Federico Marini (marinif@uni-mainz.de), Johanna Mazur and Harald Binder
Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), Mainz
Center for Thrombosis and Hemostasis (CTH), Mainz
July 1st, 2015
export.Frames
to png
and gif
format (via ImageMagick)locator
function used to behave weirdlysnap()
!
snap()
!
flowcatchR
in a simple scriptlibrary("flowcatchR") data("MesenteriumSubset") plateletsMesenterium <- channel.Frames(MesenteriumSubset, mode="red") preprocessedPlatelets <- preprocess.Frames(plateletsMesenterium,brush.size=3, brush.shape="disc", at.wwidth=10, at.wheight=10,kern.size=3, kern.shape="disc",ws.tolerance=1, ws.radius=1) platelets <- particles(plateletsMesenterium, preprocessedPlatelets) linkedPlatelets <- link.particles(platelets,L=26, R=2,lambda1=1, lambda2=1,include.area=TRUE) trajPlatelets <- trajectories(linkedPlatelets) plot2D.TrajectorySet(trajPlatelets, MesenteriumSubset)
Need of a common computing environment
flowcatchR
in Docker containers# go to the folder containing the files (<< 1 Mb) docker build -t "jupyflow" /path/to/dockerfile # or once it is available online docker pull jupyflow # might take a while docker run -p 8888:8888 jupyflow
… and just open the browser on localhost:8888
!
Additional containers: RStudio server and Shiny server (combined with docker-compose
)
flowcatchR
and the IPython/Jupyter Notebook
shinyFlow
- flowcatchR
as a Shiny app
IMBEI
CTH Mainz
Thank you for your attention!
Manuscript in preparation, flowcatchR: an end-user-friendly workflow solution for automated analysis of fast moving blood cells, Marini F., Mazur J., Binder H.
marinif@uni-mainz.de