Elucidating transcriptomic profiles from single-cell RNA sequencing data using nature-inspired compressed sensing.
IF: 13.994
Cited by: 5


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-inspired optimization algorithms (CSCS, ABCCS, BACS and FACS) to improve the quality of the decompression process. Several experiments establish that the three proposed methods outperform benchmark methods on nine different datasets, especially the FACS method. We illustrate therefore, the robustness and convergence of FACS in various aspects; notably, time complexity and parameter analyses highlight properties of our proposed FACS. Furthermore, differential gene-expression analysis, cell-type clustering, gene ontology enrichment and pathology analysis are conducted, which bring novel insights into cell-type identification and characterization mechanisms from different perspectives. All algorithms are written in Python and available at https://github.com/Philyzh8/Nature-inspired-CS.


Spatial Transcriptomics

MeSH terms

Cluster Analysis
Computational Biology
Gene Expression Profiling
Gene Regulatory Networks
Molecular Sequence Annotation
Reproducibility of Results
Signal Transduction
Single-Cell Analysis
Time Factors


Yu, Zhuohan
Bian, Chuang
Liu, Genggeng
Zhang, Shixiong
Wong, Ka-Chun
Li, Xiangtao

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