Setup Instructions

R

Install R. You’ll need R version 3.4.3 or higher.¹ Download and install R for Windows or Mac (download the latest R-3.x.x.pkg file for your appropriate version of Mac OS).

RStudio

Download and install RStudio Desktop version >= 1.1.149.

R and RStudio are separate downloads and installations. R is the underlying statistical computing environment, but using R alone is no fun. RStudio is a graphical integrated development environment that makes using R much easier. You need R installed before you install RStudio.

CRAN packages

We will need to install several core packages needed for most lessons. Launch RStudio (RStudio, not R itself). Ensure that you have internet access, then copy and paste the following commands, one-at-a-time, into the Console panel (usually the lower-left panel, by default) and hit the Enter/Return key. If you receive an error message when trying to install any particular package, please make note of which one you had trouble with, and email one of the instructors prior to class with the command you typed and the error you received.

install.packages("dplyr")
install.packages("readr")
install.packages("tidyr")
install.packages("ggplot2")

A few notes:

• Commands are case-sensitive.
• You must be connected to the internet.
• Even if you’ve installed these packages in the past, do re-install the most recent version. Many of these packages are updated often, and we may use new features in the workshop that aren’t available in older versions.
• If you’re using Windows you might see errors about not having permission to modify the existing libraries – disregard these. You can avoid this by running RStudio as an administrator (right click the RStudio icon, then click “Run as Administrator”).
• These core packages are part of the “tidyverse” ecosystem (see tidyverse.org). There is a tidyverse package that’s kind of a meta-package that automatically installs/loads all of the above packages and several other commonly used packages for data analysis that all play well together.² You could optionally install the tidyverse package instead of all these packages individually. See tidyverse.org for more.

Check that you’ve installed everything correctly by closing and reopening RStudio and entering the following command at the console window (don’t worry about any messages that look something like the following objects are masked from ...³, or Warning message: package ... was build under R version ...⁴):

library(dplyr)
library(readr)
library(tidyr)
library(ggplot2)

This may produce some notes or other output, but as long as you don’t get an error message, you’re good to go. If you get a message that says something like: Error in library(somePackageName) : there is no package called 'somePackageName', then the required packages did not install correctly. Please do not hesitate to email one of the instructors prior to class if you are still having difficulty. In this email, please copy and paste what you typed in the console, and all of the output that streams by in the console.

RMarkdown

Several additional setup steps required for the reproducible research with RMarkdown class.

1. First, install R, RStudio, and the core CRAN packages as described above. Also install the knitr and rmarkdown packages.

install.packages("knitr")
install.packages("rmarkdown")

1. Next, launch RStudio (not R). Click File, New File, R Markdown. This may tell you that you need to install additional packages (knitr, yaml, htmltools, caTools, bitops, rmarkdown, and maybe a few others). Click “Yes” to install these.
2. Optional: If you want to convert to PDF, you will need to install a \(\LaTeX\) typesetting engine. This differs on Mac and Windows. Note that this part of the installation may take up to several hours, and isn’t strictly required for the class.
   • Windows: Download and install MiKTeX: https://miktex.org/download. Read the installation tutorial first at https://miktex.org/howto/install-miktex.
   • Mac: Download and install MacTeX.pkg at http://www.tug.org/mactex/mactex-download.html. This is a very large download (>2 gigabytes), and may take a while depending on your network speed. Do this prior to the course.

Bioconductor

Install the core Bioconductor packages (more information here). These packages are installed differently than “regular” R packages from CRAN. Copy and paste these lines of code into your R console one at a time.

source("http://bioconductor.org/biocLite.R")
biocLite()

A few notes:

• We will be using the latest versions of Bioconductor from the 3.5 release. This requires R version 3.4.0 or higher. If you have R 3.4.0 installed, running the commands above will install Bioconductor 3.5. See http://bioconductor.org/news/bioc_3_5_release/.
• If at any point in the Bioconductor package installations you get prompts in the console asking you to update any existing packages, type n at the prompt at hit enter.
• If you see a note long the lines of “binary version available but the source version is later”, followed by a question, “Do you want to install from sources the package which needs compilation? y/n”, type n for no, and hit enter.

