The Single Cell Plus Workshop

This workshop is part of a joint research initiative between Hong Kong University and the University of Sydney.

Aim of this workshop

In this workshop we will focus on two mouse liver datasets to illustrate three critical topics in single-cell RNA-Seq analysis.

  1. Quality control of sc-RNASeq data,
  2. The scMerge package for integrating multiple sc-RNASeq data,
  3. Some possible downstream analysis after scMerge.

At the end of this workshop you should have some basic understanding of scRNASeq data and some key computational and statistical challenges of this type of data.

Prerequisites

Ideally, you should be somewhat familiar with R. However, if you haven’t used R before, don’t worry! This is not a programming workshop, you can still pick up important practical skills by running the codes we provided.

Please try to download and install:

Installation

You can find all the data and materials here.

You should be able to run the codes below in R to install all the packages needed in this workshop. If you encounter any problems. Please let one of the instructors know.

install.packages("BiocManager")
BiocManager::install(c("scMerge", "devtools", 
                       "DropletUtils", "edgeR",
                       "ggpubr", "MAST", 
                       "plyr", "Rtsne", 
                       "scales", "scater", 
                       "scran", "tidyverse", 
                       "viridis"), version = "3.10")


devtools::install_github("SydneyBioX/scdney")

For Windows users

If you could not run the last line of the above instructions, then please

  1. Try to install Rtools35 from https://cran.r-project.org/bin/windows/Rtools/.
  2. Run devtools::install_github("SydneyBioX/scdney") again.

For Mac users

If you could not run the last line of the above instructions, then please

  1. Try to install gfortran-6.1 from https://cran.r-project.org/bin/macosx/tools/.
  2. Try the following code instead.
BiocManager::install(c("DescTools", "amap", 
                       "doParallel", "ggridges", 
                       "lme4", "mice", 
                       "methods", "caret", 
                       "randomForest", "clusteval", 
                       "dendextend", "gmodels", 
                       "e1071"), version = "3.10")

install.packages("https://github.com/SydneyBioX/scdney/releases/download/V0.1.4/scdney_0.1.4.tgz", repos = NULL)

Clipboard

In some circumstances, we might need to share codes that are not currently a part of the existing materials. Please click here to access these codes.

References

Methodologies

  1. scMerge leverages factor analysis, stable expression, and pseudoreplication to merge multiple single-cell RNA-seq datasets, Proceedings of the National Academy of Sciences, United States of America, 2019. Yingxin Lin, Shila Ghazanfar, Kevin Y.X. Wang, Johann A. Gagnon-Bartsch, Kitty K. Lo, Xianbin Su, Ze-Guang Han, John T. Ormerod, Terence P. Speed, Pengyi Yang, Jean Y. H. Yang.

  2. Evaluating stably expressed genes in single cells (Under review). Yingxin Lin, Shila Ghazanfar, Dario Strbenac, Andy Wang, Ellis Patrick, Dave Lin, Terence Speed, Jean Y. H. Yang, Pengyi Yang.

  3. Impact of similarity metrics on single-cell RNA-seq data clustering, Briefings in Bioinformatics, 2018. Taiyun Kim, Irene Rui Chen, Yingxin Lin, Andy Y. Y. Wang, Jean Y. H. Yang, Pengyi Yang.

Data

  1. Single-cell RNA-Seq analysis reveals dynamic trajectories during mouse liver development, BMC Genomics, 2017. Xianbin Su, Yi Shi, Xin Zou, Zhao-Ning Lu, Gangcai Xie, Jean Y. H. Yang, Chong-Chao Wu, Xiao-Fang Cui, Kun-Yan He, Qing Luo, Yu-Lan Qu, Na Wang, Lan Wang, Ze-Guang Han.

  2. A single‐cell transcriptomic analysis reveals precise pathways and regulatory mechanisms underlying hepatoblast differentiation, Hepatology, 2017. Li Yang, Wei‐Hua Wang, Wei‐Lin Qi, Zhen Guo, Erfei Bi, Cheng‐Ran Xu.