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.
View Article and Find Full Text PDFSingle-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.
View Article and Find Full Text PDFThe 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.
View Article and Find Full Text PDFThe 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.
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