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scSTEM 0.1.0

License: MIT DOI

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Getting Started

1. Introduction

Single cell STEM (scSTEM) is a shiny app based R package for visualizing and clustering genes in pseudotime ordered single cell RNA-seq data. scSTEM is a GUI based tool and thus does not require any coding experience.

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2. Installation

2.1 OS specific reqiurements

scSTEM may have system dependencies that differ for different OS. Different requirments for different OS are described here.

Windows

Linux

Run the following commands to install Docker and other system depedencies (tested with Ubuntu 18.04):

# Install docker
sudo apt-get update && apt-get install -y --no-install-recommends \
    ca-certificates curl gnupg lsb-release
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | gpg --dearmor -o /usr/share/keyrings/docker-archive-keyring.gpg
echo \
  "deb [arch=$(dpkg --print-architecture) signed-by=/usr/share/keyrings/docker-archive-keyring.gpg] https://download.docker.com/linux/ubuntu \
  $(lsb_release -cs) stable" | tee /etc/apt/sources.list.d/docker.list > /dev/null
sudo apt-get update && sudo apt-get install -y --no-install-recommends docker-ce docker-ce-cli containerd.io

# Install system dependencies
sudo apt-get install -y --no-install-recommends \
      	imagemagick libgdal-dev libhdf5-dev libudunits2-dev \
        libgeos-dev libproj-dev libgmp3-dev

You may install scSTEM without installing docker. However, many trajectory inference methods might not be running correctly except for Monocle3.

MacOS

Next steps

Once system dependencies have been installed, you may choose to install scSTEM by method 2.2 or 2.3 or 2.4. If you encounter any issues during installation, you may refer to 2.4 Trouble shootings or create a new issue under this repository.

2.2 Use renv to install scSTEM.

For easy installation and reproducibility, you may use renv to install all dependencies and scSTEM. Make sure that you have 1) R version >= 4.1.0, download R here, 2) Java, download Java here installed and 3) internet access. We would recommend installing Rstudio to interact with R in an easy-to-use GUI. If R asks "Do you want to install from sources the package which needs compilation?", it is recommended to select No (or N). Below are installation steps using renv: 1.In R, execute the following code to install renv:

install.packages("renv")
  1. Create a new directory to store dependency files for scSTEM (e.g. /home/alex/scstem). renv will later install dependencies into this personal folder. Download the renv.lock file in this repository or right click and 'save as' from this link. renv.lock is the lock file which contains the dependency information. Note that when you copy and paste the content of renv.lock into a text editor and save the file, your text editor (such as the default TextEdit in macOS) may automatically append file extension after renv.lock. This will cause renv to fail because renv.lock file cannot be found. Double-check the file name and its extension. Remove any extensions after renv.lock (e.g. .rtf or RTF).
  2. Once lock file is downloaded, copy the file into the folder you just created. Make sure the file name is 'renv.lock'. Then in R, execute the following code to activate the environment in this personal folder.
install_folder = "/home/alex/scstem/"
renv::activate(install_folder)
# "/home/alex/scstem/" is the path to the directory we just created (The direcotry where the renv.lock file should be saved.)
# You will need to replace them with your own directory and path.
  1. Once the environment is activated. Install all necessary files into this personal folder simply by using the following code.
renv::restore(prompt = F)
  1. How to run and exit scSTEM. To activate the project environment and run scSTEM, simply executing the following code. R will then load all dependencies from the folder we just created.
renv::activate(install_folder)
library("scSTEM")
run_scstem_GUI()

Where install_folder is the project folder we just created to install scSTEM. After analysis is done, you may deactivate the project environment for scSTEM, simiply by executing the following code:

renv::deactivate()
  1. How to uninstall scSTEM Thanks to renv, uninstalling scSTEM is quite easy. The only thing you need to do is just removing the folder (in this tutorial, "/home/alex/scstem/"). This would not impact other packages you installed for R.

2.3 (Skip this if 2.1 is successful) Manually install all dependencies.

2.3.1 Install R dependencies.

Make sure that you have 1) R version >= 4.1.0, download R here, 2) Java, download Java here installed and 3) internet access. We would recommend installing Rstudio to interact with R in an easy-to-use GUI. Execute the following code to install all R dependencies. If R asks "Do you want to install from sources the package which needs compilation?", it is recommended to select No (or N). If windows users are seeing "Rtools is required to build R packages but is not currently installed" during installation, you may instal Rtools from https://cran.r-project.org/bin/windows/Rtools/

# Install devtools
install.packages('devtools')

# Install Bioconductor (for R >= 4.1.0)
if (!requireNamespace("BiocManager", quietly = TRUE))
  install.packages("BiocManager")
BiocManager::install(version = "3.13", ask = FALSE)

# Install Bioconductor dependencies
BiocManager::install(c('BiocGenerics', 'DelayedArray', 'DelayedMatrixStats',
                       'limma', 'S4Vectors', 'SingleCellExperiment',
                       'SummarizedExperiment', 'batchelor', 'Matrix.utils'), ask = FALSE)

