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CELLX_analysis.Rmd
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CELLX_analysis.Rmd
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---
title: "CELLX analysis"
author: "Mikhail Dozmorov"
date: "`r Sys.Date()`"
always_allow_html: yes
output:
pdf_document:
toc: no
html_document:
theme: united
toc: yes
editor_options:
chunk_output_type: console
---
```{r setup, echo=FALSE, message=FALSE, warning=FALSE}
# Set up the environment
library(knitr)
opts_chunk$set(cache.path='cache/', fig.path='img/', cache=F, tidy=T, fig.keep='high', echo=F, dpi=100, warnings=F, message=F, comment=NA, warning=F, results='as.is', fig.width = 10, fig.height = 6) #out.width=700,
library(pander)
panderOptions('table.split.table', Inf)
set.seed(1)
library(dplyr)
options(stringsAsFactors = FALSE)
```
```{r libraries, include=FALSE}
library(knitr)
library(ggplot2)
library(plotly)
library(writexl)
library(readr)
library(cowplot)
```
# Data from CELLX
- Go to [http://54.149.52.246/cgi-bin/RPPA/cellx.cgi](http://54.149.52.246/cgi-bin/RPPA/cellx.cgi)
- Select "Expression" tab out of "CNV"/"Expression"/"Mutation"/"Other" tabs
- Select the "RSEM-barplot" option on the sidebar. Read more about RSEM selected_gene expression measures at [https://deweylab.github.io/RSEM/](https://deweylab.github.io/RSEM/)
- Select any cancer-associated selected_gene from [http://cancer.sanger.ac.uk/census](http://cancer.sanger.ac.uk/census), e.g., "ERBB2". Alternatively, use any selected_gene name you may find biologically interesting
- Enter lower-case selected_gene name into the "HUGO" textbox at the bottom of the page
- Click "Submit" - the page will refresh in ~20 sec
- Save the tab-separated data using "Download table" link into `data/RSEM_expression_GENESYMBOL.data.tsv`
- Import the downloaded data into R
```{r settings}
selected_gene <- "TMEM219" # Change
min_number_of_normal <- 12 # Minimum number of samples to conduct a test
n_top <- 15 # How many top results to use
fileNameIn1 <- paste0("data/RSEM_expression_", selected_gene, ".data.tsv") # Input data
fileNameOut1 <- paste0("results/CELLX_analysis_", selected_gene, ".xlsx") # Output results
```
# Gene `r selected_gene` analysis
```{r data.setup, include=F}
# Read annotations
cancers <- openxlsx::read.xlsx("data.TCGA/TCGA_cancers.xlsx")
cells <- read_tsv("data.TCGA/CCLE_Cell_lines_annotations_20181226.txt")
cells <- mutate(cells, Cell.ID = as.character(sapply(cells$CCLE_ID, function(x) strsplit(x, "_")[[1]][1])) )# Add cell ID column
# Read expression data
expr <- read.table(fileNameIn1, header = TRUE, stringsAsFactors = FALSE)
samples <- unique(expr$affy_source)
cancer <- sort(samples[substr(samples, (nchar(samples) - 1), nchar(samples)) != "_N"])
normal <- sort(samples[substr(samples, (nchar(samples) - 1), nchar(samples)) == "_N"])
expr$tissue <- factor(NA, levels = c("Cancer", "Normal"))
expr$tissue[expr$affy_source %in% cancer == T] <- "Cancer"
expr$tissue[expr$affy_source %in% cancer == F] <- "Normal"
# Separate CCLE and TCGA expression
expr_ccle <- expr[ grep("CCLE", expr$affy_source, invert = FALSE), ]
expr_tcga <- expr[ grep("CCLE", expr$affy_source, invert = TRUE), ]
expr_tcga$affy_source <- as.character(sapply(expr_tcga$affy_source, function(x) strsplit(x, "-")[[1]][2]))
expr_tcga$affy_source <- paste(expr_tcga$affy_source, expr_tcga$tissue, sep = "_")
# Attach annotations
# intersect(expr_ccle$cell, cells$Cell.ID) %>% length
expr_ccle <- left_join(expr_ccle, cells, by = c("cell" = "Cell.ID")) # ToDo: Better merging, currently many missing
