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 .

CellexalVR: A virtual reality platform to visualize and analyze single-cell omics data.

Single-cell RNAseq is a routinely used method to explore heterogeneity within cell populations. Data from these experiments are often visualized using dimension reduction methods such as UMAP and tSNE, where each cell is projected in two or three dimensional space. Three-dimensional projections can be more informative for larger and complex datasets because they are less prone to merging and flattening similar cell-types/clusters together. However, visualizing and cross-comparing 3D projections using current software on conventional flat-screen displays is far from optimal as they are still essentially 2D, and lack meaningful interaction between the user and the data. Here we present CellexalVR (www.cellexalvr.med.lu.se), a feature-rich, fully interactive virtual reality environment for the visualization and analysis of single-cell experiments that allows researchers to intuitively and collaboratively gain an understanding of their data.

Nature as the "natural" goal for water management: a conversation.

The goals for water-quality and ecosystem integrity are often defined relative to "natural" reference conditions in many water-management systems, including the European Union Water Framework Directive. This paper examines the difficulties created for water management by using "natural" as the goal. These difficulties are articulated from different perspectives in an informal (fictional) conversation that takes place after a workshop on reference conditions in water-resources management. The difficulties include defining the natural state and modeling how a system might be progressed toward the natural, as well as the feasibility and desirability of restoring a natural state. The paper also considers the appropriateness for developing countries to adopt the use of natural as the goal for water management. We conclude that failure to critically examine the complexities of having "natural" as the goal will compromise the ability to manage the issues that arise in real basins by not making the ambiguities associated with this "natural" goal explicit. This is unfortunate both for the western world that has embraced this model of "natural as the goal" and for the developing world in so far as they are encouraged to adopt this model.

The Swedish Regional Climate Modelling Programme, SWECLIM: a review.

The Swedish Regional Climate Modelling Programme, SWECLIM, was a 6.5-year national research network for regional climate modeling, regional climate change projections and hydrological impact assessment and information to a wide range of stakeholders. Most of the program activities focussed on the regional climate system of Northern Europe. This led to the establishment of an advanced, coupled atmosphere-ocean-hydrology regional climate model system, a suite of regional climate change projections and progress on relevant data and process studies. These were, in turn, used for information and educational purposes, as a starting point for impact analyses on different societal sectors and provided contributions also to international climate research.