MyoManager is a R toolbox for reading, visualizing, and analyzing microscopy images focused on muscle stem cells in their native microenvironment — the muscle tissue niche. Specifically, these analysis tools will be implemented: cell counting (by counting #nuclei), nuclei morphology (by extracting quantitative shape/size features of segmented objects from image data), and signal co-localization (work in progress). While muscle stem cells are well characterized in literature, imaging these characteristics is a laborious task for researchers, the goal of this tool is to automate image analysis of hard-to-distinguish structures in muscle stem cells and provided better insight into their biology and behaviors.
Bio-image analysis is not novel to the R environment. Packages like EBImage contain a wide assortment of processing functions. However they are not sufficient enough for complex, hard-to-distinguish structures such as muscle stem cells. MyoManager is a highly specific package built for analyzing muscle stem cells in varying developmental stages in the tissue, which has not been previously attempted. Not only does this package handle visualization and basic image manipulations, it combines several general purpose functionalities of EBImage to simplify the image analysis workflow and extract image data useful for understanding muscle stem cells.
Anticipated audience of MyoManager are students and researchers who are interested in muscle stem cells and seek custom analysis with tailored output. . The package was developed using R version 4.1.1 and Mac platform.
To install the latest version of the package:
require("devtools")
install_github("karenkuang37/MyoManager", build_vignettes = TRUE)
library("MyoManager")Try out the shinyApp implementation of MyoManager:
MyoManager::runMyoManager()ls("package:MyoManager")
data(package = "MyoManager") There are (currently) 4 groups of functions available to the user:
1. loadAnadDisplay handles the loading and viewing of microscopy images. Functions loadImage and viewImage from this R script allow users to load and view a variety of image formats including jpg, png, and tiff files in single or multi-frame interactive display windows.
2. imageProcessing permits users to perform simple image manipulations including frame selection ( selectFrame ), blurring ( blurImage ), brightness and contrast adjustments ( intensityCtrl ). These can enhance the visual quality of microscopy images, serving as preparation for later analysis.
3. segmentImage contains a single function: segmentImage, which is available for the user to generate segmented (separating lines between distinct objects) visuals of cell and nuclei shapes.
4. nucleiTool contains several functions to support the counting of cell nuclei ( countNuclei ); measurement of nuclei morphological features included area, perimeter, mean radius, and eccentricity ( getFeatureData ); density distribution of one of these features ( plotFeatureMatrix ); and a matrix of pairwise scatter plots, density plots, and correlation values of these shape/size features ( plotFeatureMatrix ).
**5.Future progress: ** MyoManager will be continually developed to support more analysis functions specific to muscle stem cells, such as calculation of fusion index (ratio of [#stem cells fused with muscle fiber]:[#stem cells outside muscle fiber]), and signal co-localization (how the distribution of signals in fluorescence microscopy images can be used to determine whether two probes codistribute with one another.).
For more details about the functions, please take a look at the vignette for this package:
browseVignettes("MyoManager")The package tree structure is provided below:
- MyoManager
|- MyoManager.Rproj
|- DESCRIPTION
|- NAMESPACE
|- LICENSE
|- README
|- inst
CITATION
|- extdata
|- Human_01.tiff
|- Human_02.tiff
|- Mouse_01.tiff
|- Mouse_02.tif
|- Rabbit_01.tif
|- Rabbit_02.tif
|- overview_pic.png
|- shiny-scripts
|- app.R
|- man
|- loadImage.Rd
|- viewImage.Rd
|- validImage.R
|- blurImage.Rd
|- countNuceli.Rd
|- getFeatureData.Rd
|- intensityCtrl.Rd
|- is.wholenumber.Rd
|- plotFeature.Rd
|- plotFeatureMatrix.Rd
|- segmentImage.Rd
|- selectFrame.Rd
|- R
|- imageProcessing.R
|- loadAndDisplay.R
|- nucleiTool.R
|- segmentImage.R
|- runMyoManager.R
|- vignettes
|- Intrduction_MyoManager.Rmd
|- muscle-dystrophin.jpeg
|- muscle_stem_cell_Growth.jpeg
|- features_dens.png
|- pixel_intensity_hist.png
|- viewImage.png
|- tests
|- testthat.R
|- testthat
|- test-loadAndDisplay.R
|- test-imageProcessing.R
|- test-segmentImage.R
|- test-nucleiTool.RAn overview of the package is illustrated below.
The author of the package is Yinni Kuang.
Function loadImage is a wrap around magick package’s image_read and as_EBImage to load one or more images from files, and store the image objects as class Image from EBImage packge. Function loadImage is a wrap around EBImage package’s display, which takes a Image and prompts R’s graphic display window to open, this device supports single and multi-frame images as well as different degrees of zoom, making it easy to visualize detailed microscopy data.
Function blurImage EBImage’s makeBrush to generates a 2D matrix containing the magic brush of desired shape and size, then to apply the blurring filter to a selected image, filter2 is used.
Function segmentImage employs several functions from EBImage to produce an ideal segmented image. First, otsu computes a threshold value based on Otsu’s method, which can be then used to reduce grayscale image to binary image. Then, I used fillHull to fill in holes left in the binary mask and bwlabel to label connect nuclei objects in the foreground. Next, I used opening to perform image erosion followed by a dilation, and propagate, which is code sourced by EBImage from image analysis software CellProfiler permission granted to distribute this particular part. Propagate uses identified nuclei as ‘seeds’ to find boundaries between adjacent regions in an image. Finally, I used paintObject to higlight nuclei objects in images by outlining them.
Function countNuclei, and getFeatureData also uses the Otsu algorithm to compute threshold values and binary mask prior to counting and measuring nuclei. (Same steps as described above) EBImage functions otsu, fillHull, opening, and bwlabel were employed in the making of binary mask.
Function getFeatureData also uses the computeFeatures and its subsidiary computeFeatures.shape from EBImage calculate the quantitative measurements of nuclei area, perimeter, radius, and eccentricity.
Plotting function plotFeature generated density plots using the ggplot2 package, while function ploteFeatureMatrix produced a matrix of scatter plots and density distribution using ggpairs from the handy GGally library.
Function runMyoManager allows users unfamiliar with the R environment to run a shiny app constructed this package. Inside the app, one can interact with all of the functionalities of the package through a GUI instead of the R commandline.
Jeroen Ooms (2021). magick: Advanced Graphics and Image-Processing in R. R package version 2.7.3. Ref link: https://CRAN.R-project.org/package=magick
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Nguyen John H., Chung Jin D., Lynch Gordon S., Ryall James G, (2019): The Microenvironment Is a Critical Regulator of Muscle Stem Cell Activation and Proliferation. Front. Cell Dev. Biol., Volume 7, pp.254 doi:10.3389/fcell.2019.00254 URL:https://www.frontiersin.org/article/10.3389/fcell.2019.00254
H. Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2016. URL: https://ggplot2.tidyverse.org/
R Core Team (2019). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
This package was developed as part of an assessment for 2021 BCB410H: Applied Bioinformatics, University of Toronto, Toronto, CANADA.