Sources of variation in cell-type RNA-Seq profiles

Cell-type specific gene expression profiles are needed for many computational methods operating on bulk RNA-Seq samples, such as deconvolution of cell-type fractions and digital cytometry. However, the gene expression profile of a cell type can vary substantially due to both technical factors and biological differences in cell state and surroundings, reducing the efficacy of such methods. Here, we investigated which factors contribute most to this variation. We evaluated different normalization methods, quantified the variance explained by different factors, evaluated the effect on deconvolution of cell type fractions, and examined the differences between UMI-based single-cell RNA-Seq and bulk RNA-Seq. We investigated a collection of publicly available bulk and single-cell RNA-Seq datasets containing B and T cells, and found that the technical variation across laboratories is substantial, even for genes specifically selected for deconvolution, and this variation has a confounding effect on deconvolution. Tissue of origin is also a substantial factor, highlighting the challenge of using cell type profiles derived from blood with mixtures from other tissues. We also show that much of the differences between UMI-based single-cell and bulk RNA-Seq methods can be explained by the number of read duplicates per mRNA molecule in the single-cell sample. Our work shows the importance of either matching or correcting for technical factors when creating cell-type specific gene expression profiles that are to be used together with bulk samples

Optical microprism cavities based on dislocation-free GaN

Three-dimensional growth of nanostructures can be used to reduce the threading dislocation density that degrades III-nitride laser performance. Here, nanowire-based hexagonal GaN microprisms with flat top and bottom c-facets are embedded between two dielectric distributed Bragg reflectors to create dislocation-free vertical optical cavities. The cavities are electron beam pumped, and the quality (Q) factor is deduced from the cavity-filtered yellow luminescence. The Q factor is similar to 500 for a 1000nm wide prism cavity and only similar to 60 for a 600nm wide cavity, showing the strong decrease in Q factor when diffraction losses become dominant. Measured Q factors are in good agreement with those obtained from quasi-3D finite element frequency-domain method and 3D beam propagation method simulations. Simulations further predict that a prism cavity with a 1000nm width will have a Q factor of around 2000 in the blue spectral regime, which would be the target regime for real devices. These results demonstrate the potential of GaN prisms as a scalable platform for realizing small footprint lasers with low threshold currents

Generation and analysis of context-specific genome-scale metabolic models derived from single-cell RNA-Seq data

Single-cell RNA sequencing combined with genome-scale metabolic models (GEMs) has the potential to unravel the differences in metabolism across both cell types and cell states but requires new computational methods. Here, we present a method for generating cell-type-specific genome-scale models from clusters of single-cell RNA-Seq profiles. Specifically, we developed a method to estimate the minimum number of cells required to pool to obtain stable models, a bootstrapping strategy for estimating statistical inference, and a faster version of the task-driven integrative network inference for tissues\ua0algorithm for generating context-specific GEMs. In addition, we evaluated the effect of different RNA-Seq normalization methods on model topology and differences in models generated from single-cell and bulk RNA-Seq data. We applied our methods on data from mouse cortex neurons and cells from the tumor microenvironment of lung cancer and in both cases found that almost every cell subtype had a unique metabolic profile. In addition, our approach was able to detect cancer-associated metabolic differences between cancer cells and healthy cells, showcasing its utility. We also contextualized models from 202 single-cell clusters across 19 human organs using data from Human Protein Atlas and made these available in the web portal Metabolic Atlas, thereby providing a valuable resource to the scientific community. With the ever-increasing availability of single-cell RNA-Seq datasets and continuously improved GEMs, their combination holds promise to become an important approach in the study of human metabolism

DSAVE: Detection of misclassified cells in single-cell RNA-Seq data

Single-cell RNA sequencing has become a valuable tool for investigating cell types in complex tissues, where clustering of cells enables the identification and comparison of cell populations. Although many studies have sought to develop and compare different clustering approaches, a deeper investigation into the properties of the resulting populations is lacking. Specifically, the presence of misclassified cells can influence downstream analyses, highlighting the need to assess subpopulation purity and to detect such cells. We developed DSAVE (Down-SAmpling based Variation Estimation), a method to evaluate the purity of single-cell transcriptome clusters and to identify misclassified cells. The method utilizes down-sampling to eliminate differences in sampling noise and uses a log-likelihood based metric to help identify misclassified cells. In addition, DSAVE estimates the number of cells needed in a population to achieve a stable average gene expression profile within a certain gene expression range. We show that DSAVE can be used to find potentially misclassified cells that are not detectable by similar tools and reveal the cause of their divergence from the other cells, such as differing cell state or cell type. With the growing use of single-cell RNA-seq, we foresee that DSAVE will be an increasingly useful tool for comparing and purifying subpopulations in single-cell RNA-Seq datasets

