## ----style, echo = FALSE, results = 'asis'---------------------------------
BiocStyle::markdown()
## ----load_libraries, message=FALSE, warning=FALSE, echo=TRUE---------------
library("dplyr")
library("ggplot2")
library("viridis")
library("tibble")
library("gridExtra")
library("stringr")
library("depmap")
library("ExperimentHub")
## ----load_data, message=FALSE, warning=FALSE, echo=TRUE--------------------
## create ExperimentHub query object
eh <- ExperimentHub()
query(eh, "depmap")
rnai <- eh[["EH2260"]]
crispr <- eh[["EH2261"]]
copyNumber <- eh[["EH2262"]]
# note: the datasets listed above are from the 19Q1 release. Newer datasets,
# such as 19Q2 and 19Q3 are available.
## ----dep_score_BRCA1_184A1Breast, echo=TRUE--------------------------------
dep_score_BRCA1_184A1Breast <- rnai %>%
select(cell_line, gene_name, dependency) %>%
filter(cell_line == "184A1_BREAST",
gene_name == "BRCA1")
dep_score_BRCA1_184A1Breast
## ---- BRCA1_Avg_Dep_Score, echo=TRUE---------------------------------------
brca1_dep_score_avg_rnai <- rnai %>%
select(gene_name, dependency) %>%
filter(gene_name == "BRCA1") %>%
summarise(mean_dependency_brca1 =
mean(dependency, na.rm=TRUE))
brca1_dep_score_avg_rnai
## ---- all_gene_ds_avg_rnai, echo=TRUE--------------------------------------
all_gene_dep_score_avg_rnai <- rnai %>%
select(gene_name, dependency) %>%
summarise(mean_dependency_all_genes_rnai =
mean(dependency, na.rm=TRUE))
all_gene_dep_score_avg_rnai
## ---- soft_tissue_cell_lines, echo=TRUE------------------------------------
soft_tissue_dependency_rnai <- rnai %>%
select(cell_line, gene_name, dependency) %>%
filter(stringr::str_detect(cell_line,
"SOFT_TISSUE")) %>%
arrange(dependency)
soft_tissue_dependency_rnai
## ----cell_lines_with_entrez_id_NRF2, echo=TRUE-----------------------------
entrez_id_NRF2 <- rnai %>%
select(entrez_id, cell_line, gene_name, dependency) %>%
filter(entrez_id == "4780")
entrez_id_NRF2
## ----greatest_Dep_Score_NFE2L2, echo=TRUE----------------------------------
top_dep_score_NFE2L2_rnai <- rnai %>%
select(cell_line, gene_name, dependency) %>%
filter(gene_name == "NFE2L2") %>%
arrange(dependency)
top_dep_score_NFE2L2_rnai
## ----top_dep_score_NCIH2066_LUNG_rnai, echo=TRUE---------------------------
top_dep_score_NCIH2066_LUNG_rnai <- rnai %>%
select(cell_line, gene_name, dependency) %>%
filter(cell_line == "NCIH2066_LUNG") %>%
arrange(dependency)
top_dep_score_NCIH2066_LUNG_rnai
## ----greatest_dep_score_gene_rnai, echo=TRUE-------------------------------
greatest_dep_score_gene_rnai <- rnai %>%
select(cell_line, gene_name, dependency) %>%
arrange(dependency)
greatest_dep_score_gene_rnai
## ----cell_line_gene_rnai_lowest_dep_score, echo=TRUE-----------------------
lowest_dep_score_gene_rnai <- rnai %>%
select(cell_line, gene_name, dependency) %>%
arrange(desc(dependency))
lowest_dep_score_gene_rnai
## ----cell_line_gene_crispr_greatest_dep_score, echo=TRUE-------------------
greatest_dep_score_gene_crispr <- crispr %>%
select(cell_line, gene_name, dependency) %>%
arrange(dependency)
greatest_dep_score_gene_crispr
## ----cell_line_gene_crispr_lowest_dep_score, echo=TRUE---------------------
lowest_dep_score_gene_crispr <- crispr %>%
select(cell_line, gene_name, dependency) %>%
arrange(desc(dependency))
lowest_dep_score_gene_crispr