# Now, install monocle3 through the cole-trapnell-lab GitHub, execute:
install.packages("devtools")
devtools::install_github('cole-trapnell-lab/leidenbase', upgrade = "always")
devtools::install_github('cole-trapnell-lab/monocle3', upgrade = "always")

# Install biomaRt
BiocManager::install("biomaRt", ask = FALSE)

# Install ROGUE
devtools::install_github("PaulingLiu/ROGUE", upgrade = "always")

# Install Dynverse
devtools::install_github("dynverse/dyno", upgrade = "always")

# Install scSTEM
devtools::install_github("alexQiSong/scSTEM", upgrade = "always")

2.4 (Skip this if installation is successful) Trouble shootings

  1. make: gfortran: No such file or directory. MAC users may see the following error message:
make: gfortran: No such file or directory
make: *** [cigraph/src/AMD/Source/amd.o] Error 1
ERROR: compilation failed for package 'leidenbase'

The solutions are described below, quoted from https://cole-trapnell-lab.github.io/monocle3/docs/installation/

  1. API rate limit exceeded. This is because the dependent packages for scSTEM use GitHub API, which has been limited 60 requests by default. To increase the limit, you can execute the following code in R:usethis::create_github_token(). This will take you to your GitHub account page (you may register a new account if you haven't done so), where you can generate a new token. Once this new token has been created. You may run the following code in R: usethis::edit_r_environ(). This will take you to the R environment variable editor, where you can specify your token by GITHUB_PAT = 'your_token'. Replace your_token by the token you just created then close restart R and resume the installation steps.
  2. If you encounter error message related to Xcode, open terminal and type xcode-select --install and then follow the prompts. This is required by Monocle 3.
  3. If you don't see the package installation information after running renv::restore(prompt = F) but instead only see a message * The library is already synchronized with the lockfile. This is probably because your text editor (such as the default TextEdit in macOS) may automatically append file extension after renv.lock when you save the content by the text editor. This will cause renv to fail because renv.lock file cannot be found. Double-check the file name and its extension. Remove any extensions after renv.lock (e.g. .rtf or RTF).

3. Sample data sets

We have provided a sample data set for running the analysis. You may click Load sample data in the GUI to automatically load the sample data set. Alternatively, you may also download the sample data set (https://github.com/alexQiSong/scSTEM_sample_data) and load it manually.

4. Input and output files

scSTEM uses three files as input (See also the sample data set in 3. Sample data sets):

scSTEM output files:

5. Analysis steps

scSTEM analysis involves running dimensionality reduction, cell clustering, trajectory inference, and gene clustering. While the functionality of dimensionality reduction and cell clustering is provided here to facilitate users to choose cells of interest, they are optional for the following steps. The steps of running analysis with GUI is described below. We will use the sample data set at https://github.com/alexQiSong/scSTEM_sample_data as an example showing the analysis functionality. This data set contains human fetal immune blood cells and was published with Cao et al., 2020. Note that internet connection is required as scSTEM may convert gene IDs using biomaRt which converts the gene IDs using ensembl database.

To launch the scSTEM GUI, execute the following code in R:

library(scSTEM)
run_scstem_GUI()
  1. Step 1: Load input files. Click each of the three buttons on the top of the GUI to select corresponding input files. Click Load files to load all selected files. If files were successfully loaded, there will be a line of text showing All files successfully loaded in the top panel. Once the sample data set is downloaded, select the path for each input file and select the gene ID type and species. For ID type, gene symbol and ensmebl ID are supported and for species, human and mouse are supported.

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Alternatively, you may load sample data set by clicking the "Load sample data" button. It automatically loads the sample data set for this tutorial. This is the data set from a publised researchCao et el., 2020.

  1. Step 2: Visualizing and clustering cells. In the second panel, scSTEM can perform dimensionality reduction and cell clustering. This step can be optional for some trajectory inference methods but is required if monocle3 is selected as trajectory inference method. This step also provides 2D UMAP visualization that assists users to select cells of interest. If this step is skipped, scSTEM will take all cells from the input expression count matrix. To visualize dimensionality reduction results, first click Run UMAP. Then there is an argument PCA dim (default value 100) that may be provided by users before running UMAP. PCA dim specifies the number of dimensions for principle component analysis, which is a preprocessing step to denoise and decorrelate input dimensions before running UMAP.

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After UMAP is done, click Run Clustering and then Visualize Results. For the sample data set, we can see the cell partition number is displayed along with the cells. Let's focus on partition 4 highlited by green color here. For the sample data set, partitions may bi numbered differently on different OS. You may find the partition shown in the figure below numbered by a different number.

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Further, scSTEM also allows for coloring the cell clusters using different column information from the cell_meta_data file other than the cell partition numbers from clustering. This would allow you to view the cells by cell type or experimental batches or other meta data information. But make sure the number of unique labels to visualize is not too many otherwise the application may crash. In this tutorial, we visualize the cell cluster by selecting Blood_cell_name from the column_to_visualize dropdown list.

