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Background: Single-cell RNA sequencing (scRNA-seq) technology has contributed significantly to diverse research areas in biology, from cancer to development. Since scRNA-seq data is high-dimensional, a common strategy is to learn low-dimensional latent representations better to understand overall structure in the data. In this work, we build upon scVI, a powerful deep generative model which can learn biologically meaningful latent representations, but which has limited explicit control of batch effects. Rather than prioritizing batch effect removal over conservation of biological variation, or vice versa, our goal is to provide a bird's eye view of the trade-offs between these two conflicting objectives. Specifically, using the well established concept of Pareto front from economics and engineering, we seek to learn the entire trade-off curve between conservation of biological variation and removal of batch effects. Results: A multi-objective optimisation technique known as Pareto multi-task learning (Pareto MTL) is used to obtain the Pareto front between conservation of biological variation and batch effect removal. Our results indicate Pareto MTL can obtain a better Pareto front than the naive scalarization approach typically encountered in the literature. In addition, we propose to measure batch effect by applying a neural-network based estimator called Mutual Information Neural Estimation (MINE) and show benefits over the more standard maximum mean discrepancy measure. Conclusion: The Pareto front between conservation of biological variation and batch effect removal is a valuable tool for researchers in computational biology. Our results demonstrate the efficacy of applying Pareto MTL to estimate the Pareto front in conjunction with applying MINE to measure the batch effect. [ABSTRACT FROM AUTHOR]

Motivation: Single-cell RNA sequencing (scRNA-seq) is increasingly used to study gene expression at the level of individual cells. However, preparing raw sequence data for further analysis is not a straightforward process. Biases, artifacts and other sources of unwanted variation are present in the data, requiring substantial time and effort to be spent on pre-processing, quality control (QC) and normalization. Results: We have developed the R/Bioconductor package scater to facilitate rigorous pre-processing, quality control, normalization and visualization of scRNA-seq data. The package provides a convenient, flexible workflow to process raw sequencing reads into a high-quality expression dataset ready for downstream analysis. scater provides a rich suite of plotting tools for single-cell data and a flexible data structure that is compatible with existing tools and can be used as infrastructure for future software development. [ABSTRACT FROM AUTHOR]

Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. [ABSTRACT FROM PUBLISHER]

Methods to detect malignant lesions from screening mammograms are usually trained with fully annotated datasets, where images are labelled with the localisation and classification of cancerous lesions. However, real-world screening mammogram datasets commonly have a subset that is fully annotated and another subset that is weakly annotated with just the global classification (i.e., without lesion localisation). Given the large size of such datasets, researchers usually face a dilemma with the weakly annotated subset: to not use it or to fully annotate it. The first option will reduce detection accuracy because it does not use the whole dataset, and the second option is too expensive given that the annotation needs to be done by expert radiologists. In this paper, we propose a middle-ground solution for the dilemma, which is to formulate the training as a weakly- and semi-supervised learning problem that we refer to as malignant breast lesion detection with incomplete annotations. To address this problem, our new method comprises two stages, namely: (1) pre-training a multi-view mammogram classifier with weak supervision from the whole dataset, and (2) extending the trained classifier to become a multi-view detector that is trained with semi-supervised student–teacher learning, where the training set contains fully and weakly-annotated mammograms. We provide extensive detection results on two real-world screening mammogram datasets containing incomplete annotations and show that our proposed approach achieves state-of-the-art results in the detection of malignant breast lesions with incomplete annotations. • We explore a new experimental setting and method for the detection of malignant breast lesions using large-scale real-world screening mammogram datasets that have both weak and fully annotations. • We proposed a new two-stage training method to jointly process GRAD-CAM and pseudo-label predictions in a student–teacher and cross-view manner. • We also propose innovations to the student–teacher framework to avoid the misalignment between the student and teacher's parameter space. • We provide extensive experiments on two real-world breast cancer screening mammogram datasets containing incomplete annotations. [ABSTRACT FROM AUTHOR]

