Single Cell Plus

This workshop is funded by “Single Cell Plus”, a research initiative between Hong Kong University and the University of Sydney.

Slides

You can find the workshop slides here.

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 downstream analyses after scMerge.

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

Prerequisites

Ideally, you should be familiar with R and the Bioconductor. However, since this is not a programming workshop, you can still pick up important practical skills by running the codes we provided.

Running workshop on Google Cloud

Your instructor will provide you with the links to a Google Cloud server, username and the password. Once you log in, you will have access to RStudio via a webpage.

Type the following code into your console and you are good to go!

source("/home/user_setup.R")

Running workshop locally

  1. Please download and install:

  1. In your RStudio console pane, please install all the necessary packages by running this script. This will take a while.

  2. Download all the the workshop materials from this zip file and unzip it on your Desktop. You should have a folder with the name /desktop/BIS2019_SC-master created.

  3. Download the data from this zip file and unzip this file under /desktop/BIS2019_SC-master, so that you should have a folder created as /desktop/BIS2019_SC-master/data.

  4. Open up /desktop/SC_CPC_workshop-master/BIS2019_SC.Rproj and you should be able to run every .Rmd file in the workshop now.

Clipboard

In some circumstances, we might need to share codes beyond the existing materials. Please click here to access these codes.

References

Methodologies

  1. Cao Y., Lin Y., Ormerod, J.T., Yang, P., Yang, J., and Lo, K.K. (2019). scDC: Single cell differential composition analysis. Accepted by InCoB 2019.

  2. Geddes, T.A., Kim, T., Nan, L., Burchfield, J.G., Yang, J.Y.H., Tao, D., and Yang, P. (2019). Autoencoder-based cluster ensembles for single-cell RNA-seq data analysis. BioRxiv Accepted by InCoB 2019.

  3. Kim, T., Lo, K.K., Geddes, T.A., Kim H., Yang, J., and Yang, P. (2019) Accepted by InCoB 2019. scReClassify: post hoc cell type classification of single-cell RNA-seq data.

  4. Kim, T., Chen, I.R., Lin, Y., Wang, A.Y.-Y., Yang, J.Y.H., and Yang, P. (2019). Impact of similarity metrics on single-cell RNA-seq data clustering. Brief. Bioinform.

  5. Lin, Y., Ghazanfar, S., Wang, K.Y.X., Gagnon-Bartsch, J.A., Lo, K.K., Su, X., Han, Z.G., Ormerod, J.T., Speed, T.P., Yang, P., and Yang, J. (2019). scMerge leverages factor analysis, stable expression, and pseudoreplication to merge multiple single-cell RNA-seq datasets. Proc. Natl. Acad. Sci. U. S. A.

  6. Lin, Y., Ghazanfar, S., Strbenac, D., Wang, A., Patrick, E., Lin, D., Speed, T., Yang, J., and Yang, P. (2019). Evaluating stably expressed genes in single cells. GigaScience.

  7. Lin, Y., Cao Y., Kim H., Salim A., Speed T., Lin D., Yang P., Yang, J. (2019) scClassify: hierarchical classification of cells. BioRxiv

Data

  1. 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. (2017) Single-cell RNA-Seq analysis reveals dynamic trajectories during mouse liver development, BMC Genomics.

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