## ----comparison_rnai_crispr_dep_scores, fig.height=6, fig.width=7, fig.align="center", echo=FALSE, message=FALSE----
# sort `crispr` dep scores by most cancer inducing
top_20_dep_scores_crispr <- crispr %>%
select(gene_name, dependency) %>%
arrange(desc(dependency)) %>%
top_n(20)
# sort `crispr` dep scores by least cancer inducing
lowest_20_dep_scores_crispr <- crispr %>%
select(gene_name, dependency) %>%
arrange(dependency) %>%
top_n(-20)
# sort `rnai` dep scores by most cancer inducing
top_20_dep_scores_rnai <- rnai %>%
select(gene_name, dependency) %>%
arrange(desc(dependency)) %>%
top_n(20)
# shorten gene name so it fits on plot
top_20_dep_scores_rnai[12, 1] <- "GAS6-AS2"
# sort `rnai` dep scores by least cancer inducing
lowest_20_dep_scores_rnai <- rnai %>%
select(gene_name, dependency) %>%
arrange(dependency) %>%
top_n(-20)
# rnai highest dep scores
p1 <- ggplot(lowest_20_dep_scores_rnai) +
aes(gene_name, dependency, color = dependency) +
geom_point() + scale_colour_viridis() +
theme(axis.text.x = element_text(angle=90, hjust=1)) +
ggtitle("High DS genes rnai")
# crispr highest dep scores
p2 <- ggplot(lowest_20_dep_scores_crispr) +
aes(gene_name, dependency, color = dependency) +
geom_point() + scale_colour_viridis() +
theme(axis.text.x = element_text(angle=90, hjust=1)) +
ggtitle("High DS genes crispr ")
# rnai lowest dep scores
p3 <- ggplot(top_20_dep_scores_rnai) +
aes(gene_name, dependency, color = dependency) +
geom_point() + scale_colour_viridis() +
theme(axis.text.x = element_text(angle=90, hjust=1)) +
ggtitle("Low DS genes rnai")
# crispr lowest dep scores
p4 <- ggplot(top_20_dep_scores_crispr) +
aes(gene_name, dependency, color = dependency) +
geom_point() + scale_colour_viridis() +
theme(axis.text.x = element_text(angle=90, hjust=1)) +
ggtitle("Low DS genes crispr")
#plot as 1x2 grid
grid.arrange(p1, p2, p3, p4, nrow=2,
top = "Most Extreme Dep Scores for CRISPR and RNAI")
## ----count_comp_rnai_crispr_dep_scores, fig.height=6, fig.width=7, fig.align="center", echo=FALSE, message=FALSE----
# get counts of top 50 genes in `crispr` that are most cancer-vitality inducing
unique_lowest_dep_scores_gene_crispr <- crispr %>%
select(gene_name, dependency) %>%
arrange(desc(dependency)) %>%
top_n(50) %>%
count(gene_name) %>%
arrange(desc(n))
# TBC1D3 appears to be an extremely common for `crispr` and dominates top 100
# most cancer-vitality-inducing genes.
# get counts of top 50 genes in `rnai` that are most cancer-vitality inducing
unique_lowest_dep_scores_gene_rnai <- rnai %>%
select(gene_name, dependency) %>%
arrange(desc(dependency)) %>%
top_n(50) %>%
count(gene_name) %>%
arrange(desc(n))
# Whereas for `rnai` UBBP4 and ACTG1P4 are most common.
# shorten gene name to fit on plot
unique_lowest_dep_scores_gene_rnai[9, 1] <- "GAS6-AS2"
# get counts of top 50 genes in `crispr` with greatest dependency
unique_top_dep_scores_crispr <- crispr %>%
select(gene_name, dependency) %>%
arrange(dependency) %>%
top_n(-50) %>%
count(gene_name) %>%
arrange(desc(n))
# Most common gene for top 100 most dependent genes in `crispr` is RAN
# get counts of top 50 genes in `rnai` with greatest dependency
unique_top_dep_scores_rnai <- rnai %>%
select(gene_name, dependency) %>%
arrange(dependency) %>%
top_n(-50) %>%
count(gene_name) %>%
arrange(desc(n))
