This workshop is part of a joint research initiative between Hong Kong University and the University of Sydney.
In this workshop we will focus on two mouse liver datasets to illustrate three critical topics in single-cell RNA-Seq analysis.
scMerge
package for integrating multiple sc-RNASeq data,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.
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:
R 3.6
from https://cloud.r-project.org/RStudio
from https://www.rstudio.com/products/rstudio/download/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")
If you could not run the last line of the above instructions, then please
Rtools35
from https://cran.r-project.org/bin/windows/Rtools/.devtools::install_github("SydneyBioX/scdney")
again.If you could not run the last line of the above instructions, then please
gfortran-6.1
from https://cran.r-project.org/bin/macosx/tools/.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)
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.
Methodologies
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.
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.
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
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.
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.