scDRS is a method that links scRNA-seq with polygenic risk of disease at individual cell resolution, as described in our paper “Polygenic enrichment distinguishes disease associations of individual cells in single-cell RNA-seq data” (Zhang*, Hou* et al. 2022).
Popular k-medoids clustering algorithms, such as Partitioning Around Medoids (PAM), are prohibitively expensive in computation for large datasets. BanditPAM is a randomized version of PAM that returns the same results with high probability but is substantially faster. The algorithm is described in our paper “BanditPAM: Almost Linear Time k-Medoids Clustering via Multi-Armed Bandits” (Tiwari et al. 2020).
Empirical Bayes estimators for single-cell RNA-seq analysis, as described in our paper “Determining sequencing depth in a single-cell RNA-seq experiment” (Zhang*, Ntranos* et al. 2020).
AdaFDR is a fast and covariate-adaptive method that learns adaptive p-value thresholds from covariates to improve power while controlling FDR. The method is described in our paper “Fast and covariate-adaptive method amplifies detection power in large-scale multiple hypothesis testing” (Zhang et al. 2019).
cPCA is a dimensionality reduction algorithm that identifies low-dimensional structures that are enriched in a dataset relative to comparison data. Applications include dicovering subgroups in biological and medical data. The method is described in our paper “Exploring patterns enriched in a dataset with contrastive principal component analysis” (Abid*, Zhang* et al. 2019).