HGG-oncohistones project [source]
06 - Effect of ACVR1
Selin Jessa []
12 September, 2022
1 Configuration
Configuration of project directory & analysis outputs:
Show full config
source(here("rr_helpers.R"))
# Set up outputs
message("Document index: ", doc_id)## Document index: 06# Specify where to save outputs
out <- here("output", doc_id); dir.create(out, recursive = TRUE)
figout <- here("figures", doc_id); dir.create(figout, recursive = TRUE)
cache <- paste0(readLines(here("include/project_root.txt")), basename(here()), "/", doc_id, "/")Outputs and figures will be saved at these paths, relative to project root:
## public/output/06## public/figures/06Setting a random seed:
set.seed(100)2 Overview
This document contains analyses to understand the dependency of ACVR1 in K27M and especially H3.1K27M gliomas, and to interrogate the changes that result from knocking-out ACVR1 using CRISPR in ACVR1 mutant HGG cell lines, as shown in Figure 6.
3 Libraries
# Load libraries here
library(here)
library(tidyr)
library(dplyr)
library(ggrepel)
library(readr)
library(readxl)
library(glue)
library(tibble)
library(ggplot2)
library(purrr)
library(stringr)
library(cowplot)
library(fgsea)
library(icytobox)
source(here("include/style.R")) # contains palettes and custom style elements
source(here("code/functions/RNAseq.R"))
source(here("code/functions/ssGSEA.R"))
ggplot2::theme_set(theme_min())4 ID genes ddPCR
ddPCR assay of ID gene expression.
# load data
ddpcr <- suppressMessages(read_tsv(here("data/experimental/2022-03-10-ddPCR_ID_genes_@mhulswit.txt")))
# barplot
ddpcr_tidy <- ddpcr %>%
# remove xenografts
filter(!grepl("[Xx]eno", Sample)) %>%
gather(Stat, Value, matches("ID")) %>%
separate(Stat, into = c("Gene", "Stat"), sep = "_") %>%
spread(Stat, Value) %>%
separate(Sample, into = c("Cell_line", "CRISPR"), sep = "_") %>%
mutate(Condition = factor(Condition, levels = c("ACVR1 mutant", "ACVR1 KO")))
ddpcr_tidy %>%
rr_ggplot(aes(x = Condition, y = fold), plot_num = 1) +
geom_bar(aes(fill = Condition), stat = "identity", width = 0.5) +
geom_errorbar(aes(ymin = fold - sd, ymax = fold + sd), width = 0.25) +
scale_fill_manual(values = palette_acvr1) +
facet_grid(Cell_line ~ Gene, scales = "free_x", space = "free", drop = TRUE) +
ylim(c(0, 1.2)) +
rotate_x()## ...writing source data of ggplot to public/figures/06/ddpcr_id_genes-1.source_data.tsv
[figure @ public/figures/06/ddpcr_id_genes...]
5 Clonogenesis
Data for clone formation assay for ACVR1 mutant and KO cell lines.
# load data
clono <- suppressMessages(read_tsv(here("data/experimental/clonogenic_assay_DIPG36_@am.txt")))
# define standard error to quantify uncertainty around the mean
se <- function(x) sqrt(var(x)/length(x))
# tidy & plot
clono_tidy <- clono %>%
set_colnames(c("ACVR1 mutant", "ACVR1 KO")) %>%
gather(Condition, N_clones) %>%
mutate(Condition = factor(Condition, levels = names(palette_acvr1))) %>%
filter(!is.na(N_clones))
clono_tidy %>%
group_by(Condition) %>%
summarize(N_clones_mean = mean(N_clones),
N_clones_se = se(N_clones)) %>%
rr_ggplot(aes(x = Condition, y = N_clones_mean), plot_num = 1) +
geom_bar(aes(fill = Condition), stat = "identity", width = 0.5) +
geom_errorbar(aes(ymin = N_clones_mean - N_clones_se, ymax = N_clones_mean + N_clones_se), width = 0.25) +
geom_jitter(data = clono_tidy, aes(x = Condition, y = N_clones), width = 0.2, size = 1) +
scale_fill_manual(values = palette_acvr1) +
ylim(c(0, 150))## ...writing source data of ggplot to public/figures/06/clonogenesis-1.source_data.tsv
t.test(clono$Parental, clono$`ACVR1 KO`, alternative = "two.sided", var.equal = TRUE)##
## Two Sample t-test
##
## data: clono$Parental and clono$`ACVR1 KO`
## t = 2.7602, df = 7, p-value = 0.02809
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 6.855717 88.810950
## sample estimates:
## mean of x mean of y
## 116.50000 68.66667
[figure @ public/figures/06/clonogenesis...]
