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

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本数据集对应题为《节点孔编码巧合校正微流道框架：编码设计与稀疏反卷积》的研究工作，作者为Michael Kellman、Francois Rivest、Alina Pechacek、Lydia Sohn、Michael Lustig。我们提出了一种全新方法，可通过联合通道设计与算法手段实现单颗粒（例如细胞或病毒）的巧合校正。受多用户通信理论启发，我们借助节点孔传感（Node-Pore Sensing）技术调制通道响应，为每个颗粒赋予二进制巴克码（Barker code）特征标识。当结合我们改进的逐次干扰消除（successive interference cancellation）方法进行处理时，该特征标识可实现重合颗粒的分离，同时对微小颗粒具备高检测灵敏度。我们识别出多种建模误差来源，并通过数据驱动自校准步骤与稳健回归方法缓解了绝大多数影响。此外，我们开展了仿真分析，以阐明我们的方法针对这类随机系统模型误差的鲁棒性与局限性。最后，我们使用多台编码设备开展实验验证，对多尺寸颗粒组成的异质样本进行筛选。相关软件可通过以下DOI获取：10.5281/zenodo.846448