MOTIVATION: Single-cell RNA sequencing (scRNA-seq) has become an important method for characterizing cellular heterogeneity, revealing more biological insights than the bulk RNA-seq. The surge in scRNA-seq data across multiple individuals calls for efficient and statistically powerful methods for differential expression (DE) analysis that addresses individual-level biological variability. RESULTS: We introduced DiSC, a method for conducting individual-level DE analysis by extracting multiple distributional characteristics, jointly testing their association with a variable of interest, and using a flexible permutation testing framework to control the false discovery rate (FDR). Our simulation studies demonstrated that DiSC effectively controlled the FDR across various settings and exhibited high statistical power in detecting different types of gene expression changes. Moreover, DiSC is computationally efficient and scalable to the rapidly increasing sample sizes in scRNA-seq studies. When applying DiSC to identify DE genes potentially associated with COVID-19 severity and Alzheimer's disease across various types of peripheral blood mononuclear cells and neural cells, we found that our method was approximately 100 times faster than other state-of-the-art methods and the results were consistent and supported by existing literature. While DiSC was developed for scRNA-seq data, its robust testing framework can also be applied to other types of single-cell data. We applied DiSC to cytometry by time-of-flight data, DiSC identified significantly more DE markers than traditional methods. AVAILABILITY AND IMPLEMENTATION: The R software package "SingleCellStat" is freely available on CRAN (https://cran.r-project.org/web/packages/SingleCellStat/index.html) and GitHub (https://github.com/Lujun995/DiSC). The replication code for reproducing the analyses in this study is publicly accessible at https://github.com/Lujun995/DiSC_Replication_Code. The scRNA-seq expression matrix and metadata utilized in our simulations and analyses can be retrieved from https://cells.ucsc.edu/autism/rawMatrix.zip, https://cellxgene.cziscience.com/collections/1ca90a2d-2943-483d-b678-b809bf464c30, and https://covid19.cog.sanger.ac.uk/submissions/release1/haniffa21.processed.h5ad.

BACKGROUND: After decades of identifying risk factors using array-based genome-wide association studies (GWAS), genetic research of complex diseases has shifted to sequencing-based rare variants discovery. This requires large sample sizes for statistical power and has brought up questions about whether the current variant calling practices are adequate for large cohorts. It is well-known that there are discrepancies between variants called by different pipelines, and that using a single pipeline always misses true variants exclusively identifiable by other pipelines. Nonetheless, it is common practice today to call variants by one pipeline due to computational cost and assume that false negative calls are a small percent of total. RESULTS: We analyzed 10,000 exomes from the Alzheimer's Disease Sequencing Project (ADSP) using multiple analytic pipelines consisting of different read aligners and variant calling strategies. We compared variants identified by using two aligners in 50,100, 200, 500, 1000, and 1952 samples; and compared variants identified by adding single-sample genotyping to the default multi-sample joint genotyping in 50,100, 500, 2000, 5000 and 10,000 samples. We found that using a single pipeline missed increasing numbers of high-quality variants correlated with sample sizes. By combining two read aligners and two variant calling strategies, we rescued 30% of pass-QC variants at sample size of 2000, and 56% at 10,000 samples. The rescued variants had higher proportions of low frequency (minor allele frequency [MAF] 1-5%) and rare (MAF < 1%) variants, which are the very type of variants of interest. In 660 Alzheimer's disease cases with earlier onset ages of ≤65, 4 out of 13 (31%) previously-published rare pathogenic and protective mutations in APP, PSEN1, and PSEN2 genes were undetected by the default one-pipeline approach but recovered by the multi-pipeline approach. CONCLUSIONS: Identification of the complete variant set from sequencing data is the prerequisite of genetic association analyses. The current analytic practice of calling genetic variants from sequencing data using a single bioinformatics pipeline is no longer adequate with the increasingly large projects. The number and percentage of quality variants that passed quality filters but are missed by the one-pipeline approach rapidly increased with sample size.