = T)
stats_caption <- paste("Wilcoxon global vs. selection",
paste("Ni_Pr:", format(ni_pr_test$p.value, digits=8),
"Cohen D:", round(ni_pr_effect$estimate, digits = 4),
"(", ni_pr_effect$magnitude, ")"),
paste("Ni_EZH2i:", format(ni_ezh2i_test$p.value, digits=8),
"Cohen D:", round(ni_ezh2i_effect$estimate, digits = 4),
"(", ni_ezh2i_effect$magnitude, ")"),
paste("Pr_EZH2i:", format(pr_ezh2i_test$p.value, digits=8),
"Cohen D:", round(pr_ezh2i_effect$estimate, digits = 4),
"(", pr_ezh2i_effect$magnitude, ")"),
sep = "\n")
my_comparisons <- list(c("EZH2i vs Naive", "EZH2i vs Primed"),
c("Primed vs Naive", "EZH2i vs Naive"),
c("Primed vs Naive", "EZH2i vs Primed"))
k27_up_color <- "#009784"
ggplot(data=df_long, aes(x = group, y = fc)) +
geom_violin(size = 0.8) +
stat_compare_means(data=df_long_k27,
comparisons = my_comparisons, method = "wilcox.test", paired = TRUE) +
rasterize(
geom_jitter(data=df_long_k27, color = k27_up_color, alpha = 0.7, size = 0.1),
dpi = 300) +
geom_hline (yintercept = 0, linetype = "dashed") +
coord_cartesian(ylim = c(-13, 24)) +
theme_default(base_size = 14) +
labs(x = "Condition",
y = "Log2 FC",
title = "RNASeq expression changes",
subtitle = "Global vs H3K27m3 Naïve >> Primed TSS",
caption = stats_caption)
Download values: Distribution, K27 higher naive points,
H327m3 Primed >> Naive
df <-
genes[, c(
"name",
"RNASeq_DS_Pr_vs_Ni_log2FoldChange",
"RNASeq_DS_EZH2i_vs_Ni_log2FoldChange",
"RNASeq_DS_EZH2i_vs_Pr_log2FoldChange"
)]
colnames(df) <- c("name", "Primed vs Naive", "EZH2i vs Naive", "EZH2i vs Primed")
df_k27_up <- df[df$name %in% k27_groups_loci$Pr_higher_than_Ni$name, ]
df_k27_up$`Primed vs Naive` <- -df_k27_up$`Primed vs Naive`
df$`Primed vs Naive` <- -df$`Primed vs Naive`
df_long <- df %>% pivot_longer(!name, names_to = "group", values_to = "fc")
df_long_k27 <- df_k27_up %>% pivot_longer(!name, names_to = "group", values_to = "fc")
df_long$group <- factor(df_long$group, levels = c("Primed vs Naive", "EZH2i vs Naive", "EZH2i vs Primed"))
df_long_k27$group <- factor(df_long_k27$group, levels = c("Primed vs Naive", "EZH2i vs Naive", "EZH2i vs Primed"))
levels(df_long$group) <- c("Naive vs Primed", "EZH2i vs Naive", "EZH2i vs Primed")
levels(df_long_k27$group) <- c("Naive vs Primed", "EZH2i vs Naive", "EZH2i vs Primed")
my_comparisons <- list(c("EZH2i vs Naive", "EZH2i vs Primed"),
c("Naive vs Primed", "EZH2i vs Naive"),
c("Naive vs Primed", "EZH2i vs Primed"))
# Stats subset vs global
ni_pr_test <- wilcox.test(df_k27_up[, "Primed vs Naive"],
df[!df$name %in% df_k27_up$name , "Primed vs Naive"], na.rm = T)
ni_pr_effect <- cohen.d(df_k27_up[, "Primed vs Naive"],
df[!df$name %in% df_k27_up$name , "Primed vs Naive"], na.rm = T)
col <- "EZH2i vs Naive"
ni_ezh2i_test <- wilcox.test(df_k27_up[, col],
df[!df$name %in% df_k27_up$name , col], na.rm = T)
ni_ezh2i_effect <- cohen.d(df_k27_up[, col],
df[!df$name %in% df_k27_up$name , col], na.rm = T)
col <- "EZH2i vs Primed"
pr_ezh2i_test <- wilcox.test(df_k27_up[, col],
df[!df$name %in% df_k27_up$name , col], na.rm = T)
pr_ezh2i_effect <- cohen.d(df_k27_up[, col],
df[!df$name %in% df_k27_up$name , col], na.rm = T)
