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      Fall single cell RNA sequencing workshop @ UCSF

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Introduction and Lectures
Intro to the Workshop and Core
What is Bioinformatics/Genomics Perspective?
Experimental Design and Cost Estimation
Introduction to Command-Line and the Cluster
Logging in and Transferring Files
Intro to Command-Line
Advanced Command-Line (extra)
Running jobs on the Cluster and using modules
Intro to R and Rstudio
Getting Started
Intro to R
Prepare Data in R (extra)
Data in R (extra)
Data Reduction
Project setup
Generating Expression Matrix
scRNAseq Analysis
Prepare scRNAseq Analysis
scRNAseq Analysis - PART1
scRNAseq Analysis - PART2
scRNAseq Analysis - PART3
scRNAseq Analysis - PART4
scRNAseq Analysis - PART5
scRNAseq Analysis - PART6
Guest lecture by Dr. Gerald Quon
Prepare Single Cell Alignment
Single Cell Alignment (scAlign)
Support
Cheat Sheets
Software and Links
Scripts
ETC
Closing thoughts
Workshop Photos
Github
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