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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))

Version Author Date
7117e7a C. Navarro 2021-07-08

H3K27m3 groups heatmap panels

genes_loci <- import("./data/bed/Kumar_2020/Kumar_2020_genes_hg38_UCSC.bed")
genes_tss <- promoters(genes_loci, upstream = 2500, downstream = 2500)

filter_by_k27_group <- function(gr, df, group) {
  gr[gr$name %in% df[df$k27_bivalency_grp == group, "name"], ]
}

groups <- levels(genes$k27_bivalency_grp)
genes_k27_loci_groups <- lapply(groups, filter_by_k27_group, gr=genes_tss, df = genes)
names(genes_k27_loci_groups) <- groups

ns_gene_names <- genes %>% filter(k27_bivalency_grp == "None" & (H3K4m3_Ni_mean_cov > 2 | H3K4m3_Pr_mean_cov > 2)) %>% dplyr::select(name)
genes_k27_ns_w_k4 <- genes_tss[genes_tss$name %in% ns_gene_names$name, ]

plot_bw_heatmap_panel(
    c(bwfiles$k27[c(1, 3)], bwfiles$ub[c(1, 3)]),
    list(genes_k27_loci_groups$Pr_higher_than_Ni,
         genes_k27_loci_groups$Ni_higher_than_Pr,
         genes_k27_loci_groups$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(genes_k27_loci_groups$Ni_higher_than_Pr, genes_k27_ns_w_k4),
  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

Plot the H3K4m3 part, sorted by the same reference

plot_bw_heatmap_panel(
    c(bwfiles$k27[1], bwfiles$k4[c(1, 3)]),
    list(genes_k27_loci_groups$Pr_higher_than_Ni,
         genes_k27_loci_groups$Ni_higher_than_Pr,
         genes_k27_loci_groups$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

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$name %in% genes_k27_loci_groups$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)

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$name %in% genes_k27_loci_groups$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)

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) 

Primed markers

primed_markers <-
  c("CD24",
    "ZIC2",
    "SFRP2",
    "OTX2",
    "CYTL1",
    "HMX2",
    "THY1",
    "DUSP6",
    "PTPRZ1"
  )
combined_heatmap(genes, primed_markers) 

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