Efficient pattern unmixing of multiplex proteins based on variable weighting of texture descriptors

Abstract

Modern biological imaging techniques enable the exhibition of complex subcellular distributions across different organelles for multiplex proteins. Quantifying the fraction of a protein in each cellular compartment is important for obtaining great insights into various protein functions and cell mechanisms. However, the imaging quality is affected by the specific cell type, resulting in the loss of significant protein subcellular location pattern-related information. To improve the pattern distinguishing ability, herein, we propose a novel concept of texture descriptors via introducing spatial structures of interested micropatterns for variable-weighting modeling of multiplex protein patterns. Aiming at developing an automatic modeling strategy, the particle swarm optimization (PSO) algorithm is also used to optimize the variable weights and other parameters in the models. Such a parameter-free computational system, named TexVW-MPUnmixing, has been applied in multiplex pattern unmixing of proteins based on modeling a cell fluorescence microscope image set while coupling with linear partial least squares (PLS) and a non-linear support vector machine (SVM) separately. The results demonstrate that the proposed TexVW-MPUnmixing is able to greatly improve the protein pattern unmixing precision because of the introduction of spatial structure descriptors. It, thus, holds great potential in efficient automatic unmixing of multiplex protein patterns.