Unmixing biological fluorescence image data with sparse and low-rank Poisson regression.

ABSTRACT:

Motivation

Multispectral biological fluorescence microscopy has enabled the identification of multiple targets in complex samples. The accuracy in the unmixing result degrades (i) as the number of fluorophores used in any experiment increases and (ii) as the signal-to-noise ratio in the recorded images decreases. Further, the availability of prior knowledge regarding the expected spatial distributions of fluorophores in images of labeled cells provides an opportunity to improve the accuracy of fluorophore identification and abundance.

Results

We propose a regularized sparse and low-rank Poisson regression unmixing approach (SL-PRU) to deconvolve spectral images labeled with highly overlapping fluorophores which are recorded in low signal-to-noise regimes. First, SL-PRU imple

SUBMITTER: Wang R 

PROVIDER: S-EPMC10081874 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature