MOTIVATION: Single-cell technologies enable high-resolution cellular studies but face challenges in identifying differential features due to data complexity. RESULTS: We present OTMODE, a non-parametric method using unbalanced Sinkhorn algorithm and Wald test to improve differential feature identification in single-cell multi-omics data. Under simulation, OTMODE achieved superior performance (average 90% F1 score; average 92% AUC score) with high efficiency (2.2 s for 5000 cells). In practice, it shows greater sensitivity than other state-of-the-art methods in detecting meaningful processes and can evaluate annotation accuracy by identifying potentially misannotated clusters from auto-annotation tools. Furthermore, OTMODE integrates seamlessly with Scanpy, offering a user-friendly solution for researchers. AVAILABILITY AND IMPLEMENTATION: OTMODE is freely available at https://github.com/Eggong/OTMODE and also available at https://pypi.org/project/OTMODE/.

Abstract Background Genome-wide association studies (GWAS) have revealed numerous loci for complex diseases, yet the cellular contexts through which these variants act remain poorly understood. Single-cell RNA sequencing (scRNA-seq) offers high-resolution profiling of cellular heterogeneity, but linking GWAS signals to disease-relevant cells remains a challenge. Methods We developed a statistical framework that integrates GWAS-prioritized genes with single-cell transcriptomes to detect disease-relevant cells. Each target cell is contrasted with pools of healthy reference cells, ensuring that differences reflect disease rather than technical variability. Two controls are implemented: reference cells matched by sequencing depth and expression-matched control genes without known disease associations. Iterative Monte Carlo sampling generates comparisons across random draws of reference cells and control genes, reducing stochastic noise. A difference-of-differences statistic captures disease-specific shifts, with Fisher’s method aggregating significance and enrichment tests identifying cell types overrepresented in patients. Results In simulations and real-world datasets, the framework showed strong performance. In large peripheral blood cohorts, it consistently identified trait-associated populations, with monocytes as a benchmark. Analyses of disease datasets revealed both established and novel pathogenic populations, including age-associated B cells and cytotoxic CD8+ T cells in systemic lupus erythematosus, antigen-presenting acinar cells in Sjögren’s syndrome, and dysregulated proximal tubules in chronic kidney disease. Conclusions These findings demonstrate the utility of integrating GWAS with single-cell transcriptomics to pinpoint disease-relevant cells in complex traits.

The arrival of cloud computing age makes data outsourcing an important and convenient application. More and more individuals and organizations outsource large amounts of graph data to the cloud computing platform (CCP) for the sake of saving cost. As the server on CCP is not completely honest and trustworthy, the outsourcing graph data are usually encrypted before they are sent to CCP. The optimal route finding on graph data is a popular operation which is frequently used in many fields. The optimal route finding with support for semantic search has stronger query capabilities, and a consumer can use similar words of graph vertices as query terms to implement optimal route finding. Due to encrypting the outsourcing graph data before they are sent to CCP, it is not easy for data customers to manipulate and further use the encrypted graph data. In this paper, we present a solution to execute privacy‐guarding optimal route finding with support for semantic search on the encrypted graph in the cloud computing scenario (PORF). We designed a scheme by building secure query index to implement optimal route finding with support for semantic search based on searchable encryption idea and stemmer mechanism. We give formal security analysis for our scheme. We also analyze the efficiency of our scheme through the experimental evaluation.

17th Annual Conference of the International Speech Communication Association, INTERSPEECH 2016, San Francisco, US, 8-16 September 2016

This paper aims to give a quantitative description of the linguistic acoustic properties, i.e., fundamental frequency and duration, of a seven-tone dialect in Qiyang County (QY hereafter), south of Hunan Province, China. There are seven surface contrasting tones in the QY dialect, a branch of Old Xiang group. No acoustic analysis has been done in the QY dialect so far. What this paper concentrates on is the falling-rising-falling (FRF hereafter) pitch in two of the seven tones, which has been reported nowhere else. Many parameters which have potential influence on the pitch contour are considered so as to specify the factors which contribute to the forming of this FRF contour. Although emphasis is placed on the analysis of citation tones of monosyllables, evidence from disyllables will be provided as well to support the pitch contour found in monosyllables. But a further perception test should be conducted to decide if the FRF contour is linguistically significant.