Josephson junctions containing two ferromagnetic layers are being considered for use in cryogenic memory. Our group recently demonstrated that the ground-state phase difference across such a junction with carefully chosen layer thicknesses could be controllably toggled between zero and π by switching the relative magnetization directions of the two layers between the antiparallel and parallel configurations. However, several technological issues must be addressed before those junctions can be used in a large-scale memory. Many of these issues can be more easily studied in single junctions, rather than in the Superconducting QUantum Interference Device (SQUID) used for the phase-sensitive measurements. In this work, we report a comprehensive study of spin-valve junctions containing a Ni layer with a fixed thickness of 2.0 nm, and a NiFe layer of thickness varying between 1.1 and 1.8 nm in steps of 0.1 nm. We extract the field shift of the Fraunhofer patterns and the critical currents of the junctions in the parallel and antiparallel magnetic states, as well as the switching fields of both magnetic layers. We also report a partial study of similar junctions containing a slightly thinner Ni layer of 1.6 nm and the same range of NiFe thicknesses. These results represent the first step toward mapping out a "phase diagram" for phase-controllable spin-valve Josephson junctions as a function of the two magnetic layer thicknesses.

Motivation: Analysis of singe cell RNA sequencing (scRNA-seq) typically consists of different steps including quality control, batch correction, clustering, cell identification and characterization, and visualization. The amount of scRNA-seq data is growing extremely fast, and novel algorithmic approaches improving these steps are key to extract more biological information. Here, we introduce: (i) two methods for automatic cell type identification (i.e. without expert curator) based on a voting algorithm and a Hopfield classifier, (ii) a method for cell anomaly quantification based on isolation forest, and (iii) a tool for the visualization of cell phenotypic landscapes based on Hopfield energy-like functions. These new approaches are integrated in a software platform that includes many other state-of-the-art methodologies and provides a self-contained toolkit for scRNA-seq analysis. Results: We present a suite of software elements for the analysis of scRNA-seq data. This Python-based open source software, Digital Cell Sorter (DCS), consists in an extensive toolkit of methods for scRNA-seq analysis. We illustrate the capability of the software using data from large datasets of peripheral blood mononuclear cells (PBMC), as well as plasma cells of bone marrow samples from healthy donors and multiple myeloma patients. We test the novel algorithms by evaluating their ability to deconvolve cell mixtures and detect small numbers of anomalous cells in PBMC data. Availability: The DCS toolkit is available for download and installation through the Python Package Index (PyPI). The software can be deployed using the Python import function following installation. Source code is also available for download on Zenodo: doi.org/10.5281/zenodo.2533377.

Motivation Analysis of singe cell RNA sequencing (scRNA-seq) typically consists of different steps including quality control, batch correction, clustering, cell identification and characterization, and visualization. The amount of scRNA-seq data is growing extremely fast, and novel algorithmic approaches improving these steps are key to extract more biological information. Here, we introduce: (i) two methods for automatic cell type identification (i.e., without expert curator) based on a voting algorithm and a Hopfield classifier, (ii) a method for cell anomaly quantification based on isolation forest, and (iii) a tool for the visualization of cell phenotypic landscapes based on Hopfield energy-like functions. These new approaches are integrated in a software platform that includes many other state-of-the-art methodologies and provides a self-contained toolkit for scRNA-seq analysis. Results We present a suite of software elements for the analysis of scRNA-seq data. This Python-based open source software, Digital Cell Sorter (DCS), consists in an extensive toolkit of methods for scRNA-seq analysis. We illustrate the capability of the software using data from large datasets of peripheral blood mononuclear cells (PBMC), as well as plasma cells of bone marrow samples from healthy donors and multiple myeloma patients. We test the novel algorithms by evaluating their ability to deconvolve cell mixtures and detect small numbers of anomalous cells in PBMC data. Availability The DCS toolkit is available for download and installation through the Python Package Index (PyPI). The software can be deployed using the Python import function following installation. Source code is also available for download on Zenodo: DOI 10.5281/zenodo.2533377. Supplementary information Supplemental Materials are available at PeerJ online.

Deconvolution methods infer levels of immune and stromal infiltration from bulk expression of tumor samples. These methods allow projection of characteristics of the tumor microenvironment, known to affect patient outcome and therapeutic response, onto the millions of bulk transcriptional profiles in public databases, many focused on uniquely valuable and clinically-annotated cohorts. Despite the wide development of such methods, a standardized dataset with ground truth to evaluate their performance has been lacking. We generated and sequenced in vitro and in silico admixtures of tumor, immune, and stromal cells and used them as ground truth in a community-wide DREAM Challenge that provided an objective, unbiased assessment of six widely-used published deconvolution methods and of 22 new analytical approaches developed by international teams. Our results demonstrate that existing methods predict many cell types well, while team-contributed methods highlight the potential to resolve functional states of T cells that were either not covered by published reference signatures or estimated poorly by some published methods. Our assessment and the open-source implementations of top-performing methods will allow researchers to apply the deconvolution approach most appropriate to querying their cell type of interest. Further, our publicly-available admixed and purified expression profiles will be a valuable resource to those developing deconvolution methods, including in non-malignant settings involving immune cells.

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