Figure 3.
Carmen Navarro
2021-07-01
Last updated: 2021-07-09
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Knit directory: hesc-epigenomics/
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|---|---|---|---|---|
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Summary
This is the supplementary notebook for figure 3.
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)H3K27m3 groups heatmap panels
H3K27m3 group annotation
select_groups <- function(df, pval_col, fc_col, basemean_col, quantile,
p_cutoff = 0.05, fc_cutoff = 1, basemean_quantile = 0.1) {
# I don't want to discard the NAs as they will go to the unenriched group.
df[is.na(df[[pval_col]]), pval_col] <- 1
min_mean <- quantile(df[[basemean_col]], probs = basemean_quantile)
signif_up_tss <- df %>% filter(.data[[pval_col]] <= p_cutoff & .data[[fc_col]] > fc_cutoff & .data[[basemean_col]] > min_mean)
signif_down_tss <- df %>% filter(.data[[pval_col]] <= p_cutoff & .data[[fc_col]] < -fc_cutoff & .data[[basemean_col]] > min_mean)
not_signif <- df %>% filter(.data[[pval_col]] > p_cutoff)
# Top
mean_cutoff <- quantile(not_signif[[basemean_col]], quantile)
always_up <- not_signif %>% filter(.data[[basemean_col]] >= mean_cutoff)
rest <- not_signif %>% filter(.data[[basemean_col]] < mean_cutoff)
list(up = signif_up_tss,
down = signif_down_tss,
always_up = always_up,
not_enriched = rest)
}
select_groups_bivalent <- function(df, quantile = 0.8, p_cutoff = 0.05, fc_cutoff = 1, basemean_quantile = 0.1, min_k4 = 2) {
select_k4 <- function(df, min_k4) {
df %>% filter(.data[["H3K4m3_Pr_mean_cov"]] > min_k4 | .data[["H3K4m3_Ni_mean_cov"]] > min_k4)
}
groups <- select_groups(
df,
"H3K27m3_DS_Pr_vs_Ni_padj",
"H3K27m3_DS_Pr_vs_Ni_log2FoldChange",
"H3K27m3_DS_Pr_vs_Ni_baseMean",
quantile,
p_cutoff,
fc_cutoff,
basemean_quantile
)
lapply(groups, select_k4, min_k4 = min_k4)
}
k27_groups <- select_groups_bivalent(genes)
# Annotate our groups
genes$k27_bivalency_grp <- "None"
genes[genes$name %in% k27_groups$up$name, "k27_bivalency_grp"] <- "Pr_higher_than_Ni"
genes[genes$name %in% k27_groups$down$name, "k27_bivalency_grp"] <- "Ni_higher_than_Pr"
genes[genes$name %in% k27_groups$always_up$name, "k27_bivalency_grp"] <- "Always_up"
genes[genes$name %in% k27_groups$not_enriched$name, "k27_bivalency_grp"] <- "K4_only"
# Annotate court
genes_loci <- import("./data/bed/Kumar_2020/Kumar_2020_genes_hg38_UCSC.bed")
genes_tss <- promoters(genes_loci, upstream = 2500, downstream = 2500)
court_bivalent <- rtracklayer::import("./data/bed/Bivalent_Court2017.hg38.bed")
court_biv_genes <- subsetByOverlaps(genes_tss, court_bivalent)
genes$court_bivalent <- "No"
genes[genes$name %in% court_biv_genes$name, "court_bivalent"] <- "Yes"
write.table(genes, "./data/meta/Kumar_2020_master_gene_table_rnaseq_shrunk_annotated.tsv")
k27_groups_loci <- lapply(k27_groups, make_gr_from_table)
rtracklayer::export(k27_groups_loci$up, "./figures_data/Kumar_2020_H3K27m3_Pr_higher_than_Ni_tss.bed")
rtracklayer::export(k27_groups_loci$down, "./figures_data/Kumar_2020_H3K27m3_Ni_higher_than_Pr_tss.bed")
rtracklayer::export(k27_groups_loci$always_up, "./figures_data/Kumar_2020_H3K27m3_always_up_tss.bed")
rtracklayer::export(k27_groups_loci$not_enriched, "./figures_data/Kumar_2020_H3K27m3_not_enriched_tss.bed")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$up,
k27_groups_loci$down,
k27_groups_loci$always_up
),
c("H3K27m3_Ni", "H3K27m3_Pr", "H2Aub_Ni", "H2Aub_Pr"),
c("Primed >> Naive", "Naive >> Primed", "Always up"),
global_scale = TRUE,
proportional = TRUE,
mode = "center"
)
| Version | Author | Date |
|---|---|---|
| 67895c5 | C. Navarro | 2021-07-01 |
Plot the second part of the heatmap panel.
