README.md

In grrchrr/VisumotR: Visualizing Motility on Images

VisumotR - Visualizing Motility Parameters on Images in R

Live-cell microscopy has become an essential tool for analyzing dynamic processes in various biological applications. To critically assess the influence of individual cells on the calculated summary statistics, and to detect heterogeneous cell populations or possible confounding factors, such as misclassified or -tracked objects, a direct mapping of gained statistical information onto the actual image data is necessary. VisumotR allows to visualize time-resolved motility parameters or any other summary statistic onto images in R. Originally, this package was intended to be used with live-cell microscopy images and cell-tracking data. But in general VisumotR can be used with any kind of data supplying a dataframe with trackIds, time and spatial coordinates (2D/3D), as well as numeric or discrete mapping parameters and corresponding images.

The package acts as a wrapper for annotating provided images with several, modifiable ggplot2-layers. With visumot_frame(), continous and discrete parameters can be mapped individually on color, shape and size for one timepoint. visumot_summary() allows to create a corresponding plot of all given statistics for the whole range of the provided data. A time-resolved image-series can be created with visumot_all() which allows to combine mapped frames and summary plots created by visumot_frame() and visumot_summary(). The ouput of all functions are ggplot2-objects which can further be manipulated in a common manner.

Display modes

Mapping color and shape

VisumotR allows for several display modes. The most common is shown above where the complete image is captured and one continous parameter is mapped on the track color. In addition, it is also possible to map discrete variables on shape and/or color or continous variables on size and/or alpha. In this example, contact-state and infection-status of tracked cells were mapped to color and shape.

Color-mapping combined with summary statistics

Each output-type of visumot_frame() can be combined with the output from visumot_summary().

Follow single tracks within sub-windows

The output of visumot_frame() does not need to be the whole image. It can be cropped manually or automatically or create sub-windows that allow to follow individual tracks, that might be interesting due to prior perfomed statistical analysis that highlighted for example outliers.

3D images and Z-projections

VisumotR supports .tiff-stacks of 3D image data as input. Since ggplot2 just works in 2D, the user can choose from different Z-projections to view the data in 2D. All output modes work with 3D-image data. In addition, it is possible to follow several individual tracks on their respective Z-axis in parallel.

GUI: Shiny App

In addition to the functionality as an R-package, VisumotR can be completely used within a Shiny-app by calling visumot_shiny(). The Shiny-app acts as an graphical-user interface and allows to import and prepare tracking and image data as well as process these with all VisumotR functionalities. In contrast to using VisumotR in an IDE or terminal, the Shiny-app adds a much more interactive way of data-analysis and allows users that are not proficient with the R language to process their data with VisumotR.

Install VisumotR

# install dependencies which are not found on CRAN
devtools::install_github("zeehio/facetscales")
remotes::install_github("coolbutuseless/ggecho")

# install VisumotR from git repo
devtools::install_git("https://github.com/grrchrr/VisumotR")

grrchrr/VisumotR documentation built on March 3, 2021, 11:15 a.m.