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AE-TPGG:a novel autoencoder-based approach for single-cell RNA-seq data imputation and dimensionality reduction

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Frontiers of Computer Science 2023年第3期17卷 217-234页

作者： Shuchang ZHAO Li ZHANG xuejun liu MIIT Key Laboratory of Pattern Analysis and Machine Intelligence College of Computer Science and TechnologyNanjing University of Aeronautics and AstronauticsNanjing 211106China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing 210023China College of Computer Science and Technology Nanjing Forestry UniversityNanjing 210037China

Single-cell RNA sequencing(scRNA-seq)technology has become an effective tool for high-throughout transcriptomic study,which circumvents the averaging artifacts corresponding to bulk RNA-seq technology,yielding new perspectives on the cellular diversity of potential superficially homogeneous *** various sequencing techniques have decreased the amplification bias and improved capture efficiency caused by the low amount of starting material,the technical noise and biological variation are inevitably introduced into experimental process,resulting in high dropout events,which greatly hinder the downstream *** the bimodal expression pattern and the right-skewed characteristic existed in normalized scRNA-seq data,we propose a customized autoencoder based on a twopart-generalized-gamma distribution(AE-TPGG)for scRNAseq data analysis,which takes mixed discrete-continuous random variables of scRNA-seq data into account using a twopart model and utilizes the generalized gamma(GG)distribution,for fitting the positive and right-skewed continuous *** adopted autoencoder enables AE-TPGG to captures the inherent relationship between *** addition to the ability of achieving low-dimensional representation,the AETPGG model also provides a denoised imputation according to statistical characteristic of gene *** on real datasets demonstrate that our proposed model is competitive to current imputation methods and ameliorates a diverse set of typical scRNA-seq data analyses.

关键词： scRNA-seq autoencoder TPGG data imputation dimensionality reduction

Detecting differential expression from RNA-seq data with expression measurement uncertainty

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Frontiers of Computer Science 2015年第4期9卷 652-663页

作者： LiZHANG Songcan CHEN xuejun liu College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing 210016 China

High-throughput RNA sequencing （RNA-seq） has emerged as a revolutionary and powerful technology for expression profiling. Most proposed methods for detect- ing differentially expressed （DE） genes from RNA-seq are based on statistics that compare normalized read counts be- tween conditions. However, there are few methods consider- ing the expression measurement uncertainty into DE detec- tion. Moreover, most methods are only capable of detecting DE genes, and few methods are available for detecting DE isoforms. In this paper, a Bayesian framework （BDSeq） is proposed to detect DE genes and isoforms with consideration of expression measurement uncertainty. This expression mea- surement uncertainty provides useful information which can help to improve the performance of DE detection. Three real RAN-seq data sets are used to evaluate the performance of BDSeq and results show that the inclusion of expression mea- surement uncertainty improves accuracy in detection of DE genes and isoforms. Finally, we develop a GamSeq-BDSeq RNA-seq analysis pipeline to facilitate users.

关键词： RNA-seq Bayesian method differentially ex-pressed genes/isoforms expression measurement uncer-tainty analysis pipeline

A Comprehensive Review on RNA-seq Data Analysis

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Transactions of Nanjing University of Aeronautics and Astronautics 2016年第3期33卷 339-361页

作者： Zhang Li liu xuejun College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics

RNA-sequencing(RNA-seq),based on next-generation sequencing technologies,has rapidly become a standard and popular technology for transcriptome ***,serious challenges still exist in analyzing and interpreting the RNA-seq *** the development of high-throughput sequencing technology,the sequencing depth of RNA-seq data increases *** intricate biological process of transcriptome is more complicated and diversified beyond our ***,most of the remaining organisms still have no available reference genome or have only incomplete genome ***,a large number of bioinformatics methods for various transcriptomics studies are proposed to effectively settle these *** review comprehensively summarizes the various studies in RNA-seq data analysis and their corresponding analysis methods,including genome annotation,quality control and pre-processing of reads,read alignment,transcriptome assembly,gene and isoform expression quantification,differential expression analysis,data visualization and other analyses.

