Buck9195/Gladstone-Bioinformatics-Workshop
1
0
Fork 0
mirror of https://github.com/gladstone-institutes/Bioinformatics-Workshops.git synced 2025-11-30 09:45:43 -08:00
Code Issues Projects Releases Packages Wiki Activity Actions

demo for scATAC-seq

Browse source
This commit is contained in:
Ayushi Agrawal 2023-01-31 10:50:29 -08:00
parent 52e762911f
commit 06655d66c0
1 changed files with 55 additions and 0 deletions
Download patch file Download diff file Expand all files Collapse all files

55
intro-sc-atac-seq/ArchR_demo_1_preprocessing_data.Rmd Normal file
View file

@ -0,0 +1,55 @@
---
title: 'Hands-on component of Single-cell ATAC-seq workshop: Sessions 1-2'
author: "Ayushi Agrawal"
date: "1/31/2023"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
##Introduction
We'll review the current practices for some of the common steps in scATAC-seq data analysis in the Sessions 1-2. We'll discuss different practices for each step and the assumptions underlying various tools and their limitations. This document provides an exposure to one of the popular tools for such analysis. As we'll discuss, the right choice of method in any application depends on a number of factors, including the biological systems under study and the characteristics of the data in hand. For the purpose of our workshop, we'll limit the hands-on component to the ArchR package in R. In general, analysis might require multiple tools in different languages and/or novel development. For more, please see the slide deck in materials.
The following is based on [this vignette](https://www.archrproject.com//articles/Articles/tutorial.html) from the ArchR developers. Please note that ArchR is designed to be run on Unix-based operating systems such as macOS and linux. ArchR is NOT supported on Windows or other operating systems.
## Setup the working environment
```{r message=FALSE, warning=FALSE}
# TODO: remove this line: internal reference: https://github.com/gladstone-institutes/atacseq_cardio_multiomics
library(ArchR)
#set threads
addArchRThreads(threads = 16)
#load the genome
addArchRGenome("hg19")
```
## Load the data
```{r}
#one way to get the files
inputFiles <- getTutorialData(tutorial = "Multiome")
#another way to specify files
filesUrl <- data.frame(
fileUrl = c(
"https://jeffgranja.s3.amazonaws.com/ArchR/TestData/Multiome/pbmc_sorted_3k.fragments.tsv.gz",
"https://jeffgranja.s3.amazonaws.com/ArchR/TestData/Multiome/pbmc_sorted_3k.filtered_feature_bc_matrix.h5",
"https://jeffgranja.s3.amazonaws.com/ArchR/TestData/Multiome/pbmc_unsorted_3k.fragments.tsv.gz",
"https://jeffgranja.s3.amazonaws.com/ArchR/TestData/Multiome/pbmc_unsorted_3k.filtered_feature_bc_matrix.h5"
),
md5sum = c(
"d49f4012ff65d9edfee86281d6afb286",
"e326066b51ec8975197c29a7f911a4fd",
"5737fbfcb85d5ebf4dab234a1592e740",
"bd4cc4ff040987e1438f1737be606a27"
),
stringsAsFactors = FALSE
)
```