plot_bw_heatmap_panel(
c(bwfiles$k27[c(1, 3)], bwfiles$ub[c(1, 3)]),
list(k27_groups_loci$up, k27_groups_loci$not_enriched),
c("H3K27m3_Ni", "H3K27m3_Pr", "H2Aub_Ni", "H2Aub_Pr"),
c("Primed >> Naive", "Rest"),
global_scale = TRUE,
proportional = TRUE,
mode = "center",
zmin = 0,
zmax = 23
)
| Version | Author | Date |
|---|---|---|
| 67895c5 | C. Navarro | 2021-07-01 |
H3K4m3
Plot the H3K4m3 part, sorted by the same reference
plot_bw_heatmap_panel(
c(bwfiles$k27[1], bwfiles$k4[c(1, 3)]),
list(k27_groups_loci$up,
k27_groups_loci$down,
k27_groups_loci$always_up
),
c("H3K27m3_Ni", "H3K4m3_Ni", "H3K4m3_Pr"),
c("Primed >> Naive", "Naive >> Primed", "Always up"),
global_scale = TRUE,
proportional = TRUE,
mode = "center"
)
| Version | Author | Date |
|---|---|---|
| 7117e7a | C. Navarro | 2021-07-08 |
plot_bw_heatmap_panel(
c(bwfiles$k27[1], bwfiles$k4[c(1, 3)]),
list(k27_groups_loci$up, k27_groups_loci$not_enriched),
c("H3K27m3_Ni", "H3K27m3_Pr", "H2Aub_Ni", "H2Aub_Pr"),
c("Primed >> Naive", "Rest"),
global_scale = TRUE,
proportional = TRUE,
mode = "center",
zmin = 0,
zmax = 188
)
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$name %in% k27_groups_loci$down$name, ]
df_long <- df %>% pivot_longer(!name, names_to = "group", values_to = "fc")
df_long_k27 <- df_k27_up %>% pivot_longer(!name, names_to = "group", values_to = "fc")
df_long$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)
| Version | Author | Date |
|---|---|---|
| 67895c5 | C. Navarro | 2021-07-01 |
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$name %in% k27_groups_loci$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 <- "#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)
| Version | Author | Date |
|---|---|---|
| 67895c5 | C. Navarro | 2021-07-01 |
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.
Combined heatmaps
Naive markers
naive_markers <-
c("KLF17",
"DPPA5",
"DNMT3L",
"GATA6",
"TBX3",
"IL6ST",
"DPPA3",
"KLF5",
"KLF4",
"HORMAD1",
"KHDC3L",
"ALPP",
"ALPPL2",
"ZNF729",
"TRIM60"
)
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)) 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$name %in% naive_markers, cols_used]
values[, tpm_cols] <- log2(values[, tpm_cols] + 1)
write.table(values, "./figures_data/fig3_messmer_naive_heatmap_global_scale.tsv")Primed markers
primed_markers <-
c("CD24",
"ZIC2",
"SFRP2",
"OTX2",
"CYTL1",
"HMX2",
"THY1",
"DUSP6",
"PTPRZ1"
)
combined_heatmap(genes, primed_markers, rnaseq_limits = c(0, 11.5), k4m3_limits = c(0, 90), k27m3_limits = c(0, 8), ub_limits = c(0, 11))values <- genes[genes$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")
sessionInfo()R version 4.1.0 (2021-05-18)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.2 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] cowplot_1.1.1 ggrastr_0.2.3 ggpubr_0.4.0
[10] effsize_0.8.1 forcats_0.5.1 stringr_1.4.0
[13] dplyr_1.0.7 purrr_0.3.4 readr_1.4.0
[16] tidyr_1.1.3 tibble_3.1.2 ggplot2_3.3.5
[19] tidyverse_1.3.1 rtracklayer_1.52.0 GenomicRanges_1.44.0
[22] GenomeInfoDb_1.28.1 IRanges_2.26.0 S4Vectors_0.30.0
[25] 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_0.5.1.1 fansi_0.5.0
[13] magrittr_2.0.1 openxlsx_4.2.4
[15] globals_0.14.0 Biostrings_2.60.1
[17] modelr_0.1.8 matrixStats_0.59.0
[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_1.98-1.3 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_1.0.6
[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_3.99-0.6 farver_2.1.0
[49] sass_0.4.0 dbplyr_2.1.1
[51] utf8_1.2.1 tidyselect_1.1.1
[53] labeling_0.4.2 rlang_0.4.11
[55] reshape2_1.4.4 later_1.2.0
[57] munsell_0.5.0 cellranger_1.1.0
[59] tools_4.1.0 cli_3.0.0
[61] generics_0.1.0 broom_0.7.8
[63] evaluate_0.14 yaml_2.2.1
[65] knitr_1.33 fs_1.5.0
[67] zip_2.2.0 dendextend_1.15.1
[69] future_1.21.0 whisker_0.4
[71] xml2_1.3.2 compiler_4.1.0
[73] rstudioapi_0.13 beeswarm_0.4.0
[75] curl_4.3.2 ggsignif_0.6.2
[77] reprex_2.0.0 bslib_0.2.5.1
[79] stringi_1.6.2 highr_0.9
[81] lattice_0.20-44 Matrix_1.3-4
[83] vctrs_0.3.8 pillar_1.6.1
[85] lifecycle_1.0.0 furrr_0.2.3
[87] jquerylib_0.1.4 data.table_1.14.0
[89] bitops_1.0-7 seriation_1.3.0
[91] httpuv_1.6.1 R6_2.5.0
[93] BiocIO_1.2.0 promises_1.2.0.1
[95] TSP_1.1-10 gridExtra_2.3
[97] rio_0.5.27 vipor_0.4.5
[99] parallelly_1.26.1 codetools_0.2-18
[101] assertthat_0.2.1 SummarizedExperiment_1.22.0
[103] openssl_1.4.4 rprojroot_2.0.2
[105] rjson_0.2.20 withr_2.4.2
[107] GenomicAlignments_1.28.0 Rsamtools_2.8.0
[109] GenomeInfoDbData_1.2.6 hms_1.1.0
[111] grid_4.1.0 rmarkdown_2.9
[113] MatrixGenerics_1.4.0 carData_3.0-4
[115] Cairo_1.5-12.2 git2r_0.28.0
[117] Biobase_2.52.0 lubridate_1.7.10
[119] ggbeeswarm_0.6.0 restfulr_0.0.13