We introduce Sashimi plots, a quantitative multi-sample visualization of mRNA sequencing reads aligned to gene annotations. Sashimi plots are made using alignments (stored in the SAM/BAM format) and gene model annotations (in GFF format), which can be custom-made by the user or obtained from databases such as Ensembl or UCSC. We describe two implementations of Sashimi plots: (1) a stand-alone command line implementation aimed at making customizable publication quality figures, and (2) an implementation built into the Integrated Genome Viewer (IGV) browser, which enables rapid and dynamic creation of Sashimi plots for any genomic region of interest, suitable for exploratory analysis of alternatively spliced regions of the transcriptome. Isoform expression estimates outputted by the MISO program can be optionally plotted along with Sashimi plots. Sashimi plots can be used to quickly screen differentially spliced exons along genomic regions of interest and can be used in publication quality figures. The Sashimi plot software and documentation is available from: http://genes.mit.edu/burgelab/miso/docs/sashimi.html
The existence of doublets is a key confounder in single-cell RNA sequencing (scRNA-seq) data analysis. Computational methods have been developed for detecting doublets from scRNA-seq data. We developed an R package DoubletCollection to integrate the installation and execution of eight doublet-detection methods. DoubletCollection also provides a unified interface to perform and visualize downstream analysis after doublet detection. Here, we present a protocol of using DoubletCollection to benchmark doublet-detection methods. This protocol can automatically accommodate new doublet-detection meth…
RNA-sequencing (RNA-seq) has become an exemplar technology in modern biology and clinical applications over the past decade. It has gained immense popularity in the recent years driven by continuous efforts of the bioinformatics community to develop accurate and scalable computational tools. RNA-seq is a method of analyzing the RNA content of a sample using the modern sequencing platforms. It generates enormous amounts of transcriptomic data in the form of nucleotide sequences, known as reads. RNA-seq analysis enables the probing of genes and corresponding transcripts which is essential for an…
Coronavirus disease 2019 (COVID-19) has impacted almost every part of human life worldwide, posing a massive threat to human health. There is no specific drug for COVID-19, highlighting the urgent need for the development of effective therapeutics. To identify potentially repurposable drugs, we employed a systematic approach to mine candidates from U.S. FDA-approved drugs and preclinical small-molecule compounds by integrating the gene expression perturbation data for chemicals from the Library of Integrated Network-Based Cellular Signatures project with a publicly available single-cell RNA se…
The advent of DNA and RNA sequencing has revolutionized the study of genomics and molecular biology. Next generation sequencing (NGS) technologies like Illumina, Ion Torrent, SOLiD sequencing etc. have brought about a quick and cheap way to sequence genomes. Recently, third generation sequencing (TGS) technologies like PacBio and Oxford Nanopore Technology (ONT) have also been developed. Different technologies use different underlying methods for sequencing and are prone to different error rates. Though many tools exist for error correction of sequencing data from NGS and TGS methods, no stand…
High-throughput cDNA sequencing (RNA-seq) is a very powerful technique to quantify gene expression in an unbiased way. The Crustacean family is among the groups of organisms sparsely represented in current genomic databases. Here we present transcriptome data from Uca minax (red-jointed fiddler crab) as an opportunity to extend our knowledge. Next generation sequencing was performed on six tissue samples from Uca minax using the Illumina HiSeq system. Six Transcriptome libraries were created using Trinity; a free, open-source software tool for de novo transcriptome assembly of high-throughput …
In our Brief Communication (DOI: 10.1038/s41592-018-0033-z), we presented the method SAVER for recovering true gene expression levels in noisy single cell RNA sequencing data. We evaluated the performance of SAVER, along with comparable methods MAGIC and scImpute, in an RNA FISH validation experiment and a data downsampling experiment. In a Comment [arXiv:1908.07084v1], Li & Li were concerned with the use of the downsampled datasets, specifically focusing on clustering results obtained from the Zeisel et al. data. Here, we will address these comments and, furthermore, amend the data downsampli…
Single-cell RNA sequencing (scRNA-seq) provides a high throughput, quantitative and unbiased framework for scientists in many research fields to identify and characterize cell types within heterogeneous cell populations from various tissues. However, scRNA-seq based identification of discrete cell-types is still labor intensive and depends on prior molecular knowledge. Artificial intelligence has provided faster, more accurate, and user-friendly approaches for cell-type identification. In this review, we discuss recent advances in cell-type identification methods using artificial intelligence …