stats_caption <- paste("Wilcoxon global vs. selection",
paste("Ni_Pr:", format(ni_pr_test$p.value, digits=8),
"Cohen D:", round(ni_pr_effect$estimate, digits = 4),
"(", ni_pr_effect$magnitude, ")"),
paste("Ni_EZH2i:", format(ni_ezh2i_test$p.value, digits=8),
"Cohen D:", round(ni_ezh2i_effect$estimate, digits = 4),
"(", ni_ezh2i_effect$magnitude, ")"),
paste("Pr_EZH2i:", format(pr_ezh2i_test$p.value, digits=8),
"Cohen D:", round(pr_ezh2i_effect$estimate, digits = 4),
"(", pr_ezh2i_effect$magnitude, ")"),
sep = "\n")
k27_up_color <- "#ff4b40"
ggplot(data=df_long, aes(x = group, y = fc)) +
geom_violin(size = 0.8) +
stat_compare_means(
data=df_long_k27, comparisons = my_comparisons,
method = "wilcox.test", paired = TRUE) +
rasterize(
geom_jitter(data=df_long_k27, color = k27_up_color, alpha = 0.7, size = 0.1),
dpi = 300) +
geom_hline (yintercept = 0, linetype = "dashed") +
coord_cartesian(ylim = c(-13, 24)) +
theme_default(base_size = 14) +
labs(x = "Condition",
y = "Log2 FC",
title = "RNASeq expression changes",
subtitle = "Global vs H3K27m3 Primed >> Naïve TSS",
caption = stats_caption)
write.table(df_long[!is.na(df_long$fc), ],
file = "./figures_data/fig3_violin_k27_pr_higher_violin.tsv",
col.names = T, sep = "\t", quote = F, row.names = F)
write.table(df_long_k27[!is.na(df_long_k27$fc), ],
file = "./figures_data/fig3_violin_k27_pr_higher_jitter_k27_values.tsv",
col.names = T, sep = "\t", quote = F, row.names = F)Download values: K27 primed higher points.
H327m3 always high
df <-
genes[, c(
"name",
"RNASeq_DS_Pr_vs_Ni_log2FoldChange",
"RNASeq_DS_EZH2i_vs_Ni_log2FoldChange",
"RNASeq_DS_EZH2i_vs_Pr_log2FoldChange"
)]
colnames(df) <- c("name", "Primed vs Naive", "EZH2i vs Naive", "EZH2i vs Primed")
df_k27_up <- df[df$name %in% k27_groups_loci$Always_up$name, ]
df_k27_up$`Primed vs Naive` <- -df_k27_up$`Primed vs Naive`
df$`Primed vs Naive` <- -df$`Primed vs Naive`
df_long <- df %>% pivot_longer(!name, names_to = "group", values_to = "fc")
df_long_k27 <- df_k27_up %>% pivot_longer(!name, names_to = "group", values_to = "fc")
df_long$group <- factor(df_long$group, levels = c("Primed vs Naive", "EZH2i vs Naive", "EZH2i vs Primed"))
df_long_k27$group <- factor(df_long_k27$group, levels = c("Primed vs Naive", "EZH2i vs Naive", "EZH2i vs Primed"))
levels(df_long$group) <- c("Naive vs Primed", "EZH2i vs Naive", "EZH2i vs Primed")
levels(df_long_k27$group) <- c("Naive vs Primed", "EZH2i vs Naive", "EZH2i vs Primed")
my_comparisons <- list(c("EZH2i vs Naive", "EZH2i vs Primed"),
c("Naive vs Primed", "EZH2i vs Naive"),
c("Naive vs Primed", "EZH2i vs Primed"))
# Stats subset vs global
ni_pr_test <- wilcox.test(df_k27_up[, "Primed vs Naive"],
df[!df$name %in% df_k27_up$name , "Primed vs Naive"], na.rm = T)
ni_pr_effect <- cohen.d(df_k27_up[, "Primed vs Naive"],
df[!df$name %in% df_k27_up$name , "Primed vs Naive"], na.rm = T)
col <- "EZH2i vs Naive"
ni_ezh2i_test <- wilcox.test(df_k27_up[, col],
df[!df$name %in% df_k27_up$name , col], na.rm = T)
ni_ezh2i_effect <- cohen.d(df_k27_up[, col],
df[!df$name %in% df_k27_up$name , col], na.rm = T)
col <- "EZH2i vs Primed"
