Motivation Genetic intra-tumor heterogeneity (ITH) characterizes the differences in genomic variations between tumor clones, and accurately unmasking ITH is important for personalized cancer therapy. Single-cell DNA sequencing (scDNA-seq) now emerges as a powerful means for deciphering underlying ITH based on point mutations of single cells. However, detecting tumor clones from single-cell mutation data remains challenging due to the error-prone and discrete nature of the data. Results We introduce bmVAE, a bioinformatics tool for learning low-dimensional latent representation of single cell based on a variational autoencoder and then clustering cells into subpopulations in the latent space. bmVAE takes single-cell binary mutation data as inputs, and outputs inferred cell subpopulations as well as their genotypes. To achieve this, the bmVAE framework is designed to consist of three modules including dimensionality reduction, cell clustering and genotype estimation. We assess the method on various synthetic datasets where different factors including false negative rate, data size and data heterogeneity are considered in simulation, and further demonstrate its effectiveness on two real datasets. The results suggest bmVAE is highly effective in reasoning ITH, and performs competitive to existing methods. Availability bmVAE is freely available at https://github.com/zhyu-lab/bmvae. Supplementary information Supplementary data are available at Bioinformatics online.
Some wild relatives have higher grain zinc (Zn) concentrations than common wheat and were widely used for wheat biofortification. However, little is known about the mechanisms controlling Zn accumulation in wheat and its wild relatives. This study is aimed to shed light on the causes of different grain Zn concentrations in wheat at a physiological level. Six genotypes, including one Triticale, one Triticum monococcum, one Triticum petropavlovskyi, and three Triticum aestivum, with different grain Zn concentrations, were evaluated in this study. Zn concentrations in roots and aboveground tissues in the field at grain maturity were measured to assess the differences in Zn uptake and partitioning among genotypes. Zn concentrations in roots and shoots at the seedling stage in hydroponics were also assessed to see if the differences in Zn uptake efficiencies could be identified before planting in the laboratory. The triticale had the highest grain Zn concentration, followed by the cultivated einkorn wheat, the T. petropavlovskyi wheat, and then the three common wheat varieties. No difference in Zn concentration was detected between grains from the main stem and grains from tillers. Genotypes with high grain Zn concentration generally had higher Zn uptake and rachis Zn loading efficiencies. The cultivated einkorn wheat showed retarded translocation of Zn in nodes. Genotypes with high Zn uptake efficiencies may be identified at the seedling stage in hydroponics. Grain Zn concentration is a product of multiple physiological processes. Roots, nodes, and rachis could be the potential target tissues for further research related to grain Zn accumulation.
Channel pruning has been demonstrated as a highly effective approach to compress large convolutional neural networks. Existing differentiable channel pruning methods usually use deterministic soft masks to scale the channelwise outputs and explore an appropriate threshold on the masks to remove unimportant channels, which sometimes causes unexpected damage to the network accuracy when there are no sweet spots that clearly separate important channels from redundant ones. In this article, we introduce a new differentiable channel pruning method based on polarization of probabilistic channelwise soft masks (PPSMs). We use variational inference to approximate the posterior distributions of the masks and simultaneously exploit a polarization regularization to push the probabilistic masks towards either 0 or 1; thus, the channels with near-zero masks can be safely eliminated with little hurt on network accuracy. Our method significantly relieves the difficulty faced by the existing methods to find an appropriate threshold on the masks. The joint inference and polarization of probabilistic soft masks enable PPSM to yield better pruning results than the state of the arts. For instance, our method prunes 65.91% FLOPs of ResNet50 on the ImageNet dataset with only 0.7% model accuracy degradation.
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