Elucidating transcriptomic profiles from single-cell RNA sequencing data using nature-inspired compressed sensing.

ABSTRACT: Gene-expression profiling can define the cell state and gene-expression pattern of cells at the genetic level in a high-throughput manner. With the development of transcriptome techniques, processing high-dimensional genetic data has become a major challenge in expression profiling. Thanks to the recent widespread use of matrix decomposition methods in bioinformatics, a computational framework based on compressed sensing was adopted to reduce dimensionality. However, compressed sensing requires an optimization strategy to learn the modular dictionaries and activity levels from the low-dimensional random composite measurements to reconstruct the high-dimensional gene-expression data. Considering this, here we introduce and compare four compressed sensing frameworks coming from nature-inspir

SUBMITTER: Yu Z 

PROVIDER: S-EPMC8579163 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature