Last updated: 2022-02-05

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Rmd	00ded0f	C. Navarro	2022-02-04	fig 4

Summary

This is the supplementary notebook for figure 4.

Expression of trophectoderm and placental-specific genes

genes_list <- c("EPAS1", "MSX2", "GATA3", "NR2F2", "CLDN4", "GATA2", "IGF2",
    "CDX2", "SLC40A1", "KRT7", "FRZB", "CGA", "ERP27", "KRT23", "CGB5", "VGLL1",
    "ENPEP", "TP63" )

fig <- combined_heatmap(genes, genes_list, cluster_rows = F,
  rnaseq_limits = c(0, 12.5),
  k4m3_limits = c(0, 80),
  k27m3_limits = c(0, 12),
  ub_limits = c(0, 12))

fig

Image analysis

2 Inhibitors comparison

Channels: - c1: NANOG - c2: K27m3 - c3: GATA3 - c4: DAPI

Metadata_Well:

A05: Wildtype
B05: EZH2i_d7
C05: EEDi_d7

Metadata_Site represents a cluster of cells, where each cell has a number assigned ObjectNumber.

library(tidyverse)
library(ggplot2)
library(ggpubr)

source("./code/globals.R")

knitr::opts_chunk$set(error=FALSE, warning=FALSE, message=FALSE)
knitr::opts_chunk$set(dev = c('png', 'my_svg'), fig.ext = c("png", "svg"), fig.width = 8, fig.height = 8)
old <- theme_set(theme_classic())

expr <- read.table(file.path(params$datadir, "INH_well5_Nuclei.txt"), header = TRUE)

expr$Metadata_Well <-
  factor(
    expr$Metadata_Well,
    levels = c("A05", "B05", "C05"),
    labels = c("WT", "EZH2i", "EEDi")
  )

well <- filter(expr, expr$Intensity_MeanIntensity_c4_raw < 0.025 
               & Intensity_IntegratedIntensity_c4_raw < 30)

H3K27m3 vs GATA3

df <-
  well %>% dplyr::select(
    Metadata_Well,
    ObjectNumber,
    Metadata_Site,
    Intensity_MeanIntensity_c1_raw,
    Intensity_MeanIntensity_c2_raw,
    Intensity_MeanIntensity_c3_raw,
    Intensity_MeanIntensity_c4_raw
  ) %>%
  rename(
    NANOG = Intensity_MeanIntensity_c1_raw,
    H3K27m3 = Intensity_MeanIntensity_c2_raw,
    GATA3 = Intensity_MeanIntensity_c3_raw,
    DAPI = Intensity_MeanIntensity_c4_raw,
    Well = Metadata_Well,
    Cell = ObjectNumber,
    Site = Metadata_Site
  )

df_long <- df %>% pivot_longer(!c(Well, Cell, Site), names_to = "channel", values_to = "value")

my_comparisons <- list(c("EZH2i", "WT"), c("EEDi", "WT"))

ggplot(df_long %>% filter(channel != "DAPI"), aes(y=value, x=Well, color=Well)) + 
  geom_boxplot() + 
  facet_wrap(. ~ channel, scales = "free_y", nrow = 1) + 
  stat_compare_means(comparisons = my_comparisons, method = "t.test") +
  scale_color_manual(values = c("grey", "#4682B4", "#8B0000"))

Version	Author	Date
0b22445	C. Navarro	2022-02-04

summaries <- df %>% group_by(Well) %>% summarise(cutoff = mean(GATA3)*1.5)
cutoff <- summaries[summaries$Well == "WT", "cutoff"][[1]]

GATA3 vs NANOG and H3K27m3

GATA3 vs H3K27m3 per nucleus

ggplot(df, aes(x = H3K27m3, y = GATA3, color = Well)) + 
  geom_point(alpha = 0.7, size = 2) + 
  scale_color_manual(values = c("grey", "#4682B4", "#8B0000"))  + 
  geom_hline(yintercept =cutoff, linetype = "dotted")

Version	Author	Date
0b22445	C. Navarro	2022-02-04

GATA3 vs NANOG per nucleus

ggplot(df, aes(x = NANOG, y = GATA3, color = Well)) + 
  geom_point(alpha = 0.7, size = 2) + 
  scale_color_manual(values = c("grey", "#4682B4", "#8B0000")) + 
  geom_hline(yintercept =cutoff, linetype = "dotted")

Version	Author	Date
0b22445	C. Navarro	2022-02-04

# Total number of cells
perc_positive <- right_join(df %>% group_by(Well) %>% summarise(n_total = n()),
                            df %>% group_by(Well) %>% filter(GATA3 > cutoff) %>% 
                              summarise(n_positive = n()), by="Well") %>%
  mutate(perc_positive = (n_positive / n_total)*100)

perc_positive

# A tibble: 2 × 4
  Well  n_total n_positive perc_positive
  <fct>   <int>      <int>         <dbl>
1 EZH2i     235         28         11.9 
2 EEDi      270          7          2.59

H3K27m3 Timecourse

Channels:

c1: NANOG
c2: H3K37m3
c3: OCT3/4
c4: DAPI