ciatah
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SNR data export
Hi, Biafra~
Lately, I've been working on a two-photon .tiff
file through computeManualSortSignals
on MatLab 2020b, win10. Normally, all things pretty good so far. Thank you for the update version2.5.1
, it's amazing.
Could you please concretely enlighten a handy approach or code to help me to export the SNR
value of every identified cell while or finishing the computeManualSortSignals
part. I could only read it from the interface like below on-by-one.
If there is no misunderstanding on SNR
here in CIAtah v2.5.1
, that is Signals Noise Ratio
, an important parameter I would use someday. So, hope you would help me with that.
And, one more thing, have you ever considered uploading a teaching video on the Internet about the rest of the operation after computeManualSortSignals
?
Best Wishes to you!
Benson from SheepMountain, NJ
hey Benson, great to hear things are working out for you. To grab the SNR you can load in the already processed data then compute SNR as below:
% Load data
[inputImages,inputSignals,infoStruct,algorithmStr] = ciapkg.io.loadSignalExtraction('path_to_file.mat');
% Compute SNR
inputSnr = computeSignalSnr(inputSignals,'detectMethod','raw','numStdsForThresh',2);
re: after computeManualSortSignals
, are there specific steps or analyses you are referring to? We'll have a book chapter out soon that covers some later steps, e.g. comparing neural activity to behavior/stimuli.
Hi, Biafra
Thanks for the reply so quickly.
-
The grabbing is smoothly with your code demonstrated above, which is significant for my job-to-do.
-
Frankly speaking, I am a novice on
CalciumImagingAnalysis
and NeuralScience. There are so many analysis jobs I should- and would- do. Such astime-frequency analysis
within a single neuron or a given population, classified neuronspatial correlation analysis
. Each of them seems worthy yet intricacy to handling. I have to learn some other knowledge and Matlab skills to accomplish the later steps. -
So, it is terrific to acknowledge that the new book chapter comes out soon which covers some later steps.
-
We will follow your great algorithm persistently and looking forward to other more powerful processing approaches.
Benson From SheepMountain, NJ