scholarly journals ROSeq: Modeling expression ranks for noise-tolerant differential expression analysis of scRNA-Seq data

2018 ◽  
Author(s):  
Krishan Gupta ◽  
Manan Lalit ◽  
Aditya Biswas ◽  
Ujjwal Maulik ◽  
Sanghamitra Bandyopadhyay ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Differential Expression ◽  
Negative Binomial ◽  
Single Cells ◽  
Expression Patterns ◽  
Critical Role ◽  
Differential Expression Analysis ◽  
Parametric Models ◽  
Specific Gene

1AbstractSystematic delineation of complex biological systems is an ever-challenging and resource-intensive process. Single cell transcriptomics allows us to study cell-to-cell variability in complex tissues at an unprecedented resolution. Accurate modeling of gene expression plays a critical role in the statistical determination of tissue-specific gene expression patterns. In the past few years, considerable efforts have been made to identify appropriate parametric models for single cell expression data. The zero-inflated version of Poisson/Negative Binomial and Log-Normal distributions have emerged as the most popular alternatives due to their ability to accommodate high dropout rates, as commonly observed in single cell data. While the majority of the parametric approaches directly model expression estimates, we explore the potential of modeling expression-ranks, as robust surrogates for transcript abundance. Here we examined the performance of the Discrete Generalized Beta Distribution (DGBD) on real data and devised a Wald-type test for comparing gene expression across two phenotypically divergent groups of single cells. We performed a comprehensive assessment of the proposed method, to understand its advantages as compared to some of the existing best practice approaches. Besides striking a reasonable balance between Type 1 and Type 2 errors, we concluded that ROSeq, the proposed differential expression test is exceptionally robust to expression noise and scales rapidly with increasing sample size. For wider dissemination and adoption of the method, we created an R package called ROSeq, and made it available on the Bioconductor platform.

scholarly journals SCRIBE: a new approach to dropout imputation and batch effects correction for single-cell RNA-seq data

2019 ◽  
Author(s):  
Yiliang Zhang ◽  
Kexuan Liang ◽  
Molei Liu ◽  
Yue Li ◽  
Hao Ge ◽  
...  
Keyword(s):  
Gene Expression ◽  
Data Analysis ◽  
Single Cell ◽  
Single Cells ◽  
Expression Patterns ◽  
Dropout Rate ◽  
Biological Variation ◽  
Specific Gene ◽  
Batch Effects ◽  
Cell Recovery

AbstractSingle-cell RNA sequencing technologies are widely used in recent years as a powerful tool allowing the observation of gene expression at the resolution of single cells. Two of the major challenges in scRNA-seq data analysis are dropout events and batch effects. The inflation of zero(dropout rate) varies substantially across single cells. Evidence has shown that technical noise, including batch effects, explains a notable proportion of this cell-to-cell variation. To capture biological variation, it is necessary to quantify and remove technical variation. Here, we introduce SCRIBE (Single-Cell Recovery Imputation with Batch Effects), a principled framework that imputes dropout events and corrects batch effects simultaneously. We demonstrate, through real examples, that SCRIBE outperforms existing scRNA-seq data analysis tools in recovering cell-specific gene expression patterns, removing batch effects and retaining biological variation across cells. Our software is freely available online at https://github.com/YiliangTracyZhang/SCRIBE.

scholarly journals Single-Cell RNA-Seq of Mouse Decidual Leukocytes Reveals Intriguing Gestational Changes in the Immune Cell Landscape and Effects of SRC-1/-2 Double-Deficiency

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. A753-A754
Author(s):  
Jingfei Chen ◽  
Jialei Duan ◽  
Alina Montalbano ◽  
Boxun Li ◽  
Gary Hon ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Immune Cell ◽  
Expression Patterns ◽  
Critical Role ◽  
Fetal Lung ◽  
Specific Gene ◽  
Rna Seq ◽  
Cell Clusters ◽  
Pregnant Mice