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Now you will see that instead of showing cell partition number (default option), the UMAP plot is colored and labeled by blood cell type name.

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  1. Step 3: Infer trajectories. In the third panel, scSTEM can perform trajectory inference. To infer trajectories for the input data, simply select cell partitions of interest from the partition drop-down list (generated from the second step) and then select trajectory inference method from the Method drop-down list. Once partition and Method were selected, users can click infer trajectory to infer trajectories. For this sample data set, let's select partition 4 and monocle3 from the Method list to infer trajectoryies. The use partition option is only valid for monocle3 as inference method. Once selected, monocle3 will infer disjoint trajectory graph separately for each cell partition.

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Note that for monocle3, you may prune the trajectory to avoid having too may small branches. You could do this by tunning the parameter in the Infer trajectory dropdown list, where you can enter a value of minimal branch length (Larger value will result in less small branches, default value = 10, only effective when Monocle3 is the inference method). After this is done, you may click infer button to infer the trajectory.

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  1. Step 4: Check paths. In the fourth panel, users may 1) visualize the paths inferred by the previous step and 2) check linear fit for each path. For visualization, the selected path is highlighted by red color and cells mapped to the path is marked by yellow color. Let's select path1 to visualize. Selected path will be shown on the right part of the GUI.

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In addition, we may also be interested in testing whether the change of expression is linear for each gene along the trajectory path. scSTEM can do a quick check by fitting linear models using expression of each gene to predict cell pseudtime on each segment of a selected path in Step 4 panel. Users may do this by clicking on the check linear button in the Step 4 panel. After fitting is completed, a plot will appear in a pop-up window showing the percentage of genes which has significant "good fit" from F test. As shown below, Terminal segment represents the segment located at the end of a trajecotry path and otherwise not.

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  1. Step 5: Run STEM analysis. In the fifth panel, users can perform scSTEM analysis. Currently there are two parameters metric and path to be specified for scSTEM. metric specifies the method to summarize expression data from trajectories. Users can select mean, entropy_reduction, or change_rate from the metric drop-down list. In the select a path drop-down list, users can specify a path of interest to run scSTEM clustering (which can be visualized in step 4). Users will need to provide an output folder where output files and temporary files can be saved. Click the output folder button to specify an output folder. In this sample data set, let's select path2 and path3 in the select a path drop-down list, and mean in the metric drop-down list to run the STEM analysis. Alt text
    scSTEM will iterate through the selected paths and generate clusters for each of the path. This is done by calling the STEM java program. The STEM java GUI will show the profile plots after clustering is finished. In the screenshot shown below, profiles with the same color together represent a gene cluster. Alt text
    Users may further click the profile to have a detailed view of gene expression pattern in the pop-up window, in which users may also view and save the cluster gene and GO annotations by clicking Cluster Gene Table and Cluster GO Table, respectively.

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  1. Step 6: Run comparative analysis. In the sixth panel, users can perform comparison of gene clustering results from different trajectory paths. To do this, first select the two paths to be compared in the two drop-down lists Path name 1 and Path name 2, then click the Run comparison button. scSTEM will call STEM java program to run gene clustering and comparison of clustering results for the selected paths. For this sample data set, let's select path2 and path3 from the drop-down lists to compare. After clustering is done, STEM java program will show a cluster comparison plot with each row representing the matched clusters from the two paths.

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To zoom-in the plot, you may hold and drag the right mouse. To move the plot in the window, you may hold and drag the left mouse.

6. Upgrade to new version of scSTEM

Upgrade existing version of scSTEM is quite easy with a single line of code in R:

devtools::install_github("alexQiSong/scSTEM", upgrade = "always")

Note that you will need to activate the corresponding environment before upgrading scSTEM if you installed it by renv (See 2.1, 5):

renv::activate(install_folder)

Analysis code

Code for the analyses performed in scSTEM publication can be found under /analysis. The results presented in the publication were generated with R 4.1.0 on Windows10. Other OS may yield slightly different results. For other figures and results not produced by the scripts here, they can be generated by the scSTEM GUI.

STEM Java program

We have made changes to original STEM Java prgram as in https://github.com/jernst98/STEM_DREM. The modified version of STEM, along with its source code, can be downloaded from: https://github.com/alexQiSong/scSTEM-STEM-java

Contact

Contact us if you have any questions:
Qi (Alex) Song: qisong@andrew.cmu.edu; sqsq3178@gmail.com
Ziv Bar-Joseph: zivbj@andrew.cmu.edu

Cite

Please cite the following paper if you used scSTEM in your research.
Qi Song, Jingtao Wang, Ziv Bar-Joseph. scSTEM: Clustering pseudo-time ordered single cell data. Genome Biology. 2022;23(1):150.

Copyright

©2021 Qi Song, Ziv Bar-Joseph. Systems Biology Group at Carnegie Mellon University. All rights reserved.