A comprehensive comparison of the Sun to other stars: searching for self-selection effects

If the origin of life and the evolution of observers on a planet is favoured by atypical properties of a planet's host star, we would expect our Sun to be atypical with respect to such properties. The Sun has been described by previous studies as both typical and atypical. In an effort to reduce this ambiguity and quantify how typical the Sun is, we identify eleven maximally-independent properties that have plausible correlations with habitability, and that have been observed by, or can be derived from, sufficiently large, currently available and representative stellar surveys. By comparing solar values for the eleven properties, to the resultant stellar distributions, we make the most comprehensive comparison of the Sun to other stars. The two most atypical properties of the Sun are its mass and orbit. The Sun is more massive than 95 -/+ 2% of nearby stars and its orbit around the Galaxy is less eccentric than 93 +/- 1% of FGK stars within 40 parsecs. Despite these apparently atypical properties, a chi^2 -analysis of the Sun's values for eleven properties, taken together, yields a solar chi^2 = 8.39 +/- 0.96. If a star is chosen at random, the probability that it will have a lower value (be more typical) than the Sun, with respect to the eleven properties analysed here, is only 29 +/- 11%. These values quantify, and are consistent with, the idea that the Sun is a typical star. If we have sampled all reasonable properties associated with habitability, our result suggests that there are no special requirements for a star to host a planet with life.Comment: Published in the Astrophysical Journal, 684:691-706, 2008 September 1. This version corrects two small errors the press could not correct before publication - the errors are addressed in an erratum ApJ will release on Dec 1, 200

Analysis of spatial variability in hyperspectral imagery of the uterine cervix in vivo

The use of fluorescence and reflectance spectroscopy in the analysis of cervical histopathology is a growing field of research. The majority of this research is performed with point-like probes. Typically, clinicians select probe sites visually, collecting a handful of spectral samples. An exception to this methodology is the Hyperspectral Diagnostic Imaging (HSDI®) instrument developed by Science and Technology International. This non-invasive device collects contiguous hyperspectral images across the entire cervical portio. The high spatial and spectral resolution of the HSDI instruments make them uniquely well suited for addressing the issues of coupled spatial and spectral variability of tissues in vivo. Analysis of HSDI data indicates that tissue spectra vary from point to point, even within histopathologically homogeneous regions. This spectral variability exhibits both random and patterned components, implying that point monitoring may be susceptible to significant sources of noise and clutter inherent in the tissue. We have analyzed HSDI images from clinical CIN (cervical intraepithelial neoplasia) patients to quantify the spatial variability of fluorescence and reflectance spectra. This analysis shows the spatial structure of images to be fractal in nature, in both intensity and spectrum. These fractal tissue textures will limit the performance of any point-monitoring technology

Visualisation in imaging mass spectrometry using the minimum noise fraction transform

Extent: 6p.Background: Imaging Mass Spectrometry (IMS) provides a means to measure the spatial distribution of biochemical features on the surface of a sectioned tissue sample. IMS datasets are typically huge and visualisation and subsequent analysis can be challenging. Principal component analysis (PCA) is one popular data reduction technique that has been used and we propose another; the minimum noise fraction (MNF) transform which is popular in remote sensing. Findings: The MNF transform is able to extract spatially coherent information from IMS data. The MNF transform is implemented through an R-package which is available together with example data from http://staff.scm.uws.edu.au/∼glenn/#Software. Conclusions: In our example, the MNF transform was able to find additional images of interest. The extracted information forms a useful basis for subsequent analyses.Glenn Stone, David Clifford, Johan OR Gustafsson, Shaun R McColl and Peter Hoffman