Systematic replication enables normalization of high-throughput imaging assays.

MOTIVATION: High-throughput fluorescent microscopy is a popular class of techniques for studying tissues and cells through automated imaging and feature extraction of hundreds to thousands of samples. Like other high-throughput assays, these approaches can suffer from unwanted noise and technical artifacts that obscure the biological signal. In this work, we consider how an experimental design incorporating multiple levels of replication enables the removal of technical artifacts from such image-based platforms. RESULTS: We develop a general approach to remove technical artifacts from high-throughput image data that leverages an experimental design with multiple levels of replication. To illustrate the methods, we consider microenvironment microarrays (MEMAs), a high-throughput platform designed to study cellular responses to microenvironmental perturbations. In application to MEMAs, our approach removes unwanted spatial artifacts and thereby enhances the biological signal. This approach has broad applicability to diverse biological assays. AVAILABILITY AND IMPLEMENTATION: Raw data are on synapse (syn2862345), analysis code is on github: gjhunt/mema_norm, a reproducible Docker image is available on dockerhub: gjhunt/mema_norm. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Automatic Transformation and Integration to Improve Visualization and Discovery of Latent Effects in Imaging Data.

Proper data transformation is an essential part of analysis. Choosing appropriate transformations for variables can enhance visualization, improve efficacy of analytical methods, and increase data interpretability. However determining appropriate transformations of variables from high-content imaging data poses new challenges. Imaging data produces hundreds of covariates from each of thousands of images in a corpus. Each of these covariates will have a different distribution and need a potentially different transformation. As such imaging data produces hundreds of covariates, determining an appropriate transformation for each of them is infeasible by hand. In this paper we explore simple, robust, and automatic transformations of high-content image data. A central application of our work is to microenvironment microarray bio-imaging data from the NIH LINCS program. We show that our robust transformations enhance visualization and improve the discovery of substantively relevant latent effects. These transformations enhance analysis of image features individually and also improve data integration approaches when combining together multiple features. We anticipate that the advantages of this work will likely also be realized in the analysis of data from other high-content and highly-multiplexed technologies like Cell Painting or Cyclic Immunofluorescence. Software and further analysis can be found at gjhunt.github.io/rr.

dtangle: accurate and robust cell type deconvolution.

MOTIVATION: Cell type composition of tissues is important in many biological processes. To help understand cell type composition using gene expression data, methods of estimating (deconvolving) cell type proportions have been developed. Such estimates are often used to adjust for confounding effects of cell type in differential expression analysis (DEA). RESULTS: We propose dtangle, a new cell type deconvolution method. dtangle works on a range of DNA microarray and bulk RNA-seq platforms. It estimates cell type proportions using publicly available, often cross-platform, reference data. We evaluate dtangle on 11 benchmark datasets showing that dtangle is competitive with published deconvolution methods, is robust to outliers and selection of tuning parameters, and is fast. As a case study, we investigate the human immune response to Lyme disease. dtangle's estimates reveal a temporal trend consistent with previous findings and are important covariates for DEA across disease status. AVAILABILITY AND IMPLEMENTATION: dtangle is on CRAN (cran.r-project.org/package=dtangle) or github (dtangle.github.io). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Saturn's magnetic field revealed by the Cassini Grand Finale.

During 2017, the Cassini fluxgate magnetometer made in situ measurements of Saturn's magnetic field at distances ~2550 ± 1290 kilometers above the 1-bar surface during 22 highly inclined Grand Finale orbits. These observations refine the extreme axisymmetry of Saturn's internal magnetic field and show displacement of the magnetic equator northward from the planet's physical equator. Persistent small-scale magnetic structures, corresponding to high-degree (>3) axisymmetric magnetic moments, were observed. This suggests secondary shallow dynamo action in the semiconducting region of Saturn's interior. Some high-degree magnetic moments could arise from strong high-latitude concentrations of magnetic flux within the planet's deep dynamo. A strong field-aligned current (FAC) system is located between Saturn and the inner edge of its D-ring, with strength comparable to the high-latitude auroral FACs.

Improved synthesis of O-linked, and first synthesis of S- linked, carbohydrate functionalised N-carboxyanhydrides (glycoNCAs).

An improved method for the synthesis of glycosylated N-carboxyanhydrides, which are monomers for glycopeptide synthesis, is presented.