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2 results for “Sparse deconvolution”
Node-Pore Coded Coincidence Correcting Microfluidic Channel Framework: Code Design and Sparse Deconvolution
<p>This is the dataset for the work titled and authored by:</p> <p><strong>Node-Pore Coded Coincidence Correcting Microfluidic Channel Framework: Code Design and Sparse Deconvolution</strong></p> <p>Michael Kellman, Francois Rivest, Alina Pechacek, Lydia Sohn, Michael Lustig</p> <p>We present a novel method to perform individual particle (e.g. cells or viruses) coincidence correction through joint channel design and algorithmic methods. Inspired by multiple-user communication theory, we modulate the channel response, with Node-Pore Sensing, to give each particle a binary Barker code signature. When processed with our modified successive interference cancellation method, this signature enables both the separation of coincidence particles and a high sensitivity to small particles. We identify several sources of modeling error and mitigate most effects using a data-driven self-calibration step and robust regression. Additionally, we provide simulation analysis to highlight our robustness, as well as our limitations, to these sources of stochastic system model error. Finally, we conduct experimental validation of our techniques using several encoded devices to screen a heterogeneous sample of several size particles.</p> <p>Software can be found under this DOI:</p> <p>10.5281/zenodo.846448</p>
Blind Sparse Deconvolution for Spike Inference from Fluorescence: Larval Zebrafish fluorescence recordings
<p>This repository contains the two fluorescence imaging datasets used in the publication "Blind Sparse Deconvolution for Spike Inference from Fluorescence Recordings". Each dataset is a T X N DF/F matrix, where T is the number of fluorescence measurement and N is the number of neurons. They were obtained from light-sheet fluorescence microscopy. Both recordings correspond to spontaneous activity.</p> <p>The first file "DFF_20Hz_GCaMP3.mat" was obtained by a 2D recording acquired at 20 frame/second for 20 minutes of a 5dpf-old zebrafish larva expressing the genetically encoded indicator GCaMP3 (elavl3:GCaMP3). The images were parsed into 8082 neural traces.</p> <p>The second file "DFF_1Hz_GCaMP5.mat" was obtained by a 3D, whole-brain recording acquired at 20 frame/second and 20 stacks, (1 measurement/voxel/s) of a 5dpf-old zebrafish larva expressing the genetically encoded indicator GCaMP5. After segmentation, 255463 fluorescence traces encompassing the brain volume are computed independently.</p>
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