• Stars
    star
    457
  • Rank 95,775 (Top 2 %)
  • Language
    R
  • Created over 4 years ago
  • Updated 6 months ago

Reviews

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

Repository Details

Single-cell RNA-seq data analysis workshop

Audience Computational skills required Duration
Biologists Introduction to R 3-session online workshop (~7.5 hours of trainer-led time)

Description

This repository has teaching materials for a hands-on Introduction to single-cell RNA-seq analysis workshop. This workshop will instruct participants on how to design a single-cell RNA-seq experiment, and how to efficiently manage and analyze the data starting from count matrices. This will be a hands-on workshop in which we will focus on using the Seurat package using R/RStudio. Working knowledge of R is required or completion of the Introduction to R workshop.

Note for Trainers: Please note that the schedule linked below assumes that learners will spend between 3-4 hours on reading through, and completing exercises from selected lessons between classes. The online component of the workshop focuses on more exercises and discussion/Q & A.

These materials were developed for a trainer-led workshop, but are also amenable to self-guided learning.

Learning Objectives

  • Explain common considerations when designing a single-cell RNA-seq experiment
  • Discuss the steps involved in taking raw single-cell RNA-sequencing data and generating a count (gene expression) matrix
  • Compute and assess QC metrics at every step in the workflow
  • Cluster cells based on expression data and derive the identity of the different cell types present
  • Perform integration of different sample conditions

Lessons

Installation Requirements

Applications

Download the most recent versions of R and RStudio for your laptop:

Packages for R

Note 1: Install the packages in the order listed below.

Note 2:  All the package names listed below are case sensitive!

Note 3: At any point (especially if you’ve used R/Bioconductor in the past), in the console R may ask you if you want to update any old packages by asking Update all/some/none? [a/s/n]:. If you see this, type "a" at the prompt and hit Enter to update any old packages. Updating packages can sometimes take quite a bit of time to run, so please account for that before you start with these installations.

Note 4: If you see a message in your console along 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.

(1) Install the 4 packages listed below from Bioconductor using the the BiocManager::install() function.

  1. AnnotationHub
  2. ensembldb
  3. multtest
  4. glmGamPoi

Please install them one-by-one as follows:

BiocManager::install("AnnotationHub")
BiocManager::install("ensembldb")
& so on ...

(2) Install the 8 packages listed below from CRAN using the install.packages() function.

  1. tidyverse
  2. Matrix
  3. RCurl
  4. scales
  5. cowplot
  6. BiocManager
  7. Seurat
  8. metap

Please install them one-by-one as follows:

install.packages("tidyverse")
install.packages("Matrix")
install.packages("RCurl")
& so on ...

(3) Finally, please check that all the packages were installed successfully by loading them one at a time using the library() function.

library(Seurat)
library(tidyverse)
library(Matrix)
library(RCurl)
library(scales)
library(cowplot)
library(AnnotationHub)
library(ensembldb)

(4) Once all packages have been loaded, run sessionInfo().

sessionInfo()

Citation

To cite material from this course in your publications, please use:

Mary Piper, Meeta Mistry, Jihe Liu, William Gammerdinger, & Radhika Khetani. (2022, January 6). hbctraining/scRNA-seq_online: scRNA-seq Lessons from HCBC (first release). Zenodo. https://doi.org/10.5281/zenodo.5826256

A lot of time and effort went into the preparation of these materials. Citations help us understand the needs of the community, gain recognition for our work, and attract further funding to support our teaching activities. Thank you for citing this material if it helped you in your data analysis.


These materials have been developed by members of the teaching team at the Harvard Chan Bioinformatics Core (HBC). These are open access materials distributed under the terms of the Creative Commons Attribution license (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

More Repositories

1

scRNA-seq

SCSS
511
star
2

In-depth-NGS-Data-Analysis-Course

HTML
430
star
3

DGE_workshop

HTML
358
star
4

Training-modules

Materials for short, half-day workshops
Jupyter Notebook
285
star
5

Intro-to-ChIPseq

Intro to ChIPseq using HPC
SCSS
271
star
6

Intro-to-rnaseq-hpc-O2

This repository has teaching materials for a 2 and 3-day Introduction to RNA-sequencing data analysis workshop using the O2 Cluster
HTML
162
star
7

DGE_workshop_salmon_online

HTML
159
star
8

Intro-to-R

SCSS
90
star
9

Intro-to-rnaseq-hpc-salmon-flipped

Introduction to bulk RNA-seq
HTML
84
star
10

Intro-to-R-flipped

R
64
star
11

DGE_workshop_salmon

HTML
64
star
12

Intro-to-ChIPseq-flipped

HTML
64
star
13

Intro-to-R-with-DGE

R
57
star
14

main

SCSS
51
star
15

Intro-to-rnaseq-hpc-salmon

HTML
46
star
16

Intro-to-shell-flipped

HTML
34
star
17

Accessing_public_genomic_data

Tutorials on accessing public reference and genomic data
HTML
28
star
18

variant_analysis

HTML
22
star
19

publication_perfect

Six hour hands-on to creating publication-quality plots in R
SCSS
21
star
20

bioinformatics_online

Resource list and preparation instructions for online learning and teaching for Bioinformatics
SCSS
19
star
21

Intro-to-Shell

Introduction to the Unix shell
HTML
19
star
22

rnaseq_overview

Short course describing the considerations for a successful RNA-seq experiment
HTML
16
star
23

rnaseq-cb321

June 5, 2019
HTML
9
star
24

Intro-to-Unix-archived

Materials for a 2-day introduction to the bash language, the Linux OS, and high-performance computing.
Shell
9
star
25

Intro-to-rnaseq-hpc-gt

Introduction to RNA-seq using HPC
Shell
9
star
26

Intro-to-rnaseq-hpc-orchestra

This repository has teaching materials for a 2-day Introduction to RNA-sequencing data analysis workshop using the Orchestra Cluster.
Shell
9
star
27

versioning_data_scripts

GitKraken lesson (forked from HBS-RCS)
R
8
star
28

Peak_analysis_workshop

An introduction to various methods/approaches for the analysis of peaks generated from ChIP-seq / CUT&RUN / ATAC-seq
SCSS
8
star
29

reproducibility-tools

R
7
star
30

Intro-to-rnaseq-fasrc-salmon-flipped

HTML
7
star
31

GCC-BOSC-2018

Lessons for "Setting up for Success when planning an RNA-seq experiment" session at GCC-BOSC 2018
HTML
5
star
32

Intro-to-shell-fasrc-flipped

Shell training materials for the FAS-RC cluster
Shell
4
star
33

RNA-seq-CB321qc_2022

Teaching RNA-seq experimental design and analysis + data storage and sharing for genomics data
SCSS
3
star
34

Rmarkdown_analysis_reports

SCSS
3
star
35

Intro-to-R-online-Catalyst

Contains materials for an online version of the Introduction to R
CSS
3
star
36

EpiR

Introduction to R for Epi summer program
SCSS
2
star
37

datafest2021_Rmarkdown

2
star
38

version-control-gitkraken

R
1
star
39

Galaxy_RNA-Seq

1
star