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Deconvolution pipeline
Figure S5 presents the details of the pipeline.

-First step: align_PSF_DataStack.m

Empirical 3D system PSF is aligned (along the z-axis) with the raw data stack. 
This is achieved by performing deconvolution (Richardson-Lucy) of a small number of 
z-slices (typically separated by 100 µm) of data with a set of 2D PSFs sampled at 
different depths (typically separated by 10 µm). The resulting deconvolved images 
are analyzed (manually or automatically) for sharpness to determine the global 
z-axis alignment of the system PSF and the raw data stack.

-Second step: deconv_Time_Series_Data.m
All the z-slices of the image stack are deconvolved using 2D PSFs sampled from system 
PSF at correspondingly aligned z-positions. For time lapse datasets, PSF and stack 
alignment is calculated using the first time point data, which is then used to 
deconvolve all the time points

script used for cellular segmentation

Delta F over F
gen_Baseline_For_DFOF.m : generates baseline F as an average over entire recording duration
gen_DFOF.m : generate delta F over F

PCA and ICA analyses for identifying synchrony (Figure 7)
Synchrony_ICA_PCA_analysisCode.m : All the code used to generate Figure 7 is
calculate_iterative_noise.m : function to calculate the noise level cutoff for dF/F traces

Utility scripts
proj_In_Time.m : generate maximum intensity, average intensity and standard deviation over a 
specified recording duration
gen_XZ_YZ_MIProj.m : generate XZ and YZ maximum intensity projections
Python scripts used for FWHM calculations of the empirical PSF  (Figure 1C-top plot)


Code used for Tomer et al 2015 Cell, SPED light sheet microscopy paper




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