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      Advanced Single Cell RNA-Seq Workshop

Home
Introduction and Lectures
Intro to the Workshop and Core
Schedule
What is Bioinformatics/Genomics?
Experimental Design and Cost Estimation
Single Cell Sample Preparation - Dr. Diana Burkart-Waco
Support
Cheat Sheets
Software and Links
Scripts
Prerequisites
CLI - Logging in and Transferring Files
CLI - Intro to Command-Line
CLI - Advanced Command-Line (extra)
CLI - Running jobs on the Cluster and using modules
R - Getting Started
R - Intro to R
R - Prepare Data in R (extra)
R - Data in R (extra)
More Materials (extra)
Data Reduction
Generating Expression Matrices
Expression project setup
Preprocessing reads with HTStream
Generating Expression Tables
VDJ T cell and B cell
Velocity analysis
Data analysis
scRNA analysis prepare
Mapping Comparison
Anchoring (Comparison dataset)
Shiny App Install/Overview
App Practical Usage
AWS Hosted App (Optional)
Monocle
VDJ T cell and B cell analysis
Velocity analysis
ETC
Closing thoughts
Workshop Photos
Github page
Biocore website

Create a new RStudio project

Open RStudio and create a new project, for more info see https://support.rstudio.com/hc/en-us/articles/200526207-Using-Projects

Learn more about packrat see https://rstudio.github.io/packrat/

Set some options and make sure the packages ‘knitr’, ‘tidyverse’, ‘reshape2’, and ‘gr are installed (if not install it), and then load

In the R console run the following commands

if (!any(rownames(installed.packages()) == "knitr")){
  install.packages("knitr")
}
library(knitr)

if (!any(rownames(installed.packages()) == "tidyverse")){
  install.packages("tidyverse")
}
library(tidyverse)

if (!any(rownames(installed.packages()) == "reshape2")){
  install.packages("reshape2")
}
library(reshape2)

if (!any(rownames(installed.packages()) == "gridExtra")){
  install.packages("gridExtra")
}
library(gridExtra)

Learn more about the tidyverse see https://www.tidyverse.org.

Open a new R Notebook

An R notebook is an R Markdown document with chunks that can be executed independently and interactively, with output visible immediately beneath the input. More info see https://rmarkdown.rstudio.com/r_notebooks.html

R Markdown

This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see http://rmarkdown.rstudio.com.

When you click the preview or Knit button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. You can embed R code and plots in chunks like this:

```{r chunk_name}
print('hello world!')
```

Review the R Markdown page and R Markdown cheat sheets.

Try ‘knitting’ to html, pdf, and doc as well as previewing the notebook. Open the resulting documents.

Try executing the code chunks in the R Notebook.

Download the data file for the workshop document and preview/open it

This is the stats file generated after running samtools stats on a bam file generated from running BWA MEM.

In the R console run the following command.

download.file("https://raw.githubusercontent.com/ucdavis-bioinformatics-training/2019_August_UCD_mRNAseq_Workshop/master/intro2R/Data_in_R_files/bwa_mem_Stats.log", "bwa_mem_Stats.log")

Download the template Markdown workshop document and open it

In the R console run the following command

download.file("https://raw.githubusercontent.com/ucdavis-bioinformatics-training/2019_August_UCD_mRNAseq_Workshop/master/intro2R/data_in_R.Rmd", "data_in_R.Rmd")

Edit the file YAML portion

The top YAML (YAML ain’t markup language) portion of the doc tells RStudio how to parse the document.

---
title: "Data_in_R"
author: your_name
date: current_date
output:
    html_notebook: default
    html_document: default
---

What are we going to do?

We will recreate some of the plots generated with plot-bamstats on the same file

You can view the output of plot-bamstats -> bwa_mem_stats.html