pr_ezh2i_test <- wilcox.test(df_k27_up[, col],
df[!df$name %in% df_k27_up$name , col], na.rm = T)
pr_ezh2i_effect <- cohen.d(df_k27_up[, col],
df[!df$name %in% df_k27_up$name , col], na.rm = T)
stats_caption <- paste("Wilcoxon global vs. selection",
paste("Ni_Pr:", format(ni_pr_test$p.value, digits=8),
"Cohen D:", round(ni_pr_effect$estimate, digits = 4),
"(", ni_pr_effect$magnitude, ")"),
paste("Ni_EZH2i:", format(ni_ezh2i_test$p.value, digits=8),
"Cohen D:", round(ni_ezh2i_effect$estimate, digits = 4),
"(", ni_ezh2i_effect$magnitude, ")"),
paste("Pr_EZH2i:", format(pr_ezh2i_test$p.value, digits=8),
"Cohen D:", round(pr_ezh2i_effect$estimate, digits = 4),
"(", pr_ezh2i_effect$magnitude, ")"),
sep = "\n")
k27_up_color <- "#4463b9"
ggplot(data=df_long, aes(x = group, y = fc)) +
geom_violin(size = 0.8) +
stat_compare_means(
data=df_long_k27, comparisons = my_comparisons,
method = "wilcox.test", paired = TRUE) +
rasterize(
geom_jitter(data=df_long_k27, color = k27_up_color, alpha = 0.7, size = 0.1),
dpi = 300) +
geom_hline (yintercept = 0, linetype = "dashed") +
coord_cartesian(ylim = c(-13, 24)) +
theme_default(base_size = 14) +
labs(x = "Condition",
y = "Log2 FC",
title = "RNASeq expression changes",
subtitle = "Global vs H3K27m3 H3K27m3 always up TSS",
caption = stats_caption)
| Version | Author | Date |
|---|---|---|
| 4018a69 | C. Navarro | 2021-07-15 |
write.table(df_long[!is.na(df_long$fc), ],
file = "./figures_data/fig3_violin_k27_always_up_violin.tsv",
col.names = T, sep = "\t", quote = F, row.names = F)
write.table(df_long_k27[!is.na(df_long_k27$fc), ],
file = "./figures_data/fig3_violin_k27_always_up_jitter_k27_values.tsv",
col.names = T, sep = "\t", quote = F, row.names = F)Download values: K27 always up points.
Combined heatmaps
Naive markers
naive_markers <-
c("KLF17",
"DPPA5",
"DNMT3L",
"GATA6",
"TBX3",
"IL6ST",
"DPPA3",
"KLF5",
"KLF4",
"HORMAD1",
"KHDC3L",
"ALPP",
"ALPPL2",
"ZNF729",
"TRIM60"
)
tpm_cols <- grep("RNASeq_TPM", colnames(genes), value = T)
cols_used <- c("name", "H3K27m3_Ni_mean_cov", "H3K27m3_Pr_mean_cov", "H3K27m3_Ni_EZH2i_mean_cov", "H3K27m3_Pr_EZH2i_mean_cov",
"H3K4m3_Ni_mean_cov", "H3K4m3_Pr_mean_cov", "H3K4m3_Ni_EZH2i_mean_cov", "H3K4m3_Pr_EZH2i_mean_cov",
"H2Aub_Ni_mean_cov", "H2Aub_Pr_mean_cov", "H2Aub_Ni_EZH2i_mean_cov", "H2Aub_Pr_EZH2i_mean_cov",
tpm_cols)
values <- genes[genes$name %in% naive_markers, cols_used]
values[, tpm_cols] <- log2(values[, tpm_cols] + 1)
write.table(values, "./figures_data/fig3_messmer_naive_heatmap_global_scale.tsv")
fig <- combined_heatmap(
genes,
naive_markers,
rnaseq_limits = c(0, 11.5),
k4m3_limits = c(0, 90),
k27m3_limits = c(0, 8),
ub_limits = c(0, 11)
)
fig orca(fig, "./output/fig3_messmer_naive_heatmap_global_scale.svg", width = 9, height = 9, more_args = c('--disable-gpu'))Primed markers
primed_markers <-
c("CD24",
"ZIC2",
"SFRP2",
"OTX2",
"CYTL1",
"HMX2",
"THY1",
"DUSP6",
"PTPRZ1"
)
values <- genes[genes$name %in% primed_markers, cols_used]
values[, tpm_cols] <- log2(values[, tpm_cols] + 1)
write.table(values, "./figures_data/fig3_messmer_primed_heatmap_global_scale.tsv")
fig <- combined_heatmap(
genes,
primed_markers,
