UPDATE: Zenodo migration postponed to Oct 13 from 06:00-08:00 UTC. Read the announcement.

Dataset Open Access

Node-Pore Coded Coincidence Correcting Microfluidic Channel Framework: Code Design and Sparse Deconvolution

Kellman, Michael; Rivest, Francois; Pechacek, Alina; Sohn, Lydia; Lustig, Michael

This is the dataset for the work titled and authored by:

Node-Pore Coded Coincidence Correcting Microfluidic Channel Framework: Code Design and Sparse Deconvolution

Michael Kellman, Francois Rivest, Alina Pechacek, Lydia Sohn, Michael Lustig

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.

Software can be found under this DOI:

10.5281/zenodo.846448

Files (33.7 MB)
Name Size
B11_colloidmix_newprotein_42116_1PSI.mat
md5:7829db8ed7b414abdea7914521a13c37
12.8 MB Download
B13_colloidmix_newprotein_42116_1PSI.mat
md5:a93c4c2b1b41f7cc310c6ff71b79cc29
7.6 MB Download
B7_colloidmix_newprotein_42116_1PSI.mat
md5:cedd4f10d26026360abaf54e2f6d74fe
13.3 MB Download
readme.md
md5:de9eea5061c66d190e60203a98c20c30
3.1 kB Download
112
52
views
downloads
All versions This version
Views 112113
Downloads 5252
Data volume 397.9 MB397.9 MB
Unique views 101102
Unique downloads 2828

Share

Cite as