Check that you’ve installed everything correctly by closing and reopening RStudio and entering the following command at the console window:

library(BiocInstaller)

If you get a message that says something like: Error in library(BiocInstaller) : there is no package called 'BiocInstaller', then the required packages did not install correctly. Please do not hesitate to email one of the instructors prior to the course if you are still having difficulty. In this email, please copy and paste what you typed in the console, and all of the output that streams by in the console.

Survival Analysis

Prerequisites! This is not an introductory R class. This lesson assumes a basic familiarity with R, data frames, and to a lesser degree, manipulating data with dplyr and %>%, and data visualization with ggplot2.

Software setup: Follow instructions above for R+RStudio+Packages, CRAN packages, and Bioconductor. See the sections above for full instructions and troubleshooting tips.

For this class you’ll also need the survminer package from CRAN and the and RTCGA, RTCGA.clinical, RTCGA.mRNA, packages from Bioconductor.

If you receive an error message when trying to install any particular package, please make note of which one you had trouble with, and email one of the instructors prior to class with the command you typed and the error you received.

# Install core CRAN packages:
install.packages("dplyr")
install.packages("readr")
install.packages("tidyr")
install.packages("ggplot2")

# For this class, also install survminer from CRAN
install.packages("survminer")


# Install Bioconductor core packages:
source("http://bioconductor.org/biocLite.R")
biocLite()

# For this class, you'll also need RTCGA and RTCGA data packages
biocLite("RTCGA")
biocLite("RTCGA.clinical")
biocLite("RTCGA.mRNA")

Check that you’ve installed everything correctly by closing and reopening RStudio and entering the following commands one-at-a-time in the console pane:

# Test CRAN package installation:
library(dplyr)
library(readr)
library(tidyr)
library(ggplot2)

# Test survminer
library(survminer)

# Test RTCGA:
library(RTCGA)
library(RTCGA.clinical)
library(RTCGA.mRNA)

This may produce some notes or other output, but as long as you don’t get an error message, you’re good to go. If you get a message that says something like: Error in library(somePackageName) : there is no package called 'somePackageName', then the required packages did not install correctly. Please do not hesitate to email me prior to class if you are still having difficulty. In this email, please copy and paste what you typed in the console, and all of the output that streams by in the console.

Predictive modeling

Prerequisites! This is not an introductory R class. In addition to familiarity with R, this lesson also assumes familiarity with:

• Advanced data manipulating data with dplyr and %>%
• Data tidying with tidyr
• Advanced data visualization with ggplot2

Some knowledge of statistics and resampling procedures is helpful, but not strictly required.

Software setup: Follow instructions above for R+RStudio+Packages and CRAN packages. For this class, you’ll also need several additional packages described below. If you receive an error message when trying to install any particular package, please make note of which one you had trouble with, and email me prior to class with the command you typed and the error you received.

First, install the caret package, which provides a unified interface to hundreds of data mining and machine learning algorithms and a framework for model training and evaluation. This command will also install all the additional packages that caret recommends.

install.packages("caret", dependencies = c("Depends", "Suggests"))

When you do this, you may get a note asking you about installing source packages that need compilation. If you get this message, type n and hit Enter at the prompt for “no.”

There are binary versions available but the source versions are later:
                binary  source  needs_compilation
somePackage1    ....    ....               TRUE
somePackage1    ....    ....               FALSE

Do you want to install from sources the package which needs compilation?

Similarly, if you get a message that looks like this, type n and hit Enter for “no.”

Packages which are only available in source form, and may need compilation of C/C++/Fortran: ‘Rpoppler’ ‘Rmpi’
Do you want to attempt to install these from sources?