rnaseq_limits = c(0, 11.5),
k4m3_limits = c(0, 90),
k27m3_limits = c(0, 8),
ub_limits = c(0, 11)
)
figorca(fig, "./output/fig3_messmer_primed_heatmap_global_scale.svg", width = 9, height = 9, more_args = c('--disable-gpu'))Other figures
H3K27m3 promoter volcano plot
genes_tss <- makeGRangesFromDataFrame(genes, keep.extra.columns = T)
court_peaks <- import("./data/bed/Bivalent_Court2017.hg38.bed")
court_tss <- subsetByOverlaps(genes_tss, court_peaks)
court_tss_df <- data.frame(court_tss)
ggplot(genes, aes(x=H3K27m3_DS_Pr_vs_Ni_log2FoldChange, y=-log10(H3K27m3_DS_Pr_vs_Ni_padj))) +
rasterise(geom_point(size = 1, alpha = 0.7, color = "gray"), dpi = 300) +
theme_default(base_size = 12) +
rasterise(geom_point(data = court_tss_df, size = 1, alpha = 0.8, color = "black"), dpi = 300) +
labs(x = "Log2 FC (primed/naive)",
y = "Log10 pval", title = "Promoter H3K27m3 - Court 2017 bivalent in black") +
geom_hline(yintercept = -log10(0.05), linetype = "dashed") +
geom_vline(xintercept = 0) +
coord_cartesian(xlim = c(-6,6), ylim = c(0, 20))
df <- genes[, c("H3K27m3_DS_Pr_vs_Ni_log2FoldChange", "H3K27m3_DS_Pr_vs_Ni_padj")]
df[, "H3K27m3_DS_Pr_vs_Ni_padj"] <- -log10(df[, "H3K27m3_DS_Pr_vs_Ni_padj"])
write.table(df, "./figures_data/fig3_h3k27m3_promoter_volcano_all.tsv", sep = "\t", row.names = F)
df <- court_tss_df[, c("H3K27m3_DS_Pr_vs_Ni_log2FoldChange", "H3K27m3_DS_Pr_vs_Ni_padj")]
df[, "H3K27m3_DS_Pr_vs_Ni_padj"] <- -log10(df[, "H3K27m3_DS_Pr_vs_Ni_padj"])
write.table(df, "./figures_data/fig3_h3k27m3_promoter_volcano_court.tsv", sep = "\t", row.names = F)Piled up profiles
plot_func <-
purrr::partial(
plot_bw_profile,
loci = k27_groups_loci[c("Pr_higher_than_Ni", "Ni_higher_than_Pr", "Always_up", "K4_only")],
labels = c("Pr >> Ni", "Ni >> Pr", "Always up", "Not enriched"),
colors = c("#092ba8", "#5f9ea0", "#cccccc", "#f47770"),
mode = "center",
verbose = F
)
plot_func(bwfiles$k4[1]) +
labs(title = "H3K4m3 Naive", subtitle = "Loci: H3K27m3 groups")
plot_func(bwfiles$k4[2]) +
labs(title = "H3K4m3 Naive + EZH2i", subtitle = "Loci: H3K27m3 groups")
plot_func(bwfiles$k4[3]) +
labs(title = "H3K4m3 Primed", subtitle = "Loci: H3K27m3 groups")
plot_func(bwfiles$k4[4]) +
labs(title = "H3K4m3 Primed + EZH2i", subtitle = "Loci: H3K27m3 groups")
plot_func(bwfiles$k27[1]) +
labs(title = "H3K27m3 Naive", subtitle = "Loci: H3K27m3 groups") +
coord_cartesian(ylim = c(0, 6))
plot_func(bwfiles$k27[2]) +
labs(title = "H3K27m3 Naive + EZH2i", subtitle = "Loci: H3K27m3 groups") +
coord_cartesian(ylim = c(0, 6))
plot_func(bwfiles$k27[3]) +
labs(title = "H3K27m3 Primed", subtitle = "Loci: H3K27m3 groups") +
coord_cartesian(ylim = c(0, 6))
plot_func(bwfiles$k27[4]) +
labs(title = "H3K27m3 Primed + EZH2i", subtitle = "Loci: H3K27m3 groups") +
coord_cartesian(ylim = c(0, 6))
plot_func(bwfiles$ub[1]) +
labs(title = "H2Aub Naive", subtitle = "Loci: H3K27m3 groups")
plot_func(bwfiles$ub[2]) +
labs(title = "H2Aub Naive + EZH2i", subtitle = "Loci: H3K27m3 groups")
plot_func(bwfiles$ub[3]) +
labs(title = "H2Aub Primed", subtitle = "Loci: H3K27m3 groups")
plot_func(bwfiles$ub[4]) +