We developed a simple and reliable method for the isolation of haploid nuclei from fresh and frozen testes. The described protocol uses readily available reagents in combination with flow cytometry to separate haploid and diploid nuclei. The protocol can be completed within 1 hour and the resulting individual haploid nuclei have intact morphology. The isolated nuclei are suitable for library preparation for high-throughput DNA and RNA sequencing using bulk or single nuclei. The protocol was optimised with mouse testes and we anticipate that it can be applied for the isolation of mature sperm from other mammals including humans. [ABSTRACT FROM AUTHOR]

Next generation sequencing technology provides a powerful tool for measuring gene expression (mRNA) levels in the form of RNA-sequence data. Method development for identifying differentially expressed (DE) genes from RNA-seq data, which frequently includes many low-count integers and can exhibit severe overdispersion relative to Poisson or binomial distributions, is a popular area of ongoing research. Here we present quasi-likelihood methods with shrunken dispersion estimates based on an adaptation of Smyth's (2004) approach to estimating gene-specific error variances for microarray data. Our suggested methods are computationally simple, analogous to ANOVA and compare favorably versus competing methods in detecting DE genes and estimating false discovery rates across a variety of simulations based on real data. [ABSTRACT FROM AUTHOR]

Single-cell RNA sequencing (scRNA-seq) is the leading technique for characterizing the transcriptomes of individual cells in a sample. The latest protocols are scalable to thousands of cells and are being used to compile cell atlases of tissues, organs and organisms. However, the protocols differ substantially with respect to their RNA capture efficiency, bias, scale and costs, and their relative advantages for different applications are unclear. In the present study, we generated benchmark datasets to systematically evaluate protocols in terms of their power to comprehensively describe cell types and states. We performed a multicenter study comparing 13 commonly used scRNA-seq and single-nucleus RNA-seq protocols applied to a heterogeneous reference sample resource. Comparative analysis revealed marked differences in protocol performance. The protocols differed in library complexity and their ability to detect cell-type markers, impacting their predictive value and suitability for integration into reference cell atlases. These results provide guidance both for individual researchers and for consortium projects such as the Human Cell Atlas. A multicenter study compares 13 commonly used single-cell RNA-seq protocols. [ABSTRACT FROM AUTHOR]

Summary 1. Aliskiren is a renin inhibitor with an IC50 of 0.6 nmol/L for human renin, 4.5 nmol/L for mouse renin and 80 nmol/L for rat renin. 2. In the present study, we compared the effects of aliskiren (10 mg/kg per day), the angiotensin-converting enzyme inhibitor perindopril (0.2 mg/kg per day) and their combination on angiotensin and bradykinin peptides in female heterozygous (mRen-2)27 rats, transgenic for the mouse renin gene. 3. All three treatments produced similar reductions in systolic blood pressure, heart weight and plasma aldosterone levels and reduced angiotensin II levels in lung, but only perindopril and the combination reduced angiotensin II levels in kidney of (mRen-2)27 rats. In contrast, aliskiren and the combination, but not perindopril alone, increased cardiac bradykinin levels. Aliskiren increased immunostaining for tissue kallikrein in the heart and reduced cardiac fibrosis. 4. We investigated the mechanism underlying the increase in bradykinin levels following aliskiren treatment in Sprague-Dawley rats, in which aliskiren has a lower potency for renin inhibition. Aliskiren (10 mg/kg per day) reduced renal angiotensin levels within 24 h, but treatment for > 24 h was required to increase cardiac bradykinin levels. Moreover, 3 mg/kg per day aliskiren increased cardiac bradykinin levels, but did not reduce renal angiotensin levels. Aliskiren did not potentiate the hypotensive effects of bradykinin; however, it increased tissue kallikrein, but not plasma kallikrein, mRNA levels in the heart. 5. These data demonstrate that the aliskiren-induced increase in cardiac bradykinin levels is independent of renin inhibition and changes in bradykinin metabolism, but is associated with increased tissue kallikrein gene expression. [ABSTRACT FROM AUTHOR]