Scarf enables a highly memory-efficient analysis of large-scale single-cell genomics data.

As the scale of single-cell genomics experiments grows into the millions, the computational requirements to process this data are beyond the reach of many. Herein we present Scarf, a modularly designed Python package that seamlessly interoperates with other single-cell toolkits and allows for memory-efficient single-cell analysis of millions of cells on a laptop or low-cost devices like single-board computers. We demonstrate Scarf's memory and compute-time efficiency by applying it to the largest existing single-cell RNA-Seq and ATAC-Seq datasets. Scarf wraps memory-efficient implementations of a graph-based t-stochastic neighbour embedding and hierarchical clustering algorithm. Moreover, Scarf performs accurate reference-anchored mapping of datasets while maintaining memory efficiency. By implementing a subsampling algorithm, Scarf additionally has the capacity to generate representative sampling of cells from a given dataset wherein rare cell populations and lineage differentiation trajectories are conserved. Together, Scarf provides a framework wherein any researcher can perform advanced processing, subsampling, reanalysis, and integration of atlas-scale datasets on standard laptop computers. Scarf is available on Github: https://github.com/parashardhapola/scarf .

Phenotypic similarity for rare disease: Ciliopathy diagnoses and subtyping.

Rare diseases are often hard and long to be diagnosed precisely, and most of them lack approved treatment. For some complex rare diseases, precision medicine approach is further required to stratify patients into homogeneous subgroups based on the clinical, biological or molecular features. In such situation, deep phenotyping of these patients and comparing their profiles based on subjacent similarities are thus essential to help fast and precise diagnoses and better understanding of pathophysiological processes in order to develop therapeutic solutions. In this article, we developed a new pipeline of using deep phenotyping to define patient similarity and applied it to ciliopathies, a group of rare and severe diseases caused by ciliary dysfunction. As a French national reference center for rare and undiagnosed diseases, the Necker-Enfants Malades Hospital (Necker Children's Hospital) hosts the Imagine Institute, a research institute focusing on genetic diseases. The clinical data warehouse contains on one hand EHR data, and on the other hand, clinical research data. The similarity metrics were computed on both data sources, and were evaluated with two tasks: diagnoses with EHRs and subtyping with ciliopathy specific research data. We obtained a precision of 0.767 in the top 30 most similar patients with diagnosed ciliopathies. Subtyping ciliopathy patients with phenotypic similarity showed concordances with expert knowledge. Similarity metrics applied to rare disease offer new perspectives in a translational context that may help to recruit patients for research, reduce the length of the diagnostic journey, and better understand the mechanisms of the disease.