Abstract Single-cell RNA-seq of Mouse Decidual Leukocytes Reveals Intriguing Gestational Changes in the Immune Cell Landscape and Effects of SRC-1/-2 Double-DeficiencyOur previous findings suggest that the fetus signals the mother when it is ready to be born through secretion of surfactant components/immune modulators, surfactant protein A (SP-A) and platelet-activating factor (PAF) by the fetal lung into amniotic fluid (AF). This occurs with increased proinflammatory cytokine expression by fetal AF macrophages (Mϕ), increased myometrial NF-κB activation and contractile (CAP) gene expression. Steroid receptor coactivator (Src)-1 and Src-2 are critical for developmental induction SP-A and PAF by the fetal lung. The finding that pregnant wild-type (WT) mice carrying Src-1 and Src-2 double-deficient fetuses (Src-1/-2d/d) manifested a marked delay (~38h) in parturition, further suggests that signals for parturition arise from the fetus and that Src-1 and Src-2 serve a critical role. Infiltrating leukocytes at the maternal-fetal interface (MFI)/decidua are known to play a central role in pregnancy maintenance and parturition timing. However, there is limited knowledge regarding gestational changes in immune cell composition toward term. To analyze gestational changes in the composition of immune cells within decidua and effects of Src-1/-2d/d, we conducted single-cell RNA-seq of ~17,000 decidual leukocytes (CD45+) from pregnant mice at 15.5 and 18.5 dpc carrying WT or Src-1/-2d/d fetuses. Unsupervised clustering identified 22 distinct decidual immune cell clusters, comprised of Mϕ, B cells, natural killer cells, neutrophils, dendritic cells and monocytes. Significant differences in cell type composition and transcriptional profiles were found across study groups (WT @ 15.5 dpc vs. 18.5 dpc; Src-1/-2d/dvs. WT @15.5 dpc and 18.5 dpc). Interestingly, in deciduas of pregnant mice carrying WT fetuses, Mϕ and B cell clusters markedly increased between 15.5 and 18.5 dpc, whereas, neutrophils declined; however, these gestational changes did not occur in pregnant mice carrying Src-1/-2d/d fetuses. Differential gene expression and gene ontology enrichment analyses revealed specific gene expression patterns distinguishing these immune cell subtypes to uncover their putative functions. These findings highlight the complexity and dynamics of the decidual immune cell landscape during the transition from myometrial quiescence to contractility, and the fetal-maternal interactions leading to parturition. Support: NIH grants R01-HL050022 (CRM) and P01-HD087150 (CRM), Burroughs Wellcome Preterm Birth Grant #1019823 (CRM).

scholarly journals scNetViz: from single cells to networks using Cytoscape

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 448
Author(s):  
Krishna Choudhary ◽  
Elaine C. Meng ◽  
J. Javier Diaz-Mejia ◽  
Gary D. Bader ◽  
Alexander R. Pico ◽  
...  
Keyword(s):  
Data Analysis ◽  
Single Cell ◽  
Differential Expression ◽  
Single Cells ◽  
Differential Expression Analysis ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Cellular Heterogeneity ◽  
Pathway Enrichment Analysis ◽  
Specific Gene

Single-cell RNA-sequencing (scRNA-seq) has revolutionized molecular biology and medicine by enabling high-throughput studies of cellular heterogeneity in diverse tissues. Applying network biology approaches to scRNA-seq data can provide useful insights into genes driving heterogeneous cell-type compositions of tissues. Here, we present scNetViz — a Cytoscape app to aid biological interpretation of cell clusters in scRNA-seq data using network analysis. scNetViz calculates the differential expression of each gene across clusters and then creates a cluster-specific gene functional interaction network between the significantly differentially expressed genes for further analysis, such as pathway enrichment analysis. To automate a complete data analysis workflow, scNetViz integrates parts of the Scanpy software, which is a popular Python package for scRNA-seq data analysis, with Cytoscape apps such as stringApp, cyPlot, and enhancedGraphics. We describe our implementation of methods for accessing data from public single cell atlas projects, differential expression analysis, visualization, and automation. scNetViz enables users to analyze data from public atlases or their own experiments, which we illustrate with two use cases. Analysis can be performed via the Cytoscape GUI or CyREST programming interface using R (RCy3) or Python (py4cytoscape).

scholarly journals ZipSeq : Barcoding for Real-time Mapping of Single Cell Transcriptomes

2020 ◽  
Author(s):  
Kenneth H. Hu ◽  
John P. Eichorst ◽  
Chris S. McGinnis ◽  
David M. Patterson ◽  
Eric D. Chow ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Real Time ◽  
Dimensional Space ◽  
Single Cells ◽  
Expression Patterns ◽  
Live Cells ◽  
Glass Substrates ◽  
Complete Mapping

ABSTRACTSpatial transcriptomics seeks to integrate single-cell transcriptomic data within the 3-dimensional space of multicellular biology. Current methods use glass substrates pre-seeded with matrices of barcodes or fluorescence hybridization of a limited number of probes. We developed an alternative approach, called ‘ZipSeq’, that uses patterned illumination and photocaged oligonucleotides to serially print barcodes (Zipcodes) onto live cells within intact tissues, in real-time and with on-the-fly selection of patterns. Using ZipSeq, we mapped gene expression in three settings: in-vitro wound healing, live lymph node sections and in a live tumor microenvironment (TME). In all cases, we discovered new gene expression patterns associated with histological structures. In the TME, this demonstrated a trajectory of myeloid and T cell differentiation, from periphery inward. A variation of ZipSeq efficiently scales to the level of single cells, providing a pathway for complete mapping of live tissues, subsequent to real-time imaging or perturbation.

