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Abstract:
The quantity and complexity of experimental data being recorded in Neuroscience is increasing quickly. Many traditional data analysis tools do not scale to large datasets and there is an urgent need for accessible high-performance algorithms. To this end we developed and released a flexible Blind Source Separation (BSS) method that is capable of handling high-dimensional data such as 2p imaging recordings and encompasses many traditional methods such as sparse Principal Component Analysis, Independent Component Analysis or Non-Negative Matrix Factorization. More concretely, the algorithm is (1) based on a high-throughput probabilistic formulation, (2) can flexibly incorporate prior information about the sources (e.g. sparsity or non-negativity), (3) employs random-projection PCA to reduce its memory-footprint and can (4) be run on the GPU.
We apply the method to a 2p imaging video recorded in a zebrafish, extract regions of interest (such as the position of cells or dendrites) and single-cell calcium traces and then compare with manual labeling. We additionally benchmark the algorithm quantitatively on a composition of a 2p recordings with manually appended cells. On large-scale data the new algorithm can easily be 1-2 orders of magnitude more memory- and run-time efficient then other commonly employed BSS algorithms and can often discover more meaningful sources due to its flexible incorporation of prior information (like the sparse, non-negative responses of single cells with localized ROIs).
