Note that the CSV file paths may be different for you.
# Adapted from source: https://combine-australia.github.io/RNAseq-R/09-applying-rnaseq-solutions.html
install.packages("BiocManager")
BiocManager::install(c("limma"))
BiocManager::install(c("edgeR"))
BiocManager::install(c("org.Dm.eg.db"))
BiocManager::install(c("gplots"))
BiocManager::install(c("RColorBrewer"))
library(limma)
library(edgeR)
library(gplots)
library(RColorBrewer)
library(org.Dm.eg.db)
counts <- read.delim(file="/workspace/data/datastudios-demo-rstudio/input/2024-05-13/counts_Drosophila.txt")
targets <- read.delim(file="/workspace/data/datastudios-demo-rstudio/input/2024-05-13/SampleInfo_Drosophila.txt")
head(counts)
targets
table(targets$Group)
mycpm <- cpm(counts)
plot(counts[,1],mycpm[,1],xlim=c(0,20),ylim=c(0,5))
abline(v=10,col=2)
abline(h=2,col=4)
thresh <- mycpm > 2
keep <- rowSums(thresh) >= 3
table(keep)
counts.keep <- counts[keep,]
dim(counts.keep)
y <- DGEList(counts.keep)
barplot(y$samples$lib.size)
par(mfrow=c(1,1))
# Get log2 counts per million
logcpm <- cpm(y$counts,log=TRUE)
# Check distributions of samples using boxplots
boxplot(logcpm, xlab="", ylab="Log2 counts per million",las=2,outline=FALSE)
# Let's add a blue horizontal line that corresponds to the median logCPM
abline(h=median(logcpm),col="blue")
title("Boxplots of logCPMs (unnormalised)")
par(mfrow=c(1,2),oma=c(2,0,0,0))
group.col <- c("red","blue")[targets$Group]
boxplot(logcpm, xlab="", ylab="Log2 counts per million",las=2,col=group.col,
pars=list(cex.lab=0.8,cex.axis=0.8))
abline(h=median(logcpm),col="blue")
title("Boxplots of logCPMs\n(coloured by groups)",cex.main=0.8)
lib.col <- c("light pink","light green")[targets$Library]
boxplot(logcpm, xlab="", ylab="Log2 counts per million",las=2, col=lib.col,
pars=list(cex.lab=0.8,cex.axis=0.8))
abline(h=median(logcpm),col="blue")
title("Boxplots of logCPMs\n(coloured by library prep)",cex.main=0.8)
par(mfrow=c(1,2))
plotMDS(y,col=group.col)
legend("topright",legend=levels(targets$Group),fill=c("red","blue"))
plotMDS(y,col=lib.col)
legend("topleft",legend=levels(targets$Library),fill=c("light pink","light green"))
logcounts <- cpm(y,log=TRUE)
var_genes <- apply(logcounts, 1, var)
select_var <- names(sort(var_genes, decreasing=TRUE))[1:500]
highly_variable_lcpm <- logcounts[select_var,]
dim(highly_variable_lcpm)
mypalette <- brewer.pal(11,"RdYlBu")
morecols <- colorRampPalette(mypalette)
heatmap.2(highly_variable_lcpm,col=rev(morecols(50)),trace="none", main="Top 500 most variable genes across samples",ColSideColors=group.col,scale="row",margins=c(10,5))install.packages("dplyr")
install.packages("gapminder")
install.packages("ggplot2")
install.packages("shiny")
library(shiny)
library(dplyr)
library(ggplot2)
library(gapminder)
# Specify the application port
options(shiny.host = "0.0.0.0")
options(shiny.port = 8180)
ui <- fluidPage(
sidebarLayout(
sidebarPanel(
tags$h4("Gapminder Dashboard"),
tags$hr(),
selectInput(inputId = "inContinent", label = "Continent", choices = unique(gapminder$continent), selected = "Europe")
),
mainPanel(
plotOutput(outputId = "outChartLifeExp"),
plotOutput(outputId = "outChartGDP")
)
)
)
server <- function(input, output, session) {
# Filter data and store as reactive value
data <- reactive({
gapminder %>%
filter(continent == input$inContinent) %>%
group_by(year) %>%
summarise(
AvgLifeExp = round(mean(lifeExp)),