labs(title = "H2Aub Primed + EZH2i", subtitle = "Loci: H3K27m3 groups")
sessionInfo()R version 4.1.1 (2021-08-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.3 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/liblapack.so.3
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=sv_SE.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=sv_SE.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=sv_SE.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=sv_SE.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] parallel stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] svglite_2.0.0 heatmaply_1.2.1 viridis_0.6.1
[4] viridisLite_0.4.0 plotly_4.9.4.1 wigglescout_0.13.1
[7] ggalluvial_0.12.3 cowplot_1.1.1 ggrastr_0.2.3
[10] ggpubr_0.4.0 effsize_0.8.1 forcats_0.5.1
[13] stringr_1.4.0 dplyr_1.0.7 purrr_0.3.4
[16] readr_1.4.0 tidyr_1.1.3 tibble_3.1.4
[19] ggplot2_3.3.5 tidyverse_1.3.1 rtracklayer_1.52.0
[22] GenomicRanges_1.44.0 GenomeInfoDb_1.28.1 IRanges_2.26.0
[25] S4Vectors_0.30.0 BiocGenerics_0.38.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] readxl_1.3.1 backports_1.2.1
[3] systemfonts_1.0.2 plyr_1.8.6
[5] lazyeval_0.2.2 crosstalk_1.1.1
[7] BiocParallel_1.26.0 listenv_0.8.0
[9] digest_0.6.27 foreach_1.5.1
[11] htmltools_0.5.2 fansi_0.5.0
[13] magrittr_2.0.1 openxlsx_4.2.4
[15] globals_0.14.0 Biostrings_2.60.2
[17] modelr_0.1.8 matrixStats_0.60.1
[19] askpass_1.1 colorspace_2.0-2
[21] rvest_1.0.0 haven_2.4.1
[23] xfun_0.24 crayon_1.4.1
[25] RCurl_1.98-1.4 jsonlite_1.7.2
[27] iterators_1.0.13 glue_1.4.2
[29] registry_0.5-1 gtable_0.3.0
[31] zlibbioc_1.38.0 XVector_0.32.0
[33] webshot_0.5.2 DelayedArray_0.18.0
[35] car_3.0-11 abind_1.4-5
[37] scales_1.1.1 DBI_1.1.1
[39] rstatix_0.7.0 Rcpp_1.0.7
[41] foreign_0.8-81 htmlwidgets_1.5.3
[43] httr_1.4.2 RColorBrewer_1.1-2
[45] ellipsis_0.3.2 pkgconfig_2.0.3
[47] XML_3.99-0.7 farver_2.1.0
[49] sass_0.4.0 dbplyr_2.1.1
[51] utf8_1.2.2 tidyselect_1.1.1
[53] labeling_0.4.2 rlang_0.4.11
[55] reshape2_1.4.4 later_1.3.0
[57] munsell_0.5.0 cellranger_1.1.0
[59] tools_4.1.1 cli_3.0.1
[61] generics_0.1.0 broom_0.7.8
[63] evaluate_0.14 fastmap_1.1.0
[65] yaml_2.2.1 processx_3.5.2
[67] knitr_1.33 fs_1.5.0
[69] zip_2.2.0 dendextend_1.15.1
[71] future_1.21.0 whisker_0.4
[73] xml2_1.3.2 compiler_4.1.1
[75] rstudioapi_0.13 beeswarm_0.4.0
[77] curl_4.3.2 ggsignif_0.6.2
[79] reprex_2.0.0 bslib_0.2.5.1
[81] stringi_1.7.4 ps_1.6.0
[83] highr_0.9 lattice_0.20-44
[85] Matrix_1.3-4 vctrs_0.3.8
[87] pillar_1.6.2 lifecycle_1.0.0
[89] furrr_0.2.3 jquerylib_0.1.4
[91] data.table_1.14.0 bitops_1.0-7
[93] seriation_1.3.0 httpuv_1.6.2
[95] R6_2.5.1 BiocIO_1.2.0
[97] promises_1.2.0.1 TSP_1.1-10
[99] gridExtra_2.3 rio_0.5.27
[101] vipor_0.4.5 parallelly_1.26.1
[103] codetools_0.2-18 assertthat_0.2.1
[105] SummarizedExperiment_1.22.0 openssl_1.4.4
[107] rprojroot_2.0.2 rjson_0.2.20
[109] withr_2.4.2 GenomicAlignments_1.28.0
[111] Rsamtools_2.8.0 GenomeInfoDbData_1.2.6
[113] hms_1.1.0 grid_4.1.1
[115] rmarkdown_2.9 MatrixGenerics_1.4.0
[117] carData_3.0-4 Cairo_1.5-12.2
[119] git2r_0.28.0 Biobase_2.52.0
[121] lubridate_1.7.10 ggbeeswarm_0.6.0
[123] restfulr_0.0.13