关键词： transcriptome analysis high-throughput sequencing RNA-seq data analysis analysis pipeline

A generative deep learning framework for airfoil flow field prediction with sparse data

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Chinese Journal of Aeronautics 2022年第1期35卷 470-484页

作者： Haizhou WU xuejun liu Wei AN Hongqiang LYU MIIT Key Laboratory of Pattern Analysis and Machine Intelligence College of Computer Science and TechnologyNanjing University of Aeronautics and AstronauticsNanjing 211106China Key Laboratory of Aerodynamic Noise Control Mianyang 621000China State Key Laboratory of Aerodynamics Mianyang 621000China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing 210023China College of Aerospace Engineering Nanjing University of Aeronautics and AstronauticsNanjing 211106China

Deep learning has been probed for the airfoil performance prediction in recent *** with the expensive CFD simulations and wind tunnel experiments,deep learning models can be leveraged to somewhat mitigate such expenses with proper ***,effective training of the data-driven models in deep learning severely hinges on the data in diversity and *** this paper,we present a novel data augmented Generative Adversarial Network(GAN),daGAN,for rapid and accurate flow filed prediction,allowing the adaption to the task with sparse *** presented approach consists of two modules,pre-training module and fine-tuning *** pre-training module utilizes a conditional GAN(cGAN)to preliminarily estimate the distribution of the training *** the fine-tuning module,we propose a novel adversarial architecture with two generators one of which fulfils a promising data augmentation operation,so that the complement data is adequately incorporated to boost the generalization of the *** use numerical simulation data to verify the generalization of daGAN on airfoils and flow conditions with sparse training *** results show that daGAN is a promising tool for rapid and accurate evaluation of detailed flow field without the requirement for big training data.

关键词： CFD Flow field Generative adversarial networks(GANs) Sparse data Supercritical airfoil

On developing data-driven turbulence model for DG solution of RANS

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Chinese Journal of Aeronautics 2019年第8期32卷 1869-1884页

作者： Liang SUN Wei AN xuejun liu Hongqiang LYU College of Computer Science and Technology Nanjing University of Aeronautics and AstronauticsNanjing 211106China College of Aerospace Engineering Nanjing University of Aeronautics and AstronauticsNanjing 210016China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing 210093China

High-order Discontinuous Galerkin(DG) methods have been receiving more and more attentions in the area of Computational Fluid Dynamics(CFD) because of their high-accuracy property. However, it is still a challenge to obtain converged solution rapidly when solving the Reynolds Averaged Navier–Stokes(RANS) equations since the turbulence models significantly increase the nonlinearity of discretization system. The overall goal of this research is to develop an Artificial Neural Networks(ANNs) model with low complexity acting as an algebraic turbulence model to estimate the turbulence eddy viscosity for RANS. The ANN turbulence model is off-line trained using the training data generated by the widely used Spalart–Allmaras(SA) turbulence model before the Optimal Brain Surgeon(OBS) is employed to determine the relevancy of input *** the selected relevant features, a fully connected ANN model is constructed. The performance of the developed ANN model is numerically tested in the framework of DG for RANS, where the‘‘DG+ANN' method provides robust and steady convergence compared to the ‘‘DG+SA' method. The results demonstrate the promising potential to develop a general turbulence model based on artificial intelligence in the future given the training data covering a large rang of flow conditions.

关键词： Artificial neural network Discontinuous Galerkin method Fluid Optimal brain surgeon Spalart–Allmaras turbulence model

Detecting differential transcript usage across multiple conditions for RNA-seq data based on the smoothed LDA model

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Frontiers of Computer Science 2021年第3期15卷 217-219页

作者： Jing LI xuejun liu Daoqiang ZHANG College of Computer Science and Technology Nanjing University of Aeronautics and AstronauticsMIIT Key Laboratory of Pattern Analysis and Machine IntelligenceNanjing 211106China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing 210023China

1 Introduction and main contributions Differential transcript usage(DTU),which refers to the event that the relative transcript abundance within a gene changes between *** detect DTU,various methods have been proposed,which can be classified into exon-based models and gene-based *** approaches either cannot estimate the relative transcript abundance,or they cannot deal properly with the multi-source mapping problems of ***,few methods currently consider sample-to-sample variability under multiple conditions[1].

