To get a sense for the challenges posed by imaging data and the utility of these automated methods here are some gifs that illustrate the analysis of optical data using this tool. The data presented in this post is calcium imaging data taken from Takaki Komiyama's lab from Akinori Mitani.
So making sense of data like this is quite a challenge. The standard method of extracting signals is to manually draw ROIs (Regions of Interest) around each cell. This is tedious and can miss signals. Further, some somas are not even in focus and the cells can be missed. PCA and ICA are automated techniques which can greatly aid in the extraction of the optical signals.
PCA looks at the correlations in the data and shows ensembles of cells which share certain activity patterns. ICP can be used to browse the PCs:
ICA looks for signals that are maximally independent. This can be used to extract the signals coming from individual cells.
Part of what makes this analysis useful is that no manual ROIs have to be drawn to extract cellular signals. This is especially useful for cells whose somas are not in focus, but have large axons/dendrites in the image. It would be a challenge just to draw an ROI around such cells, but the ICA algorithm can pull them out automatically.
ICA can further isolate noise components from signal components. However, ICA is naive to the difference between cellular signals and components from noise. The results must be filtered manually by hand, or simple algorithms could be used to pull out desired signals from noise signals (i.e. looking for spatially localized components). Here is an example of a noise component from this dataset:
The diagonal corrigated lines is likely an artifact from the two-photon acquisition (or possibly because of a motion-correction algorithm). You can see it pop-up for a few frames in the raw data. ICA pulls this noise out and it can be separated from the signals of interest.