# Sort
expr_ccle <- expr_ccle[order(expr_ccle[, colnames(expr_ccle) == selected_gene], decreasing = TRUE), ]
expr_tcga <- expr_tcga[order(expr_tcga[, colnames(expr_tcga) == selected_gene], decreasing = TRUE), ]
expr_tcga_levels <- aggregate(expr_tcga[, colnames(expr_tcga) == selected_gene], by = list(expr_tcga$affy_source), mean)
expr_tcga$affy_source <- factor(expr_tcga$affy_source, levels = expr_tcga_levels[order(expr_tcga_levels$x, decreasing = TRUE), "Group.1"])
```
# CCLE expression
## Top `r n_top` cell lines with the _highest_ log2(RSEM) expression of `r selected_gene`
```{r}
expr_ccle_top <- expr_ccle[1:n_top, colnames(expr_ccle) %in% c("cell", selected_gene, "CCLE_ID")]
rownames(expr_ccle_top) <- NULL
pander(expr_ccle_top)
```
## Top `r n_top` cell lines with the _lowest_ log2(RSEM) expression of `r selected_gene`
```{r}
expr_ccle_top <- expr_ccle[(nrow(expr_ccle) - n_top):nrow(expr_ccle), colnames(expr_ccle) %in% c("cell", selected_gene, "CCLE_ID")]
rownames(expr_ccle_top) <- NULL
pander(expr_ccle_top)
```
# Cancer vs. normal `r selected_gene` log2(RSEM) expression boxplots, all cancers
```{r expression.plot, fig.height=5}
rsem <- ggplot(expr_tcga, aes(affy_source, eval(parse(text = selected_gene)))) +
geom_boxplot(aes(fill = tissue), outlier.shape = 1) +
scale_fill_manual(name = "", values = c("red", "green")) +
theme_cowplot() +
theme(axis.text.x = element_text(angle = 90, hjust = 1), panel.grid.major = element_blank()) +
labs(title = paste("Tumor-Normal Expression, ", selected_gene), x = NULL) +
ylab("log2(RSEM) gene expression") +
theme(legend.position="bottom")
rsem
```
\pagebreak
# Differential expression of selected_gene `r selected_gene` between tumor and normal tissues
The table below displays differential expression statistics comparing the expression levels of `r selected_gene` in normal versus cancer tissues (where at least `r min_number_of_normal` normal samples are available) by Welch two-sample t-test.
The table is sorted by the absolute "Fold_change" level, from largest to smallest. Positive/negative log2 Fold change indicates the gene is up/down in cancer samples, respectively, and "p-value" indicates the level of differential expression.
```{r expression.t.table}
# # Check if all normal tissues have a cancer tissue counterpart and vice versa
# table(substr(normal,1,(nchar(normal)-2)) %in% cancer)
# table(cancer %in% substr(normal,1,(nchar(normal)-2)))
cancer <- unique(sapply(as.character(expr_tcga$affy_source), function(x) strsplit(x, "_")[[1]][1]) %>% as.character) %>% sort
expr_tcga$affy_source <- as.character(expr_tcga$affy_source)
# Set up object for storing t-test results
t.table <- data.frame(Cancer_name = NA,
Normal = NA,
Cancer = NA,
Mean_expression_normal = NA,
Mean_expression_cancer = NA,
Fold_change = NA,
p_value = NA)
# Loop over all cancer tissue types to extract information
for (i in 1:length(cancer)) {
if(sum(expr_tcga$affy_source == paste0(cancer[i], "_Normal")) >= min_number_of_normal) {
t.table[i, "Cancer_name"] <- cancer[i]
t.table[i, c("Normal", "Cancer")] <- c(length(expr_tcga$affy_source == paste0(cancer[i], "_Normal")),
length(expr_tcga$affy_source == paste0(cancer[i], "_Cancer")))
t.table[i, c("Mean_expression_normal", "Mean_expression_cancer")] <-
c(expr_tcga_levels$x[expr_tcga_levels$Group.1 == paste0(cancer[i], "_Normal")],
expr_tcga_levels$x[expr_tcga_levels$Group.1 == paste0(cancer[i], "_Cancer")])
t.table[i, "Fold_change"] <- signif( (t.table$Mean_expression_cancer[i] - t.table$Mean_expression_normal[i]) )