scholarly journals DEsingle for detecting three types of differential expression in single-cell RNA-seq data

2017 ◽  
Author(s):  
Zhun Miao ◽  
Ke Deng ◽  
Xiaowo Wang ◽  
Xuegong Zhang
Keyword(s):  
Single Cell ◽  
Differential Expression ◽  
Negative Binomial ◽  
Single Cells ◽  
R Package ◽  
Supplementary Information ◽  
Binomial Model ◽  
Supplementary Data ◽  
Rna Seq ◽  
Real Zeros

AbstractSummaryThe excessive amount of zeros in single-cell RNA-seq data include “real” zeros due to the on-off nature of gene transcription in single cells and “dropout” zeros due to technical reasons. Existing differential expression (DE) analysis methods cannot distinguish these two types of zeros. We developed an R package DEsingle which employed Zero-Inflated Negative Binomial model to estimate the proportion of real and dropout zeros and to define and detect 3 types of DE genes in single-cell RNA-seq data with higher accuracy.Availability and ImplementationThe R package DEsingle is freely available at https://github.com/miaozhun/DEsingle and is under Bioconductor’s consideration [email protected] informationSupplementary data are available at bioRxiv online.

scholarly journals B-Cell and Monocyte Contribution to Systemic Lupus Erythematosus Identified by Cell-Type-Specific Differential Expression Analysis in RNA-Seq Data

2015 ◽  
Vol 9s3 ◽  
pp. BBI.S29470 ◽  
Author(s):  
Mikhail G. Dozmorov ◽  
Nicolas Dominguez ◽  
Krista Bean ◽  
Susan R. Macwana ◽  
Virginia Roberts ◽  
...  
Keyword(s):  
Gene Expression ◽  
B Cell ◽  
Differential Expression ◽  
Expression Analysis ◽  
Lupus Erythematosus ◽  
Differential Expression Analysis ◽  
Specific Gene ◽  
Cell Type ◽  
Specific Gene Expression ◽  
Cell Type Specific

Systemic lupus erythematosus (SLE) is an autoimmune disease characterized by complex interplay among immune cell types. SLE activity is experimentally assessed by several blood tests, including gene expression profiling of heterogeneous populations of cells in peripheral blood. To better understand the contribution of different cell types in SLE pathogenesis, we applied the two methods in cell-type-specific differential expression analysis, csSAM and DSection, to identify cell-type-specific gene expression differences in heterogeneous gene expression measures obtained using RNA-seq technology. We identified B-cell-, monocyte-, and neutrophil-specific gene expression differences. Immunoglobulin-coding gene expression was altered in B-cells, while a ribosomal signature was prominent in monocytes. On the contrary, genes differentially expressed in the heterogeneous mixture of cells did not show any functional enrichment. Our results identify antigen binding and structural constituents of ribosomes as functions altered by B-cell- and monocyte-specific gene expression differences, respectively. Finally, these results position both csSAM and DSection methods as viable techniques for cell-type-specific differential expression analysis, which may help uncover pathogenic, cell-type-specific processes in SLE.

scholarly journals Single-cell genomic atlas of great ape cerebral organoids uncovers human-specific features of brain development

2019 ◽  
Author(s):  
Sabina Kanton ◽  
Michael James Boyle ◽  
Zhisong He ◽  
Malgorzata Santel ◽  
Anne Weigert ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Expression Patterns ◽  
Adult Brain ◽  
The Other ◽  
Specific Gene ◽  
Great Ape ◽  
Specific Gene Expression ◽  
Gene Regulatory ◽  
Human Specific

ABSTRACTThe human brain has changed dramatically since humans diverged from our closest living relatives, chimpanzees and the other great apes1–5. However, the genetic and developmental programs underlying this divergence are not fully understood6–8. Here, we have analyzed stem cell-derived cerebral organoids using single-cell transcriptomics (scRNA-seq) and accessible chromatin profiling (scATAC-seq) to explore gene regulatory changes that are specific to humans. We first analyze cell composition and reconstruct differentiation trajectories over the entire course of human cerebral organoid development from pluripotency, through neuroectoderm and neuroepithelial stages, followed by divergence into neuronal fates within the dorsal and ventral forebrain, midbrain and hindbrain regions. We find that brain region composition varies in organoids from different iPSC lines, yet regional gene expression patterns are largely reproducible across individuals. We then analyze chimpanzee and macaque cerebral organoids and find that human neuronal development proceeds at a delayed pace relative to the other two primates. Through pseudotemporal alignment of differentiation paths, we identify human-specific gene expression resolved to distinct cell states along progenitor to neuron lineages in the cortex. We find that chromatin accessibility is dynamic during cortex development, and identify instances of accessibility divergence between human and chimpanzee that correlate with human-specific gene expression and genetic change. Finally, we map human-specific expression in adult prefrontal cortex using single-nucleus RNA-seq and find developmental differences that persist into adulthood, as well as cell state-specific changes that occur exclusively in the adult brain. Our data provide a temporal cell atlas of great ape forebrain development, and illuminate dynamic gene regulatory features that are unique to humans.

