N-ary relational knowledge base (KBs) embedding aims to map binary and beyond-binary facts into low-dimensional vector space simultaneously. Existing approaches typically decompose n-ary relational facts into subtuples (entity pairs, triples or quintuples, etc.), and they generally model n-ary relational KBs in Euclidean space. However, n-ary relational facts are semantically and structurally intact, decomposition leads to the loss of global information and undermines the semantical and structural integrity. Moreover, compared to the binary relational KBs, n-ary ones are characterized by more abundant and complicated hierarchy structures, which could not be well expressed in Euclidean space. To address the issues, we propose a gyro-polygon embedding approach to realize n-ary fact integrity keeping and hierarchy capturing, termed as PolygonE. Specifically, n-ary relational facts are modeled as gyro-polygons in the hyperbolic space, where we denote entities in facts as vertexes of gyro-polygons and relations as entity translocation operations. Importantly, we design a fact plausibility measuring strategy based on the vertex-gyrocentroid geodesic to optimize the relation-adjusted gyro-polygon. Extensive experiments demonstrate that PolygonE shows SOTA performance on all benchmark datasets, generalizability to binary data, and applicability to arbitrary arity fact. Finally, we also visualize the embedding to help comprehend PolygonE's awareness of hierarchies.

MOTIVATION: Single-cell RNA-sequencing (scRNA-seq) is fast and becoming a powerful technique for studying dynamic gene regulation at unprecedented resolution. However, scRNA-seq data suffer from problems of extremely high dropout rate and cell-to-cell variability, demanding new methods to recover gene expression loss. Despite the availability of various dropout imputation approaches for scRNA-seq, most studies focus on data with a medium or large number of cells, while few studies have explicitly investigated the differential performance across different sample sizes or the applicability of the approach on small or imbalanced data. It is imperative to develop new imputation approaches with higher generalizability for data with various sample sizes. RESULTS: We proposed a method called scHinter for imputing dropout events for scRNA-seq with special emphasis on data with limited sample size. scHinter incorporates a voting-based ensemble distance and leverages the synthetic minority oversampling technique for random interpolation. A hierarchical framework is also embedded in scHinter to increase the reliability of the imputation for small samples. We demonstrated the ability of scHinter to recover gene expression measurements across a wide spectrum of scRNA-seq datasets with varied sample sizes. We comprehensively examined the impact of sample size and cluster number on imputation. Comprehensive evaluation of scHinter across diverse scRNA-seq datasets with imbalanced or limited sample size showed that scHinter achieved higher and more robust performance than competing approaches, including MAGIC, scImpute, SAVER and netSmooth. AVAILABILITY AND IMPLEMENTATION: Freely available for download at https://github.com/BMILAB/scHinter. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Autism spectrum disorder (ASD) is a complex neuropsychiatric disorder with substantial phenotypic and genetic heterogeneity. Until now, about a thousand diverse genes have been associated with ASD, while it remains elusive that how disruptions in these different genes can lead to a common clinical phenotype. Therefore, it is essential to understand how ASD candidate genes relate to each other and identify potential shared molecular pathways. Human brain is a highly heterogeneous organ involving multiple cell types. Different functions in different types of cells may be dysregulated in ASD; investigating functional interactions between ASD candidate genes in normal human brain cells may shed new light on the genetic heterogeneity of ASD. To this end, we construct cell type-associated gene co-expression networks based on human brain cell gene expression data. Then we identify seven cell type-specific gene modules and analyze the specific gene functions in each cell type. We also identify six ASD-associated gene modules and study the dysregulated functions in ASD. Lastly, we obtain two ASD-associated cell type-specific gene modules for studying the cell type-specific aberrant functions in ASD. It is found that ASD-associated astrocytes-specific gene modules are relevant to endocytosis, neuron differentiation and cell projection organization, while ASD-associated neurons-specific gene modules are relevant to presynapse, glutamatergic synapse and neuron projection morphogenesis. Our method has been proven to be effective in discovering ASD-associated cell type-specific gene expression pattern. Our findings can promote the study of the heterogeneity of ASD in gene expression between different cell types, providing new insights into the molecular mechanisms underlying the pathogenesis of ASD.