public_sequin: An HTML repository from National Center for Advancing Translational Sciences - National Center for Advancing Translational Sciences

SEQUIN is an R/Shiny application for analysis and visualization of bulk and single-cell RNA-seq data.

For complete details, please see our manuscript or tutorial.

For bug reports, please submit an issue or email either Andrew Weisman or Andrei Bombin. We are also very open to ideas for improvements.

Access SEQUIN in the web

Access the public app here.

Install SEQUIN locally

To install SEQUIN locally, you will need the following tools installed:

git
R (>= 4.3.2)
rtools4 (only needed in Windows and its version must be the same as the installed version of R)
RStudio (optional but highly recommended)

Further, you must have cloned the public SEQUIN repository:

git clone https://github.com/ncats/public_sequin.git # clones the repository
# to switch on development branch use
git fetch origin sequin_dev_ab # fetch development branch
git checkout sequin_dev_ab # switch to development branch
git pull origin sequin_dev_ab # update development branch

Installation can then be performed either in R or RStudio.

Instructions for R

Run the installation script to activate the SEQUIN project and install all required R packages (packages will be installed in the user's default directory for R libraries):
```
setwd("/path/to/public_sequin") # Use your local repo directory
source("install_sequin.R")
```
After installation, restart R.

Launch SEQUIN using:

setwd("/path/to/public_sequin") # Use your local repo directory
shiny::runApp(launch.browser = T) # Opens SEQUIN in browser

When you're finished using SEQUIN, deactivate the project to return to the default R environment.
```
renv::deactivate()
```

To launch SEQUIN at a later time, restore the project and run the app.

setwd("/path/to/public_sequin")
renv::restore()
shiny::runApp(launch.browser = T)

Instructions for RStudio

Run the installation scripts to activate the SEQUIN project and install all required R packages (packages will be installed in the public_sequin directory and will not interfere with user's default R environment):

setwd("/path/to/public_sequin") # Use your local repo directory
source("install_1.R") # install renv and BioConductor
rstudioapi::openProject('sctl-rshiny-complex.Rproj') # activates the project; be sure to save your current workspace when prompted if you have something to save
y # after project activation renv will ask you to install BiocManager locally
source("install_2.R") # install the rest of dependencies, input Y in the terminal each time Rstudio asks you if you want to install packages
# no need to restart Rstudio

When you're finished using SEQUIN, close the project to return to the default R environment.
```
rstudioapi::executeCommand('closeProject')
```

To launch SEQUIN at a later time, restore the project and run the app.

setwd("/path/to/public_sequin")
rstudioapi::openProject('sctl-rshiny-complex.Rproj')
shiny::runApp(launch.browser = T)

Local data

Set the local directory where SEQUIN will read and write data by opening app.R and editing the first line.

# Set local data directory for running as standalone app
options(localDir = "example_data")

Use the example_data directory as an example of how to format data. We recommend using a local directory outside of public_sequin for your real data to avoid git conflicts when you pull updated versions of SEQUIN.

Each dataset must have the following:

Experiment name. Choose a unique experiment name with no spaces (e.g., example_sc). This name must be present in the experiment_name column in data_info.csv (see #4 below).
Counts file. The counts file must be in CSV format and named as follows: <experiment name>_counts.csv (e.g., example_sc_counts.csv).

The counts file must have gene symbols in the first column and sample or cell names in subsequent columns. Counts data in each sample or cell column must consist of only integers.
Metadata file. The metadata file must be in CSV format and named as follows: <experiment name>_meta.csv (e.g., example_sc_meta.csv).

The metadata file must have sample or cell names in the first column and experimental variables in subsequent columns. At least one experimental variable is required. All experimental variables must be factors (e.g., treatment group, cell line, etc.). Some features of SEQUIN will interpret numeric variables as factors which may be undesirable.
data_info.csv. All datasets should have an entry in a CSV file called data_info.csv in your local data directory (e.g., example_data/data_info.csv). data_info.csv must have the columns listed below. All columns must be present but only those in bold must have an entry for each dataset.

experiment_id: a unique integer ID value for each dataset.
experiment_name: a unique experiment name for each dataset (e.g., example_sc).
description: a brief description of the dataset.
upload_date: can be any value, but typically a date corresponding to the dataest.
unique_table: must be identical to experiment_name.
created_by: can be any value, but typically a username or nickname for the person who created the data.
type: bulk (for bulk RNA-seq) or sc (for single-cell RNA-seq).
publication: hyperlink text to be displayed in the Source column on the initial load page in the app (e.g., Walker et al., 2019).
publication_link: URL for the hyperlink in the Source column on the initial load page in the app (e.g., https://www.nature.com/articles/s41598-019-56955-1).

User-updated metadata

SEQUIN includes two tools, Merge clusters and Group cells by gene expression, that enable the user to create updated metadata files that can be loaded instead of the default metadata.

SEQUIN will record information about user-updated metadata files in sc_useradd.csv.

To delete user-updated metadata files, delete the appropriate rows from sc_useradd.csv and delete the user-updated metadata files. The user-updated metadata files can be identified by looking at the table_name column in sc_useradd.csv. If you have deleted all user-updated metadata files, you may also delete sc_useradd.csv.