We evaluate deconvolution methods, which infer levels of immune infiltration from bulk expression of tumor samples, through a community-wide DREAM Challenge. We assess six published and 22 community-contributed methods using in vitro and in silico transcriptional profiles of admixed cancer and healthy immune cells. Several published methods predict most cell types well, though they either were not trained to evaluate all functional CD8+ T cell states or do so with low accuracy. Several community-contributed methods address this gap, including a deep learning-based approach, whose strong performance establishes the applicability of this paradigm to deconvolution. Despite being developed largely using immune cells from healthy tissues, deconvolution methods predict levels of tumor-derived immune cells well. Our admixed and purified transcriptional profiles will be a valuable resource for developing deconvolution methods, including in response to common challenges we observe across methods, such as sensitive identification of functional CD4+ T cell states. Deconvolution methods infer levels of immune infiltration from bulk expression of tumour samples. Here, authors assess 6 published and 22 community-contributed methods via a DREAM Challenge using in vitro and in silico transcriptional profiles of admixed cancer and healthy immune cells.

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.

This study evaluates predictions central to the extended parallel process model (EPPM) in the context of dense breast notifications. Many EPPM propositions have gone untested and competing predictions to the model have been evaluated to an even lesser extent. Also left as an open question is exactly how perceived threat and efficacy constructs should be treated in health communication research. Using experimental data collected from women likely to receive dense breast notification letters (i.e., aged 40 to 50 years) in states with and without dense breast notification legislation, this study explicitly tests EPPM predictions regarding danger control responses. These data were largely unsupportive of the EPPM's predictions and instead finds that negative affect is more of a direct predictor of intention than expected. These data also provide evidence supporting the separate treatment of the perceived severity, susceptibility, self-efficacy, and response efficacy variables, contrary to convention in EPPM research. Implications for breast density research and EPPM theorizing are discussed in light of these findings.