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bicbioeng / BASIN

R package for statistical microscrope image analysis
MIT License
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BASIN (Bioimage Analysis, Statistic, and Comparison)

An R package that utilizes Shiny to provide a user interface for statistical analysis of two-dimensional confocal microscope images. Users can upload two images directly or a folder of images with the help of a user-generated csv file, edit their experimental design, create tables and graphs for analysis results, and generate a fully-formatted report of their experiment. For more information on BASIN, check out our documentation site. For tutorials on the BASIN workflow and running the application, check out the playlists on our YouTube channel.

BASIN Overview and Quick Tutorial

A simplified version of BASIN is available through shinyApps at both https://basin.bicbioeng.org/ and http://bicbioeng.shinyapps.io/tryBASIN. This version only takes in 2 images, but the workflow is nearly identical to the complete version and serves as a gentle tutorial to most of BASIN's features. Note that for the full version of BASIN requires the user to download a csv containing the names of the images uploaded and assign 'control' and 'test' bioconditions manually, in addition to experiment number(s), which must be positive integers only.

Installation and Usage

Option 1: Shiny Application (RECOMMENDED)

BASIN Lite Installation:

R Setup:

  1. Make sure you have the latest version of R and Rstudio installed on your computer (free and open-source, available online). Rstudio is an IDE for the R programming language, and all successive steps should be ran through the Rstudio terminal.

  2. Install the required R and Bioconductor packages using the following commands: install.packages(c("purrr", "plyr", "shiny", "shinyBS", "shinyjs", "shinydashboard", "shinycssloaders", "shinythemes", "shinyWidgets", "DT", "stringi", "ggpubr", "tcltk", "autothresholdr"))

    if (!requireNamespace("BiocManager", quietly = TRUE))\ install.packages("BiocManager") #installs Bioconductor\ BiocManager::install("EBImage") #installs EBImage

Running the application:

  1. Download the BASIN-lite folder from this github repository.
  2. Find the ui.R or server.R file in the folder and open it using RStudio
  3. At the top right corner of the opened file, there should be a green button next to the text "Run App". Use that button to start your application.

BASIN ML Installation (EXPERIMENTAL):

BASIN-ML leverages machine learning for improved cell segmentation. This module requires both Python and R, as well as an extremely specific Python environment setup in order to function properly. Reference to external documentation is required, although we have provided all necessary links below.

Python Setup:

  1. Install Anaconda on your local machine:
  2. Open the Anaconda terminal (Anaconda Prompt) and switch to the folder containing the “full_environment.yml” file using cd path\to\folder\...
  3. Install the BASIN python environment using the command conda env create -f full_environment.yml - this will take a few minutes
  4. Make sure you have the latest version of cellpose by running pip install cellpose --upgrade
  5. Ensure the installation worked by executing the following commands in the terminal:
    • Activate the environment using conda activate basin
    • Run cellpose using python -m cellpose
    • If the cellpose GUI appears, your installation has been successful
  6. Once Python installation is complete, you can always run cellpose by running python -m cellpose in the Anaconda terminal. Note that any time you open a new Anaconda terminal, you will have to re-run the conda activate basin command in order to activate your cellpose environment.

R Setup:

  1. Make sure you have the latest version of R and Rstudio installed on your computer (free and open-source, available online). Rstudio is an IDE for the R programming language, and all successive steps should be ran through the Rstudio terminal.

  2. Install the required R and Bioconductor packages: install.packages(c("purrr", "plyr", "shiny", "shinyBS", "shinyjs", "shinydashboard", "shinycssloaders", "shinythemes", "shinyWidgets", "DT", "stringi", "ggpubr", "tcltk", "autothresholdr"))

    if (!requireNamespace("BiocManager", quietly = TRUE))\ install.packages("BiocManager") #installs Bioconductor\ BiocManager::install("EBImage") #installs EBImage

  3. Install the reticulate, keras, and tensorflow packages in RStudio using

    install.packages(c(“reticulate”, “keras”, “tensorflow”))

  4. Test the ability for the packages to connect to the Python environment:

    • Run the following commands in R and check for errors:\ library(reticulate)\ env <- conda_list()$name == "basin"\ envPath <- conda_list()[env,]$python\ envPath <- stringi::stri_replace(envPath,"",regex = "python.exe")\ reticulate::use_condaenv(envPath, required=TRUE)\ keras::use_condaenv(envPath, required=TRUE)\ tensorflow::use_condaenv(envPath, required=TRUE)

Running the application:

  1. Download the BASIN-ML folder from this github repository.
  2. Find the ui.R or server.R file in the folder and open it using RStudio
  3. At the top right corner of the opened file, there should be a green button next to the text "Run App". Use that button to start your application. Note that it will take a few seconds for it to load the Python environment.
  4. Some users experience app crashes or freezes after the first run through. You will need to restart your R session if this happens.

Option 2: Package (DEPRACATED)

Details on the features and functionality of BASIN can be found in the BASIN vignette, which is accessible through the package itself. To install BASIN in R, download the tarball file BASIN_0.99.0.tar.gz into your local machine and use the command install.packages("path/to/BASIN"), replacing the "path/to/BASIN" with the location of the file. Once installed, load the package using library(BASIN). The package vignette can be accessed using the command browseVignettes("BASIN") and will contain further instructions on using the package.