Advanced: Analysis

Analysis in 𝑓(Cell) assumes that the data being supplied is co-registered. That is, that all datasets are in the same coordinate space such that a coordinates in one video correspond to the same location in the other. This is essential for this analysis process to work correctly. At the moment, 𝑓(Cell) is designed around only en-face imaging devices, and so all analysis approaches are intensity-based. This is likely to change in the future, as our group collaborates and uses our own phase-based methods for analysis- so for those of you looking to analyze phase-based optoretinograms, stay tuned...

Below are the steps in the analysis portion of 𝑓(Cell), where optional steps are indicated by dashed lines:

flowchart TD


    A(Load Dataset) --> B([Parse Tags/Metadata])
    B([Parse Tags/Metadata])  --> Ba([Gaussian Blur])
    Ba([Gaussian Blur])  --> C([Normalize Dataset])
    C([Normalize Dataset])  --> D([Segment Query Points / Extract ORGs])
    D([Segment Query Points / Extract ORG])  --> E([Standardize ORGs])
    E([Standardize ORGs])  --> F([Summarize ORGs])
    F([Summarize ORGs])  --> G([Extract Metrics])
    G([Extract Metrics])  --> H(Display/Output Results)
    
    
    classDef optional stroke-dasharray:4 5;


    click B "https://github.com/OCVL/F-Cell/wiki/Advanced:-Tag-Parsing" "Tag Parsing"
    click C "https://github.com/OCVL/F-Cell/wiki/Advanced:-Analysis-Parameters#data-normalization" "Normalize Data"
    click D "https://github.com/OCVL/F-Cell/wiki/Advanced:-Analysis-Parameters#query-point-segmentation" "Query Point Segmentation"
    click E "https://github.com/OCVL/F-Cell/wiki/Advanced:-Analysis-Parameters#signal-standardization" "Standardization"
    click F "https://github.com/OCVL/F-Cell/wiki/Advanced:-Analysis-Parameters#org-summary" "Summarization"
    click G "https://github.com/OCVL/F-Cell/wiki/Advanced:-Analysis-Metrics" "Metrics"
    click H "https://github.com/OCVL/F-Cell/wiki/Advanced:-Analysis-Display-Parameters" "Display Results"

    class Ba,C,G,H optional;

Loading

The base keys/values of the analysis JSON object follows a similar form as the pre-analysis pipeline:

Key	Parent Key	Type	Options	Description
"image_format"	"analysis"	"string"	parse-augmented string	The filename format of the super-average image generated by averaging all datasets within a group. Uses parse-based tag formatting to extract file-specific metadata.
"video_format"	"analysis"	"string"	parse-augmented string	The filename format of the video (e.g. avi, mp4, etc) associated with a single acquisition. Uses parse-based tag formatting to extract file-specific metadata.
"queryloc_format"	"analysis"	"string"	parse-augmented string	The filename format of the query locations associated with the datasets. These locations will be analyzed within and across each dataset. Uses parse-based tag formatting to extract file-specific metadata.
"recursive_search"	"analysis"	boolean	(true, **false**)	If true, then 𝑓(Cell) will recursively search and include files that fit the format defined in "image_format", "video_format", and "mask_format" within the directories specified at launch.
"metadata"	"analysis"	JSON Object	none	A JSON object holding parameters for the metadata associated with the dataset. See the metadata page for more information.
"analysis_params"	"analysis"	JSON Object	none	A JSON object holding parameters that govern how the datasets are run through the pre-analysis pipeline. See analysis parameters.
"display_params"	"analysis"	JSON Object	none	A JSON object holding parameters that govern how the datasets are displayed both during and after analysis. See display parameters.