AvgGdpPercap = round(mean(gdpPercap), digits = 2)
)
})
# Common properties for charts
chart_theme <- ggplot2::theme(
plot.title = element_text(hjust = 0.5, size = 20, face = "bold"),
axis.title.x = element_text(size = 15),
axis.title.y = element_text(size = 15),
axis.text.x = element_text(size = 12),
axis.text.y = element_text(size = 12)
)
# Render Life Exp chart
output$outChartLifeExp <- renderPlot({
ggplot(data(), aes(x = year, y = AvgLifeExp)) +
geom_col(fill = "#0099f9") +
geom_text(aes(label = AvgLifeExp), vjust = 2, size = 6, color = "#ffffff") +
labs(title = paste("Average life expectancy in", input$inContinent)) +
theme_classic() +
chart_theme
})
# Render GDP chart
output$outChartGDP <- renderPlot({
ggplot(data(), aes(x = year, y = AvgGdpPercap)) +
geom_line(color = "#f96000", size = 2) +
geom_point(color = "#f96000", size = 5) +
geom_label(
aes(label = AvgGdpPercap),
nudge_x = 0.25,
nudge_y = 0.25
) +
labs(title = paste("Average GDP per capita in", input$inContinent)) +
theme_classic() +
chart_theme
})
}
shinyApp(ui = ui, server = server)Note that the CSV file path may be different for you.
install.packages("shiny")
install.packages("plotly")
install.packages("tidyverse")
library(shiny)
library(plotly)
library(tidyverse)
ui <- fluidPage(
titlePanel("Volcano Plotly"),
fluidRow(
column(
width = 7,
plotlyOutput("volcanoPlot", height = "500px")
),
column(
width = 5,
dataTableOutput("selectedProbesTable")
)
)
)
csv_file = "/workspace/data/datastudios-demo-rstudio/input/2024-05-20_volcano-examples/NKI-DE-results.csv"
server <- function(input, output) {
differentialExpressionResults <-
read.csv(csv_file, stringsAsFactors = FALSE) %>%
mutate(
probe.type = factor(ifelse(grepl("^Contig", probe), "EST", "mRNA")),
minusLog10Pvalue = -log10(adj.P.Val),
tooltip = ifelse(is.na(HUGO.gene.symbol), probe, paste(HUGO.gene.symbol, " (", probe, ")", sep = ""))
) %>%
sample_n(1000)
output$volcanoPlot <- renderPlotly({
plot <- differentialExpressionResults %>%
ggplot(aes(x = logFC,
y = minusLog10Pvalue,
colour = probe.type,
text = tooltip,
key = row.names(differentialExpressionResults))) +
geom_point() +
xlab("log fold change") +
ylab("-log10(P-value)")
plot %>%
ggplotly(tooltip = "tooltip") %>%
layout(dragmode = "select")
})
output$selectedProbesTable <- renderDataTable({
eventData <- event_data("plotly_selected")
selectedData <- differentialExpressionResults %>% slice(0)
if (!is.null(eventData)) selectedData <- differentialExpressionResults[eventData$key,]
selectedData %>%
transmute(
probe,
gene = HUGO.gene.symbol,
`log fold change` = signif(logFC, digits = 2),
`p-value` = signif(adj.P.Val, digits = 2)
)
},
options = list(dom = "tip", pageLength = 10, searching = FALSE)
)
}
shinyApp(ui, server, options = list(height = 600))Example 4 - Load JSON lib, change working directory on startup, read CSV to data frame, export data frame to JSON file
Note that the CSV file path may be different for you.
install.packages("RJSONIO")
library(RJSONIO)
setwd("/workspace/data")
df <- read.csv("input/2024-01-16/polling_places.csv")
exportJson <- toJSON(df)
write(exportJson, "output/output.json")!pip install pandas[pyarrow] jupytext scipy jupyterlab-git qgrid seaborn nb_blackNote that the CSV file path may be different for you.
import pandas as pd
df = pd.read_csv('2024-01-16/polling_places.csv', low_memory=False)
df
df.to_json('output.json')TBD