Adrin Jalali

TL;DR: Brinkman et al. as discussed by the authors developed a computational approach that automatically reveals all possible cell subsets from tens of thousands of subsets, those that correlate strongly with clinical outcome are selected and grouped.

TL;DR: RchyOptimyx is a computational tool that constructs cellular hierarchies by combining automated gating with dynamic programming and graph theory to provide the best gating strategies to identify a target population to a desired level of purity or correlation with a clinical outcome, using the simplest possible marker panels.

TL;DR: A significantly improved version of the flowType and RchyOptimyx BioConductor-based pipeline is presented that is both 14 times faster and can accommodate multiple levels of biomarker expression for up to 96 markers, positioned to be an integral part of data analysis for high-throughput experiments on high-dimensional single-cell assay platforms.

TL;DR: By providing automated labelling of cell populations based on their immunophenotype, flowCL allows for unambiguous and reproducible identification of standardized cell types.

TL;DR: The key idea of the approach is to compare dependencies between inputs in training and test data and to increase the cost of differently behaving features in the elastic net regularization term to encourage the model to assign a higher importance to features that are robust and behave similarly across domains.