Is there a way to directly identify or select a detected cell on a image from a scattered plot? For instance, by double clicking in coordinates on the plot, corresponding cell on the image is selected as well.
Can such link be extended with R studio for instance.
You would have to specifically define which scatter plot, and generated by what. It is probably possible in some instances but very difficult in others. I don’t know of an easy way to do it instantly, but you can do things like use FCSExpress or other flow cytometry software to gate cell populations from QuPath text file lists of cells, sheets, modify the entries based on the gate, and then apply a new measurement to all cells based on whether or not they were gated.
That would require at least two scripts, one for the export and one for the import. Someone did something similar for clustering back in… February I think.
… of detected cells.
One thing at a time, for now i’m interested in being able to visualize exactly where a cell is located on the image by, for instance, double clicking its coordinates on a plot chart.
It was a very long thread, but the idea was to run the clustering algorithm outside of qupath, and import the results back into QuPath.
If you had a way to figure out which cells you were clicking on in the other program, you could use those cells (identified by a variable that counts from 1 to… however many cells you have) to do something in QuPath. That something could be labeling them a certain color, zooming in on them at a given XY coordinate, or something else.
None of that is built in, however, and you would have to code it.
Well, of course, but what are the axes of the plot, what are you plotting?
(I am assuming IJ here…). Unless you are storing (somewhere) the coordinates (or the index) of the cells the data was extracted from and link that to the plot points, you would not know which point in a plot corresponds to a given observation (of course if the axes of the plot were the x and y coordinates of the cells you would know, but I guess that is not the case here).
I think Norbert Vischer’s plugin ObjectJ could do that kind of linking, but it is a long time since I played with that: https://sils.fnwi.uva.nl/bcb/objectj/2-Tutorial.html
Regarding research projects, I really don’t know… there are lots of problems requiring bioimage analysis, and there is pretty much always a better way to do things than anything that currently exists (although finding that can be very hard).
Since I’m from a computer science background, I always end up collaborating with at least one other person (biologist or clinician). Often it’s the people I’d be working with and the data they have access to that determines the projects I choose… the computational side can always be made interesting by getting deeply enough into it
If you don’t have a chance to collaborate with someone, you could look for a public dataset that interests you (e.g. the Broad Bioimage Benchmark Collection or something on Kaggle). Benchmarking information helps a lot, since often one of the biggest problems is figuring out how to assess if your image analysis method works well enough.
This may already be achievable just by scripting. Note that the classification of the cells is preserved, so that if you have classified your cells differently in the different regions then this should be visible in the plot.
Additionally, the line
def cells = getCellObjects()
gets all the cells in the image. Potentially you could identify the cells some other way, e.g. filtering to keep only cells that have been detected inside an annotation that has the name ‘granuloma’:
This does however all become quite specific; so your workflow would need to be designed to make it possible. I can’t really suggest an easy generic way to do this, it involves getting quite deep into the specific application and how QuPath works.
1- I noticed that a couple of packages were not listed in the README file but they should be installed by the app if you didn’t install them so I don’t think they are the problem. What errors do you get?
2- This is not a file you’re supposed to find. You clone the repository by running this command in a terminal: git clone email@example.com:meechan/image-data-explorer.git. Anyway, currently, the only file needed is image_data_explorer.R which you can also download manually.