# Most common genes for top 100 most dependent genes in `rnai` are RPL7, EIF3B
# and RPL14.
# rnai highest dep scores
p5 <- ggplot(unique_top_dep_scores_crispr, aes(x=gene_name, y=n)) +
geom_bar(stat='identity', fill="steelblue2") +
ylab("count") + theme(axis.text.x = element_text(angle=90, hjust=1)) +
ggtitle("Highest DS genes crispr")
p6 <- ggplot(unique_top_dep_scores_rnai, aes(x=gene_name, y=n)) +
geom_bar(stat='identity', fill="steelblue2") +
ylab("count") + theme(axis.text.x = element_text(angle=90, hjust=1)) +
ggtitle("Highest DS genes rnai")
p7 <- ggplot(unique_lowest_dep_scores_gene_crispr, aes(x=gene_name, y=n)) +
geom_bar(stat='identity', fill="steelblue2") +
ylab("count") + theme(axis.text.x = element_text(angle=90, hjust=1)) +
ggtitle("Lowest DS genes crispr")
p8 <- ggplot(unique_lowest_dep_scores_gene_rnai, aes(x=gene_name, y=n)) +
geom_bar(stat='identity', fill="steelblue2") +
ylab("count") + theme(axis.text.x = element_text(angle=90, hjust=1)) +
ggtitle("Lowest DS genes rnai")
## plot as 1x2 grid
grid.arrange(p5, p6, p7, p8, nrow=2,
top = "Top 50 CRISPR and RNAI genes with High and Low Dep Score")
## ----mean_log_copy_num_gene, fig.height=4, fig.width=6, fig.align="center", echo=FALSE, message=FALSE----
# mean log copy number for all genes
mean_log_copy_num_gene <- copyNumber %>%
select(gene_name, log_copy_number) %>%
summarise(mean_log_copy_number_all_genes =
mean(log_copy_number, na.rm = TRUE))
# get average value
val_mean_log_copy_num_gene <- as.numeric(mean_log_copy_num_gene[1,1])
# mean log copy number for each gene
each_log_copy_num_gene <- copyNumber %>%
select(gene_name, log_copy_number) %>%
group_by(gene_name) %>%
summarise(mean_log_copy_number =
mean(log_copy_number, na.rm = TRUE))
# add an ID column
all_log_copy_num_gene <-
as.data.frame(na.omit(each_log_copy_num_gene$mean_log_copy_number)) %>%
tibble::rowid_to_column(., "ID")
# add col names
colnames(all_log_copy_num_gene, do.NULL = FALSE)
colnames(all_log_copy_num_gene) <- c("gene", "log_copy_number")
# plot of mean copy number for every gene
p9 <- ggplot(all_log_copy_num_gene) +
aes(x=gene, y=log_copy_number, color = log_copy_number) +
geom_point() + scale_colour_viridis() +
geom_hline(yintercept = val_mean_log_copy_num_gene,
linetype = "dashed", color = "red") +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
ggtitle("Mean log copy number change for every gene")
p9
## ----greatest_log_copy_num_genes, fig.height=4, fig.width=6, fig.align="center", echo=FALSE, message=FALSE----
# mean log copy number for each gene
greatest_log_copy_num_genes <- copyNumber %>%
select(gene_name, log_copy_number) %>%
group_by(gene_name) %>%
summarise(mean_long_copy_num =
mean(log_copy_number))%>%
arrange(desc(mean_long_copy_num)) %>%
top_n(20)
p10 <- ggplot(greatest_log_copy_num_genes, aes(x=gene_name,
y=mean_long_copy_num)) +
geom_bar(stat='identity', fill="steelblue2") +
ylab("log Copy Number") +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
ggtitle("Top 20 genes with largest log copy number")
p10
## ----echo = FALSE----------------------------------------------------------
sessionInfo()