The caret package provides the utilities for interfacing with many other packages’ machine learning algorithms. We’re going to fit models using Random Forest, stochastic gradient boosting, k-Nearest Neighbors, Lasso and Elastic-Net Regularized Generalized Linear Models. These require the packages randomForest, gbm, kknn, and glmnet, respectively. We will also need the mice package for multiple imputation. The following commands will install these packages.

install.packages("randomForest")
install.packages("gbm")
install.packages("kknn")
install.packages("glmnet")
install.packages("mice")

Finally, we’ll conclude with a demonstration of forecasting, for which we’ll need the prophet package.

install.packages("prophet")

Check that you’ve installed everything correctly by closing and reopening RStudio and entering the following commands one-at-a-time in the console pane. If you get an error telling you that the package isn’t installed, try re-installing it as demonstrated above. If you’re still having trouble, email me prior to class with the command you typed to install and the error(s) you received.

library(caret)
library(randomForest)
library(gbm)
library(kknn)
library(glmnet)
library(mice)
library(prophet)

Download data we’ll use in class. You will need the following datasets from the data page:

• h7n9.csv: The slightly processed raw dataset from an influenza A H7N9 outbreak in China in 2013, published by Kucharski et al 2014. Contains the original variables, with lots of missing data throughout.
• h7n9_analysisready.csv: The “analysis-ready” dataset. This data has been cleaned up, with some “feature extraction” / variable recoding done to make the data more suitable to data mining / machine learning methods used in this class. We will start with the raw data above, but I provide this data in case you don’t make it all the way through the data cleaning and feature extraction steps we will need to perform.
• ilinet.csv: Historical flu tracking data from the CDC’s U.S. Outpatient Influenza-like Illness Surveillance Network along with data from the National Center for Health Statistics (NCHS) Mortality Surveillance System. This contains ILI totals from CDC and flu + pneumonia death data from NCHS through the end of October 2017.

Recommended reading prior to class:

(check back later)

RNA-seq

Prerequisites! This is not an introductory R class. This lesson assumes a basic familiarity with R, data frames, manipulating data with dplyr and %>%, and data visualization with ggplot2.

Software setup: Follow instructions above for R+RStudio+Packages, CRAN packages, and Bioconductor. See the sections above for full instructions and troubleshooting tips.

For this class you’ll also need the DESeq2 package.

If you receive an error message when trying to install any particular package, please make note of which one you had trouble with, and email one of the instructors prior to class with the command you typed and the error you received.

# Install core CRAN packages:
install.packages("dplyr")
install.packages("readr")
install.packages("tidyr")
install.packages("ggplot2")

# Install Bioconductor core packages:
source("http://bioconductor.org/biocLite.R")
biocLite()

# For this class, you'll also need DESeq2:
biocLite("DESeq2")

Check that you’ve installed everything correctly by closing and reopening RStudio and entering the following commands one-at-a-time in the console pane:

# Test CRAN package installation:
library(dplyr)
library(readr)
library(tidyr)
library(ggplot2)

# Test DESeq2 installation:
library(DESeq2)

This may produce some notes or other output, but as long as you don’t get an error message, you’re good to go. If you get a message that says something like: Error in library(somePackageName) : there is no package called 'somePackageName', then the required packages did not install correctly. Please do not hesitate to email one of the instructors prior to class if you are still having difficulty. In this email, please copy and paste what you typed in the console, and all of the output that streams by in the console.

Download data we’ll use in class. Create a new folder somewhere on your computer that’s easy to get to (e.g., your Desktop). Name it bioconnector. Inside that folder, make a folder called data, all lowercase. Download the 3 data files below, saving them to the new bioconnector/data folder you just made.

• Length-scaled count matrix (i.e., countData): airway_scaledcounts.csv
• Sample metadata (i.e., colData): airway_metadata.csv
• Gene Annotation data: annotables_grch38.csv

Recommended reading prior to class:

1. Conesa et al. A survey of best practices for RNA-seq data analysis. Genome Biology 17:13 (2016).
2. Soneson et al. “Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences.” F1000Research 4 (2015).
3. Abstract and introduction sections of Himes et al. “RNA-Seq transcriptome profiling identifies CRISPLD2 as a glucocorticoid responsive gene that modulates cytokine function in airway smooth muscle cells.” PLoS ONE 9.6 (2014): e99625.