The problem of estimating the structure of a graph from observed data is of growing interest in the context of high-throughput genomic data, and single-cell RNA sequencing in particular. These, however, are challenging applications, since the data consist of high-dimensional counts with high variance and over-abundance of zeros. Here, we present a general framework for learning the structure of a graph from single-cell RNA-seq data, based on the zero-inflated negative binomial distribution. We demonstrate with simulations that our approach is able to retrieve the structure of a graph in a vari…
Glaucoma is the leading cause of irreversible blindness in people over the age of 60, accounting for 6.6 to 8% of all blindness in 2010, but there is still much to be learned about the genetic origins of the eye disease. With the modern development of Next-Generation Sequencing (NGS) technologies, scientists are starting to learn more about the genetic origins of Glaucoma. This research uses differential expression (DE) and gene ontology (GO) analyses to study the genetic differences between mice with severe Glaucoma and multiple control groups. Optical nerve head (ONH) and retina data samples…
Single-cell RNA-Sequencing (scRNA-Seq) is a revolutionary technique for discovering and describing cell types in heterogeneous tissues, yet its measurement of expression often suffers from large systematic bias. A major source of this bias is the cell cycle, which introduces large within-cell-type heterogeneity that can obscure the differences in expression between cell types. The current method for removing the cell-cycle effect is unable to effectively identify this effect and has a high risk of removing other biological components of interest, compromising downstream analysis. We present cc…
On June 25th, 2018, Huang et al. published a computational method SAVER on Nature Methods for imputing dropout gene expression levels in single cell RNA sequencing (scRNA-seq) data. Huang et al. performed a set of comprehensive benchmarking analyses, including comparison with the data from RNA fluorescence in situ hybridization, to demonstrate that SAVER outperformed two existing scRNA-seq imputation methods, scImpute and MAGIC. However, their computational analyses were based on semi-synthetic data that the authors had generated following the Poisson-Gamma model used in the SAVER method. We h…
We propose a probabilistic model for interpreting gene expression levels that are observed through single-cell RNA sequencing. In the model, each cell has a low-dimensional latent representation. Additional latent variables account for technical effects that may erroneously set some observations of gene expression levels to zero. Conditional distributions are specified by neural networks, giving the proposed model enough flexibility to fit the data well. We use variational inference and stochastic optimization to approximate the posterior distribution. The inference procedure scales to over on…
We propose a probabilistic model for interpreting gene expression levels that are observed through single-cell RNA sequencing. In the model, each cell has a low-dimensional latent representation. Additional latent variables account for technical effects that may erroneously set some observations of gene expression levels to zero. Conditional distributions are specified by neural networks, giving the proposed model enough flexibility to fit the data well. We use variational inference and stochastic optimization to approximate the posterior distribution. The inference procedure scales to over on…
scUnified: An AI-Ready Standardized Resource for Single-Cell RNA Sequencing Analysis
Computer Network Information Center, Chinese Academy of Sciences（中国科学院计算机网络信息中心）；Hangzhou Institute for Advanced Study,University of Chinese Academy of Sciences（中国科学院大学杭州先进研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Department of Computer Science, Columbia University（哥伦比亚大学计算机科学系）
Deep sequencing has become one of the most popular tools for transcriptome profiling in biomedical studies. While an abundance of computational methods exists for "normalizing" sequencing data to remove unwanted between-sample variations due to experimental handling, there is no consensus on which normalization is the most suitable for a given data set. To address this problem, we developed "DANA" - an approach for assessing the performance of normalization methods for microRNA sequencing data based on biology-motivated and data-driven metrics. Our approach takes advantage of well-known biolog…
Weill Cornell Medicine（威尔康奈尔医学院）；Cornell University（康奈尔大学）；Institute for Computational Biomedicine（计算生物医学研究所）；Caryl and Israel Englander Institute for Precision Medicine（卡里尔与以色列·英格兰德精准医学研究所）；Temple University（天普大学）
Single-cell RNA-sequencing (scRNA-seq) has become a routinely used technique to quantify the gene expression profile of thousands of single cells simultaneously. Analysis of scRNA-seq data plays an important role in the study of cell states and phenotypes, and has helped elucidate biological processes, such as those occurring during development of complex organisms and improved our understanding of disease states, such as cancer, diabetes, and COVID, among others. Deep learning, a recent advance of artificial intelligence that has been used to address many problems involving large datasets, ha…
