Deep learning frameworks have been developed for interpreting single-cell RNA sequencing (scRNA-seq) data and have demonstrated excellent performance across a range of tasks. However, existing methods remain limited in their ability to characterize heterogeneity at the individual level. To address this gap, we present scHILL, a framework that integrates a masked autoencoder (MAE) with a multilayer perceptron (MLP) to decipher phenotypic heterogeneity arises from immune cell heterogeneity among individuals under specific disease conditions. The MAE, pretrained with data augmentation, enables self-supervised feature learning without labels and effectively mitigates the challenge of limited sample size. The MLP further generates a score for each individual to quantify the functional significance of cells and genes. Across multiple datasets, scHILL outperforms existing methods in phenotype prediction and reveals individual-level immune cell heterogeneity in infectious disease, autoimmune disease, and cancer. scHILL provides a generalizable framework for interpreting individual-level scRNA-seq data, thereby facilitating the future realization of personalized medicine.

Haplotype networks are graphs used to represent evolutionary relationships between a set of taxa and are characterized by intuitiveness in analyzing genealogical relationships of closely related genomes. We here propose a novel algorithm termed McAN that considers mutation spectrum history (mutations in ancestry haplotype should be contained in descendant haplotype), node size (corresponding to sample count for a given node) and sampling time when constructing haplotype network. We show that McAN is two orders of magnitude faster than state-of-the-art algorithms without losing accuracy, making it suitable for analysis of a large number of sequences. Based on our algorithm, we developed an online web server and offline tool for haplotype network construction, community lineage determination, and interactive network visualization. We demonstrate that McAN is highly suitable for analyzing and visualizing massive genomic data and is helpful to enhance the understanding of genome evolution. Availability: Source code is written in C/C++ and available at https://github.com/Theory-Lun/McAN and https://ngdc.cncb.ac.cn/biocode/tools/BT007301 under the MIT license. Web server is available at https://ngdc.cncb.ac.cn/bit/hapnet/. SARS-CoV-2 dataset are available at https://ngdc.cncb.ac.cn/ncov/. Contact: [email protected] (Song S), [email protected] (Zhao W), [email protected] (Bao Y), [email protected] (Zhang Z), [email protected] (Xue Y).

Sparse general matrix-matrix multiplication (SpGEMM) is one of the most fundamental building blocks in sparse linear solvers, graph processing frameworks and machine learning applications. The existing parallel approaches for shared memory SpGEMM mostly use the row-row style with possibly good parallelism. However, because of the irregularity in sparsity structures, the existing row-row methods often suffer from three problems: (1) load imbalance, (2) high global space complexity and unsatisfactory data locality, and (3) sparse accumulator selection.

SUMMARY: mRNA/miRNA-seq technology is becoming the leading technology to globally profile gene expression and elucidate the transcriptional regulation mechanisms in living cells. Although there are many tools available for analyzing RNA-seq data, few of them are available as easy accessible online web tools for processing both mRNA and miRNA data for the RNA-seq based user community. As such, we have developed a comprehensive web application tool for processing mRNA-seq and miRNA-seq data. Our web tool wapRNA includes four different modules: mRNA-seq and miRNA-seq sequenced from SOLiD or Solexa platform and all the modules were tested on previously published experimental data. We accept raw sequence data with an optional reads filter, followed by mapping and gene annotation or miRNA prediction. wapRNA also integrates downstream functional analyses such as Gene Ontology, KEGG pathway, miRNA targets prediction and comparison of gene's or miRNA's different expression in different samples. Moreover, we provide the executable packages for installation on user's local server. AVAILABILITY: wapRNA is freely available for use at http://waprna.big.ac.cn. The executable packages and the instruction for installation can be downloaded from our web site. CONTACT: [email protected]; [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.