Characterizing the impacts of dataset imbalance on single-cell data integration.

Computational methods for integrating single-cell transcriptomic data from multiple samples and conditions do not generally account for imbalances in the cell types measured in different datasets. In this study, we examined how differences in the cell types present, the number of cells per cell type and the cell type proportions across samples affect downstream analyses after integration. The Iniquitate pipeline assesses the robustness of integration results after perturbing the degree of imbalance between datasets.

The impacts of active and self-supervised learning on efficient annotation of single-cell expression data.

A crucial step in the analysis of single-cell data is annotating cells to cell types and states. While a myriad of approaches has been proposed, manual labeling of cells to create training datasets remains tedious and time-consuming. In the field of machine learning, active and self-supervised learning methods have been proposed to improve the performance of a classifier while reducing both annotation time and label budget.

Automated assignment of cell identity from single-cell multiplexed imaging and proteomic data.

A major challenge in the analysis of highly multiplexed imaging data is the assignment of cells to a priori known cell types. Existing approaches typically solve this by clustering cells followed by manual annotation. However, these often require several subjective choices and cannot explicitly assign cells to an uncharacterized type.

MAX Functions as a Tumor Suppressor and Rewires Metabolism in Small Cell Lung Cancer.

Small cell lung cancer (SCLC) is a highly aggressive and lethal neoplasm. To identify candidate tumor suppressors we applied CRISPR/Cas9 gene inactivation screens to a cellular model of early-stage SCLC. Among the top hits was MAX, the obligate heterodimerization partner for MYC family proteins that is mutated in human SCLC.