关键词： conditions. properly differential

A mesh optimization method using machine learning technique and variational mesh adaptation

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Chinese Journal of Aeronautics 2022年第3期35卷 27-41页

作者： Tingfan WU xuejun liu Wei AN Zenghui HUANG Hongqiang LYU MIITKey Laboratory of Pattern Analysis and Machine Intelligence College of Computer Science and TechnologyNanjing University of Aeronautics and AstronauticsNanjing 211106China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing 210023China College of Aerospace Engineering Nanjing University of Aeronautics and AstronauticsNanjing 211106China

Computational mesh is an important ingredient that affects the accuracy and efficiency of CFD numerical *** light of the introduced large amount of computational costs for many adaptive mesh methods,moving mesh methods keep the number of nodes and topology of a mesh unchanged and do not increase CFD computational *** the state-of-the-art moving mesh method,the variational mesh adaptation approach has been introduced to CFD ***,quickly estimating the flow field on the updated meshes during the iterative algorithm is challenging.A mesh optimization method,which embeds a machine learning regression model into the variational mesh adaptation,is *** regression model captures the mapping between the initial mesh nodes and the flow field,so that the variational method could move mesh nodes iteratively by solving the mesh functional which is built from the estimated flow field on the updated mesh via the regression *** the optimization,the density of the nodes in the high gradient area increases while the density in the low gradient area *** examples are first used to verify the feasibility and effectiveness of the proposed *** then we use the steady subsonic and transonic flows over cylinder and NACA0012 airfoil on unstructured triangular meshes to test our *** show that the proposed method significantly improves the accuracy of the local flow features on the adaptive *** work indicates that the proposed mesh optimization approach is promising for improving the accuracy and efficiency of CFD computation.

关键词： CFD Flow field Machine learning Moving mesh method Regression models Variational mesh adaptation

FINITE DEFORMATION ELASTO-PLASTIC THEORY AND CONSISTENT ALGORITHM

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Acta Mechanica Solida Sinica 2001年第1期14卷 31-40页

作者： liu xuejun Li Mingrui Huang Wenbin Applied Mechanics Workshop China Agricultural UniversityEast Campus Beijing 100083 China Applied Mechanics Workshop China Agricultural UniversityEast Campus Beijing 100083 China Applied Mechanics Workshop China Agricultural UniversityEast Campus Beijing 100083 China

By using the logarithmic strain, the finite deformation plastic theory, corresponding to the infinitesimal plastic theory, is established successively. The plastic consistent algorithm with first order accuracy for the finite element method (FEM) is developed. Numerical examples are presented to illustrate the validity of the theory and effectiveness of the algorithm.

关键词： finite element finite deformation elasto-plasticity consistent algorithm

Aerial pollutants on a pig farm in peri-urban Beijing,China

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International Journal of Agricultural and Biological Engineering 2013年第1期6卷 36-47页

作者： Roxana Mendoza Huaitalla Eva Gallmann liu xuejun Eberhard Hartung Institute of Agricultural Engineering University of HohenheimStuttgart70599Germany College of Agricultural Resources and Environmental Sciences China Agricultural UniversityBeijing 100094China Institute of Agricultural Engineering Christian-Albrechts-University KielKiel24098Germany

The study was carried out to evaluate the airborne gases and suspended particulate matter concentrations in the pig barns of a commercial pig farm situated in the peri-urban area of Beijing,*** measurements followed the natural pig life-stages namely:gestation,farrowing,weaning,and *** order to accomplish these objectives,three different measurement devices were employed:(i)color diffusion tubes,(ii)a portable gas measuring device,and(iii)a dust measuring *** to the rotation of the devices in the different sampling places,the measurements were performed at different time periods during the months of the summer and winter season in *** pig farm had a capacity of 15000 pigs per year and the manurial system was identified as“gan qing fen”or dry cleaning of the *** main by-products generated by the farm were irrigation water and small-scale biogas *** dust concentrations were identified in the pig barns,especially during the feeding and manure cleaning events inside the farrowing and weaning barns with slatted *** dust ranged from 0 mg/m3 to 12.45 mg/m3,while the allowable dust ranged from 0 mg/m3 to 9.62 mg/*** concentration ranged from 0 ppm to 20 ppm(1 ppm=1 cm3/m3),and the carbon dioxide concentration ranged from 300 ppm to 8000 *** highest ammonia concentration was recorded in the fattening barn during the summer season,while the highest carbon dioxide concentration was reported in the weaning barn during the summer *** results of this study were similar to the results of studies performed on pig farms under natural and mechanical ventilation *** the other hand,the problems encountered during the gas measurements on the pig farm were directly related to the inadequate housing layout due to the obstruction of windows,fans and air channels for indoor ventilation,and thus the measurement devices faced harsh working ***,it is recommended that the housing system s

关键词： pig farm aerial pollutant barn China gases dust