ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2025
FemXpress: Systematic Analysis of X Chromosome Inactivation Heterogeneity in Female Single-Cell RNA-Seq Samples.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- FemXpress: Systematic Analysis of X Chromosome Inactivation Heterogeneity in Female Single-Cell RNA-Seq Samples.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
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Authors and funding
16 authors.
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Abstract
X chromosome inactivation (XCI) is crucial for balancing X-linked gene dosage in female cells by randomly silencing one X chromosome during early embryogenesis. However, accurately classifying cells based on the parental origin of the inactivated X chromosome in single-cell samples remains challenging. Here we present FemXpress, a computational tool leveraging X-linked single nucleotide polymorphisms (SNPs) to group cells based on the origin of the inactivated X chromosome in female single-cell RNA sequencing (scRNA-Seq) data. FemXpress performs robustly on both simulated and real datasets, without requiring parental genomic information, and can also identify genes that escape XCI. Applying FemXpress to single-cell RNA-Seq data from multiple tissues of a cynomolgus monkey, we reveal heterogeneity in XCI origin across organs and cell types. In each organ, we identify candidate XCI-escaping genes, and within each cell type, we observe gene expression differences associated with XCI origin, potentially contributing to phenotypic variability. Furthermore, FemXpress demonstrated strong performance in phasing XCI in scRNA-Seq datasets from embryos and colon tumors. In summary, FemXpress provides a powerful approach for XCI status analysis, offering new insights into XCI dynamics at single-cell resolution.
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