Low Quality Cells Should Be Removed from Single-Cell RNA-Seq Data Analysis

Chen, Geng; Ren, Mindong; Lv, Chengkai; Shi, Tieliu

doi:10.2139/ssrn.3307902

Cited by 1 publication

(1 citation statement)

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“…In the GeneSelector framework, scRNA-seq data preprocessing plays an integral part, beginning with the Seurat package for quality control and normalization. Initial steps involve filtering cells based on gene count thresholds to remove potential technical noise and non-informative signals [18]. The pipeline then systematically filters the genes to retain those genes expressed in a sufficient number of cells, enhancing signal clarity.…”

Section: Methodsmentioning

confidence: 99%

Genetic Optimization in Uncovering Biologically Meaningful Gene Biomarkers for Glioblastoma Subtypes

Paplomatas,

Douroumi,

Vlamos

et al. 2024

BioMedInformatics

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Background: Glioblastoma multiforme (GBM) is a highly aggressive brain cancer known for its challenging survival rates; it is characterized by distinct subtypes, such as the proneural and mesenchymal states. The development of targeted therapies is critically dependent on a thorough understanding of these subtypes. Advances in single-cell RNA-sequencing (scRNA-seq) have opened new avenues for identifying subtype-specific gene biomarkers, which are essential for innovative treatments. Methods: This study introduces a genetic optimization algorithm designed to select a precise set of genes that clearly differentiate between the proneural and mesenchymal GBM subtypes. By integrating differential gene expression analysis with gene variability assessments, our dual-criterion strategy ensures the selection of genes that are not only differentially expressed between subtypes but also exhibit consistent variability patterns. This approach enhances the biological relevance of identified biomarkers. We applied this algorithm to scRNA-seq data from GBM samples, focusing on the discovery of subtype-specific gene biomarkers. Results: The application of our genetic optimization algorithm to scRNA-seq data successfully identified significant genes that are closely associated with the fundamental characteristics of GBM. These genes show a strong potential to distinguish between the proneural and mesenchymal subtypes, offering insights into the molecular underpinnings of GBM heterogeneity. Conclusions: This study introduces a novel approach for biomarker discovery in GBM that is potentially applicable to other complex diseases. By leveraging scRNA-seq data, our method contributes to the development of targeted therapies, highlighting the importance of precise biomarker identification in personalized medicine.

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Section: Methodsmentioning

confidence: 99%