University of California, Davis（加州大学戴维斯分校）；The Pennsylvania State University（宾夕法尼亚州立大学）；Texas A&M University（得克萨斯农工大学）；Yale University（耶鲁大学）
Spectral clustering is one of the fundamental unsupervised learning methods widely used in data analysis. Sparse spectral clustering (SSC) imposes sparsity to the spectral clustering and it improves the interpretability of the model. This paper considers a widely adopted model for SSC, which can be formulated as an optimization problem over the Stiefel manifold with nonsmooth and nonconvex objective. Such an optimization problem is very challenging to solve. Existing methods usually solve its convex relaxation or need to smooth its nonsmooth part using certain smoothing techniques. In this pap…
In recent years the publication of genome sequences for the Chinese hamster and Chinese hamster ovary (CHO) cell lines have facilitated study of these biopharmaceutical cell factories with unprecedented resolution. Our understanding of the CHO cell transcriptome, in particular, has rapidly advanced through the application of next-generation sequencing (NGS) technology to characterise RNA expression (RNA-Seq). In this chapter we present a computational pipeline for the analysis of CHO cell RNA-Seq data from the Illumina platform to identify differentially expressed genes. The example data and b…
Hebei University of Technology（河北工业大学）；University of Victoria（维多利亚大学）；National University of Sciences（国立科学大学）；University of Saskatchewan（萨斯喀彻温大学）
Background: Single-cell RNA sequencing (scRNA-seq) yields valuable insights about gene expression and gives critical information about complex tissue cellular composition. In the analysis of single-cell RNA sequencing, the annotations of cell subtypes are often done manually, which is time-consuming and irreproducible. Garnett is a cell-type annotation software based the on elastic net method. Besides cell-type annotation, supervised machine learning methods can also be applied to predict other cell phenotypes from genomic data. Despite the popularity of such applications, there is no existing…
scGTN: Deep Siamese Graph Transformer Network for Single-cell RNA Sequencing Clustering
Sichuan University（四川大学）；University of International Business and Economics（对外经济贸易大学）；Great Bay University（大湾区大学）；The Education University of Hong Kong（香港教育大学）
scSiameseClu: A Siamese Clustering Framework for Interpreting single-cell RNA Sequencing Data
Computer Network Information Center, Chinese Academy of Sciences（中国科学院计算机网络信息中心）；University of Chinese Academy of Sciences（中国科学院大学）；College of Life Science, Northeast Agricultural University（东北农业大学生命科学学院）；SKL-IOTSC and Department of CIS, University of Macau（澳门大学SKL-IOTSC和CIS系）；Jilin University（吉林大学）
According to the National Cancer Institute, there were 9.5 million cancer-related deaths in 2018. A challenge in improving treatment is resistance in genetically unstable cells. The purpose of this study is to evaluate unsupervised machine learning on classifying treatment-resistant phenotypes in heterogeneous tumors through analysis of single cell RNA sequencing(scRNAseq) data with a pipeline and evaluation metrics. scRNAseq quantifies mRNA in cells and characterizes cell phenotypes. One scRNAseq dataset was analyzed (tumor/non-tumor cells of different molecular subtypes and patient identific…
Urothelial cell carcinoma (UCC) is the ninth most common cancer that accounts for 4.7% of all the new cancer cases globally. UCC development and progression are due to complex and stochastic genetic programmes. To study the cascades of molecular events underlying the poor prognosis that may lead to limited treatment options for advanced disease and resistance to conventional therapies in UCC, transcriptomics technology (RNA-Seq), a method of analysing the RNA content of a sample using modern high-throughput sequencing platforms has been employed. Here we review the principles of RNA-Seq techno…
Path Signatures Enable Model-Free Mapping of RNA Modifications
Mathematical Institute, University of Oxford（牛津大学数学研究所）；Department of Mathematics, Imperial College London（伦敦帝国理工学院数学系）；Division of Immune Diversity, German Cancer Research Center（德国癌症研究中心免疫多样性部门）；Department of Statistics, University of Warwick（华威大学统计系）；Klaus Tschira Institute for Integrative Computational Cardiology, Heidelberg University, Medical Faculty Heidelberg（海德堡大学医学院克劳斯·施齐拉整合计算心脏病学研究所）；German Centre for Cardiovascular Research (DZHK)-Partner Site Heidelberg/Mannheim（德国心血管研究联合会（DZHK）海德堡/曼海姆合作站点）；Department of Infectious Diseases, Molecular Virology, Center for Integrative Infectious Disease Research, Heidelberg University, Medical Faculty Heidelberg（海德堡大学医学院传染病与分子病毒学系综合传染病研究中心）
CASPER: Cross-modal Alignment of Spatial and single-cell Profiles for Expression Recovery
Department of Computer Science \& Engineering, Indian Institute of Technology Ropar, India Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar. Department of Biomedical Engineering, Indian Institute of Technology Ropar, India
Defining the relationship between cathepsin B and esophageal adenocarcinoma: conjoint analysis of Mendelian randomization, transcriptome-wide association studies, and single-cell RNA sequencing data