res <- t.test(expr_tcga[expr_tcga$affy_source == paste0(cancer[i], "_Normal"), selected_gene],
expr_tcga[expr_tcga$affy_source == paste0(cancer[i], "_Cancer"), selected_gene], alternative = "two.sided")
t.table[i, "p_value"] <- res$p.value
}
}
t.table <- t.table[complete.cases(t.table), ]
t.table <- t.table[ order(abs(as.numeric(t.table$Fold_change)), decreasing = TRUE), ]
t.table <- left_join(t.table, cancers, by = c("Cancer_name" = "Acronym"))
t.table <- t.table[, c("Cancer_name", "Cancer.Name", "Fold_change", "p_value", "Mean_expression_normal", "Mean_expression_cancer")]
t.table$p_value <- formatC(as.numeric(t.table$p_value), format = "e", digits = 2)
colnames(t.table) <- c("TCGA ID", "Description", "log2 Fold change", "p-value", "Mean log2(RSEM) normal", "Mean log2(RSEM) cancer")
rownames(t.table) <- NULL # Drop rownames
# Visualize the most important
pander(t.table)
```
\pagebreak
# Cancer vs. normal `r selected_gene` log2(RSEM) expression boxplots, individual cancers
The figure below is a plot that highlights the differences in `r selected_gene` expression distribution in normal and cancer tissues for each cancer type (where at least `r min_number_of_normal` normal samples are available).
```{r interactive.plot, warning=F, fig.height=12}
# Add t test info to dataset to display means and test results in the graph
expr_tcga$Cancer <- sapply(expr_tcga$affy_source, function(x) strsplit(x, "_")[[1]][1])
expr2 <- c()
for (i in cancer) {
if(sum(expr_tcga$affy_source == paste0(i, "_Normal")) > min_number_of_normal &
sum(expr_tcga$affy_source == paste0(i, "_Cancer")) > min_number_of_normal) {
expr2 <- rbind(expr2, expr_tcga[expr_tcga$affy_source == paste0(i, "_Normal") | expr_tcga$affy_source == paste0(i, "_Cancer"), ])
}
}
# expr2 <- expr2[!is.na(expr2$Mean_expression_normal), ]
expr2$tissue <- relevel(expr2$tissue, ref = "Normal") # Make normal boxplot plotted first
## Boxplot version: looks nice but the hover text doesn't work for displaying t.test statistics
rsem_int <- ggplot(expr2, aes(tissue, eval(parse(text = selected_gene)))) +
geom_boxplot(aes(fill = tissue), outlier.shape = 1) +
scale_fill_manual(name = "", values = c("green", "red")) +
theme_cowplot() +
theme(axis.text.x = element_text(angle = 90, hjust = 1), panel.grid.major = element_blank()) +
labs(title = paste("Tumor-Normal Expression, ", selected_gene, "\n"), x = NULL) +
facet_wrap(~Cancer, ncol = 7) + guides(fill = F) +
ylab("log2(RSEM) gene expression")
# ggplotly(rsem_int, tooltip = c("colour", "text"))
plot(rsem_int)
ggsave(paste0("CELLX_", selected_gene, ".pdf"), width = 6, height = 5)
```
```{r}
# Save all results
x <- list(TCGA = t.table, CCLE = expr_ccle)
write_xlsx(x, fileNameOut1)
```
```{r interactive.coord.plot, eval=FALSE}
# Cancer vs. normal expression~significance plot
# The figure below is an interactive plot that highlights the relationship between *`r names(expr)[3]`* expression in normal and cancerous tissues for each type of cancer, colored by the significance of the differences.
# Coordinate plot version
t.int <- ggplot(t.table[complete.cases(t.table), ], aes(Mean_expression_normal, Mean_expression_cancer)) +
geom_point(aes(text = paste("Name:", Acronym, ", t:", t_statistic), color = p_value)) + labs(title = paste("Expression of", selected_gene, "Across Cancer Types"), x = "\nExpression Level in Normal Tissue", y = "Expression Level in Cancer Tissue\n") +
scale_color_gradient(low = "red", high = "black")
ggplotly(t.int)
```