Fast PCA for processing calcium-imaging data from the brain of Drosophila melanogaster

BMC Medical Informatics and Decision Making

Proceedings

Martin Strauch¹  C Giovanni Galizia² 

[1] Bioinformatics and Information Mining, University of Konstanz, 78457, Konstanz, Germany;Neurobiology, University of Konstanz, 78457, Konstanz, Germany;Neurobiology, University of Konstanz, 78457, Konstanz, Germany;

关键词: Principal Component Analysis;    Singular Value Decomposition;    Independent Component Analysis;    Covariation Probability;    Source Separation;   

DOI  :  10.1186/1472-6947-12-S1-S2

来源: Springer

【 摘 要 】

BackgroundThe calcium-imaging technique allows us to record movies of brain activity in the antennal lobe of the fruitfly Drosophila melanogaster, a brain compartment dedicated to information about odors. Signal processing, e.g. with source separation techniques, can be slow on the large movie datasets.MethodWe have developed an approximate Principal Component Analysis (PCA) for fast dimensionality reduction. The method samples relevant pixels from the movies, such that PCA can be performed on a smaller matrix. Utilising a priori knowledge about the nature of the data, we minimise the risk of missing important pixels.ResultsOur method allows for fast approximate computation of PCA with adaptive resolution and running time. Utilising a priori knowledge about the data enables us to concentrate more biological signals in a small pixel sample than a general sampling method based on vector norms.ConclusionsFast dimensionality reduction with approximate PCA removes a computational bottleneck and leads to running time improvements for subsequent algorithms. Once in PCA space, we can efficiently perform source separation, e.g to detect biological signals in the movies or to remove artifacts.

【 授权许可】

CC BY   
© Strauch and Galizia; licensee BioMed Central Ltd. 2012