6 Doubling times
Doubling times of H3.1 and H3.3K27M cell lines.
# load data
doubling_H3.1_vs_H3.3 <- suppressMessages(read_tsv(here("data/experimental/doubling_time_H3.3_vs_H3.1_@am.txt")))
# tidy & plot
doubling_H3.1_vs_H3.3_tidy <- doubling_H3.1_vs_H3.3 %>%
set_colnames(c("DIPGXIII", "HSJ-019", "DIPG36", "DIPGIV")) %>%
gather(Cell_line, Doubling_time) %>%
filter(!is.na(Doubling_time))
# use standard deviation to assesss variability of the measurements
doubling_H3.1_vs_H3.3_tidy %>%
group_by(Cell_line) %>%
summarize(Doubling_mean = mean(Doubling_time),
Doubling_sd = sd(Doubling_time)) %>%
rr_ggplot(aes(x = Cell_line, y = Doubling_mean), plot_num = 1) +
geom_bar(aes(fill = Cell_line), stat = "identity", width = 0.5) +
geom_errorbar(aes(ymin = Doubling_mean - Doubling_sd, ymax = Doubling_mean + Doubling_sd), width = 0.25) +
geom_jitter(data = doubling_H3.1_vs_H3.3_tidy, aes(x = Cell_line, y = Doubling_time), width = 0.1, size = 1) +
scale_fill_manual(values = c("DIPG36" = "orange", "DIPGIV" = "orange", "DIPGXIII" = "red3", "HSJ-019" = "red3")) +
no_legend()## ...writing source data of ggplot to public/figures/06/doubling_H3.1_vs_H3.3-1.source_data.tsv
[figure @ public/figures/06/doubling_H3.1_vs_H3.3...]
# load data
doubling_ACVR1 <- suppressMessages(read_tsv(here("data/experimental/doubling_time_DIPGIV_ACVR1mut_vs_KO_@am.txt")))
# tidy & plot
doubling_ACVR1_tidy <- doubling_ACVR1 %>%
set_colnames(c("ACVR1 mutant", "ACVR1 KO")) %>%
gather(Condition, Doubling_time) %>%
mutate(Condition = factor(Condition, levels = names(palette_acvr1))) %>%
filter(!is.na(Condition))
doubling_ACVR1_tidy %>%
group_by(Condition) %>%
summarize(Doubling_mean = mean(Doubling_time),
Doubling_sd = sd(Doubling_time)) %>%
rr_ggplot(aes(x = Condition, y = Doubling_mean), plot_num = 1) +
geom_bar(aes(fill = Condition), stat = "identity", width = 0.5) +
geom_errorbar(aes(ymin = Doubling_mean - Doubling_sd, ymax = Doubling_mean + Doubling_sd), width = 0.25) +
geom_jitter(data = doubling_ACVR1_tidy, aes(x = Condition, y = Doubling_time), width = 0.1, size = 1) +
scale_fill_manual(values = palette_acvr1) +
no_legend() +
ylim(c(0, 50))## ...writing source data of ggplot to public/figures/06/doubling_ACVR1-1.source_data.tsv
[figure @ public/figures/06/doubling_ACVR1...]
7 Reproducibility
This document was last rendered on:
## 2022-09-12 15:23:43The git repository and last commit:
## Local: master /lustre06/project/6004736/sjessa/from_narval/HGG-oncohistones/public
## Remote: master @ origin (git@github.com:fungenomics/HGG-oncohistones.git)
## Head: [1a06382] 2022-09-08: Update comments, documentation, etc, based on lab feedbackThe random seed was set with set.seed(100)
The R session info:
## Error in get(genname, envir = envir) : object 'testthat_print' not found## ─ Session info ───────────────────────────────────────────────────────────────
## setting value
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## os Rocky Linux 8.6 (Green Obsidian)
## system x86_64, linux-gnu
## ui X11
## language (EN)
## collate en_CA.UTF-8
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## tz EST5EDT
## date 2022-09-12
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