Phylogenetic trees

Prerequisites! This is not an introductory R class. This lesson assumes a basic familiarity with R, data frames, manipulating data with dplyr and %>%, and most importantly, data visualization with ggplot2.

Software setup: Follow instructions above for R+RStudio+Packages, CRAN packages, and Bioconductor. See the sections above for full instructions and troubleshooting tips.

For this class you’ll also need the ggtree and Biostrings packages from Bioconductor.

If you receive an error message when trying to install any particular package, please make note of which one you had trouble with, and email one of the instructors prior to class with the command you typed and the error you received.

# Install core CRAN packages:
install.packages("dplyr")
install.packages("readr")
install.packages("tidyr")
install.packages("ggplot2")

# Install Bioconductor core packages:
source("http://bioconductor.org/biocLite.R")
biocLite()

# For this class, you'll also need ggtree and Biostrings:
biocLite("ggtree")
biocLite("Biostrings")

Check that you’ve installed everything correctly by closing and reopening RStudio and entering the following commands one-at-a-time in the console pane:

# Test CRAN package installation:
library(dplyr)
library(readr)
library(tidyr)
library(ggplot2)

# Test ggtree and Biostrings installation:
library(ggtree)
library(Biostrings)

This may produce some notes or other output, but as long as you don’t get an error message, you’re good to go. If you get a message that says something like: Error in library(somePackageName) : there is no package called 'somePackageName', then the required packages did not install correctly. Please do not hesitate to email one of the instructors prior to class if you are still having difficulty. In this email, please copy and paste what you typed in the console, and all of the output that streams by in the console.

Download data we’ll use in class. Create a new folder somewhere on your computer that’s easy to get to (e.g., your Desktop). Name it bioconnector. Inside that folder, make a folder called data, all lowercase. Download the data files below, saving them to the new bioconnector/data folder you just made.

• A simple phylogenetic tree in Newick format: tree_newick.nwk
• A rooted, time-measured phylogeny with influenza virus data: flu_tree_beast.tree
• Amino acid sequences of flu samples in the data above: flu_aasequence.fasta

Recommended reading: This lesson does not cover methods and software for generating phylogenetic trees, nor does it it cover interpreting phylogenies. Here’s a quick primer on how to read a phylogeny that you should review prior to this lesson, but it is by no means extensive. Genome-wide sequencing allows for examination of the entire genome, and from this, many methods and software tools exist for comparative genomics using SNP- and gene-based phylogenetic analysis, either from unassembled sequencing reads, draft assemblies/contigs, or complete genome sequences. These methods are beyond the scope of this lesson.

Interactive Visualization

The Interactive Visualization with JavaScript and R lesson requires installation of several R packages in addition to those mentioned above:

install.packages("highcharter")
install.packages("d3heatmap")
install.packages("leaflet")
install.packages("visNetwork")
install.packages("jsonlite")
install.packages("threejs")
install.packages("igraph")

To check that these are correctly installed, first close RStudio and then reopen it and run the following:

library(highcharter)
library(d3heatmap)
library(leaflet)
library(visNetwork)
library(jsonlite)
library(threejs)
library(igraph)

These commands may produce some notes or other output, but as long as they work without an error message, you’re good to go. If you get a message that says something like: Error in library(packageName) : there is no package called 'packageName', then the required packages did not install correctly. Please do not hesitate to email one of the instructors prior to the course if you are still having difficulty.

Shiny

The Building Shiny Web Apps in R lesson requires installation of several R packages in addition to those mentioned above:

install.packages("shiny")
install.packages("shinythemes")
install.packages("lubridate")

To check that these are correctly installed, first close RStudio and then reopen it and run the following:

library(shiny)
library(shinythemes)
library(lubridate)

These commands may produce some notes or other output, but as long as they work without an error message, you’re good to go. If you get a message that says something like: Error in library(packageName) : there is no package called 'packageName', then the required packages did not install correctly. Please do not hesitate to email one of the instructors prior to the course if you are still having difficulty.