In single-cell studies, cells can be characterized with multiple sources of heterogeneity (SOH) such as cell type, developmental stage, cell cycle phase, activation state, and so on. In some studies, many nuisance SOH are of no interest, but may confound the identification of the SOH of interest, and thus affect the accurate annotate the corresponding cell subpopulations. In this paper, we develop B-Lightning, a novel and robust method designed to identify marker genes and cell subpopulations corresponding to an SOH (e.g. cell activation status), isolating it from other SOH (e.g. cell type, cell cycle phase). B-Lightning uses an iterative approach to enrich a small set of trustworthy marker genes to more reliable marker genes and boost the signals of the SOH of interest. Multiple numerical and experimental studies showed that B-Lightning outperforms existing methods in terms of sensitivity and robustness in identifying marker genes. Moreover, it increases the power to differentiate cell subpopulations of interest from other heterogeneous cohorts. B-Lightning successfully identified new senescence markers in ciliated cells from human idiopathic pulmonary fibrosis lung tissues, new T-cell memory and effector markers in the context of SARS-COV-2 infections, and their synchronized patterns that were previously neglected, new AD markers that can better differentiate AD severity, and new dendritic cell functioning markers with differential transcriptomics profiles across breast cancer subtypes. This paper highlights B-Lightning's potential as a powerful tool for single-cell data analysis, particularly in complex data sets where SOH of interest are entangled with numerous nuisance factors.

Multiple testing is a commonly used tool in modern data science. Sometimes, the hypotheses are embedded in a space; the distances between the hypotheses reflect their co-null/co- alternative patterns. Properly incorporating the distance information in testing will boost testing power. Hence, we developed a new multiple testing framework named Distance Assisted Recursive Testing (DART). DART features in joint artificial intelligence (AI) and statistics modeling. It has two stages. The first stage uses AI models to construct an aggregation tree that reflects the distance information. The second stage uses statistical models to embed the testing on the tree and control the false discovery rate. Theoretical analysis and numerical experiments demonstrated that DART generates valid, robust, and powerful results. We applied DART to a clinical trial in the allogeneic stem cell transplantation study to identify the gut microbiota whose abundance was impacted by post-transplant care.

Summary: CRISPR-Cas9 and shRNA high-throughput sequencing screens have abundant applications for basic and translational research. Methods and tools for the analysis of these screens must properly account for sequencing error, resolve ambiguous mappings among similar sequences in the barcode library in a statistically principled manner, and be computationally efficient. Herein we present bcSeq, an open source R package that implements a fast and parallelized algorithm for mapping high-throughput sequencing reads to a barcode library while tolerating sequencing error. The algorithm uses a Trie data structure for speed and resolves ambiguous mappings by using a statistical sequencing error model based on Phred scores for each read. Availability and implementation: The package source code and an accompanying tutorial are available at http://bioconductor.org/packages/bcSeq/. Supplementary information: Supplementary data are available at Bioinformatics online.