Biology-inspired data-driven quality control for scientific discovery in single-cell transcriptomics

Abstract Background Quality control (QC) of cells, a critical first step in single-cell RNA sequencing data analysis, has largely relied on arbitrarily fixed data-agnostic thresholds applied to QC metrics such as gene complexity and fraction of reads mapping to mitochondrial genes. The few existing data-driven approaches perform QC at the level of samples or studies without accounting for biological variation. Results We first demonstrate that QC metrics vary with both tissue and cell types across technologies, study conditions, and species. We then propose data-driven QC (ddqc), an unsupervised adaptive QC framework to perform flexible and data-driven QC at the level of cell types while retaining critical biological insights and improved power for downstream analysis. ddqc applies an adaptive threshold based on the median absolute deviation on four QC metrics (gene and UMI complexity, fraction of reads mapping to mitochondrial and ribosomal genes). ddqc retains over a third more cells when compared to conventional data-agnostic QC filters. Finally, we show that ddqc recovers biologically meaningful trends in gradation of gene complexity among cell types that can help answer questions of biological interest such as which cell types express the least and most number of transcripts overall, and ribosomal transcripts specifically. Conclusions ddqc retains cell types such as metabolically active parenchymal cells and specialized cells such as neutrophils which are often lost by conventional QC. Taken together, our work proposes a revised paradigm to quality filtering best practices—iterative QC, providing a data-driven QC framework compatible with observed biological diversity

Prioritisation of structural variant calls in cancer genomes

Sensitivity of short read DNA-sequencing for gene fusion detection is improving, but is hampered by the significant amount of noise composed of uninteresting or false positive hits in the data. In this paper we describe a tiered prioritisation approach to extract high impact gene fusion events from existing structural variant calls. Using cell line and patient DNA sequence data we improve the annotation and interpretation of structural variant calls to best highlight likely cancer driving fusions. We also considerably improve on the automated visualisation of the high impact structural variants to highlight the effects of the variants on the resulting transcripts. The resulting framework greatly improves on readily detecting clinically actionable structural variants

Hydrophobization of Reduced Graphene Oxide Aerogel Using Soy Wax to Improve Sorption Properties

A special technique has been developed for producing a composite aerogel which consists of graphene oxide and soy wax (GO/wax). The reduction of graphene oxide was carried out by the stepwise heating of this aerogel to 250 °C. The aerogel obtained in the process of the stepwise thermal treatment of rGO/wax was studied by IR and Raman spectroscopy, scanning electron microscopy, and thermogravimetry. The heat treatment led to an increase in the wax fraction accompanied by an increase in the contact angle of the rGO/wax aerogel surface from 136.2 °C to 142.4 °C. The SEM analysis has shown that the spatial structure of the aerogel was formed by sheets of graphene oxide, while the wax formed rather large (200–1000 nm) clumps in the folds of graphene oxide sheets and small (several nm) deposits on the flat surface of the sheets. The sorption properties of the rGO/wax aerogel were studied with respect to eight solvent, oil, and petroleum products, and it was found that dichlorobenzene (85.8 g/g) and hexane (41.9 g/g) had the maximum and minimum sorption capacities, respectively. In the case of oil and petroleum products, the indicators were in the range of 52–63 g/g. The rGO/wax aerogel was found to be highly resistant to sorption–desorption cycles. The cyclic tests also revealed a swelling effect that occurred differently for different parts of the aerogel

Distributed under Creative Commons CC-BY 4.0 Prioritisation of structural variant calls in cancer genomes

ABSTRACT Sensitivity of short read DNA-sequencing for gene fusion detection is improving, but is hampered by the significant amount of noise composed of uninteresting or false positive hits in the data. In this paper we describe a tiered prioritisation approach to extract high impact gene fusion events from existing structural variant calls. Using cell line and patient DNA sequence data we improve the annotation and interpretation of structural variant calls to best highlight likely cancer driving fusions. We also considerably improve on the automated visualisation of the high impact structural variants to highlight the effects of the variants on the resulting transcripts. The resulting framework greatly improves on readily detecting clinically actionable structural variants