scholarly journals A network regularized linear model to infer spatial expression pattern for single cells

2021 ◽  
Author(s):  
Chaohao Gu ◽  
Zhandong Liu
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Spatial Information ◽  
Expression Profiles ◽  
Single Cells ◽  
Expression Patterns ◽  
Capture Rate ◽  
Marker Genes ◽  
Spatial Expression ◽  
Cellular Expression

Abstract Spatial gene-expression is a crucial determinant of cell fate and behavior. Recent imaging and sequencing-technology advancements have enabled scientists to develop new tools that use spatial information to measure gene-expression at close to single-cell levels. Yet, while Fluorescence In-situ Hybridization (FISH) can quantify transcript numbers at single-cell resolution, it is limited to a small number of genes. Similarly, slide-seq was designed to measure spatial-expression profiles at the single-cell level but has a relatively low gene-capture rate. And although single-cell RNA-seq enables deep cellular gene-expression profiling, it loses spatial information during sample-collection. These major limitations have stymied these methods’ broader application in the field. To overcome spatio-omics technology’s limitations and better understand spatial patterns at single-cell resolution, we designed a computation algorithm that uses glmSMA to predict cell locations by integrating scRNA-seq data with a spatial-omics reference atlas. We treated cell-mapping as a convex optimization problem by minimizing the differences between cellular-expression profiles and location-expression profiles with an L1 regularization and graph Laplacian based L2 regularization to ensure a sparse and smooth mapping. We validated the mapping results by reconstructing spatial- expression patterns of well-known marker genes in complex tissues, like the mouse cerebellum and hippocampus. We used the biological literature to verify that the reconstructed patterns can recapitulate cell-type and anatomy structures. Our work thus far shows that, together, we can use glmSMA to accurately assign single cells to their original reference-atlas locations.

scholarly journals Histologically resolved spatial multi-omics of human oral squamous cell carcinoma

2017 ◽  
Author(s):  
Tao Chen ◽  
Chen Cao ◽  
Jianyun Zhang ◽  
Aaron Streets ◽  
Yanyi Huang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Spatial Resolution ◽  
Squamous Cell ◽  
Single Cells ◽  
Expression Patterns ◽  
Critical Role ◽  
Molecular Profile ◽  
Tissue Samples

AbstractBoth the composition of cell types and their spatial distribution in a tissue play a critical role in cellular function, organ development, and disease progression. For example, intratumor heterogeneity and the distribution of transcriptional and genetic events in single cells drive the genesis and development of cancer. However, it can be challenging to fully characterize the molecular profile of cells in a tissue with high spatial resolution because microscopy has limited ability to extract comprehensive genomic information, and the spatial resolution of genomic techniques tends to be limited by dissection. There is a growing need for tools that can be used to explore the relationship between histological features, gene expression patterns, and spatially correlated genomic alterations in healthy and diseased tissue samples. Here, we present a technique that combines label-free histology with spatially resolved multi-omics in un-fixed and unstained tissue sections. This approach leverages stimulated Raman scattering microscopy to provide chemical contrast that reveals histological tissue architecture, allowing for high-resolution in situ laser micro-dissection of regions of interests. These micro-tissue samples are then processed for DNA and RNA sequencing to identify unique genetic profiles that correspond to distinct anatomical regions. We demonstrate the capabilities of this technique by mapping gene expression and copy number alterations to histologically defined regions in human squamous cell carcinoma (OSCC). Our approach provides complementary insights in tumorigenesis and offers an integrative tool for macroscale cancer tissues with spatial multi-omics assessments.

scholarly journals Exploring and analysing single cell multi-omics data with VDJView

2019 ◽  
Author(s):  
Jerome Samir ◽  
Simone Rizzetto ◽  
Money Gupta ◽  
Fabio Luciani
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
B Cells ◽  
Hypothesis Testing ◽  
Single Cell ◽  
Single Cells ◽  
Cellular Heterogeneity ◽  
Specific Gene ◽  
T And B Cells ◽  
Gene Signatures

Abstract Background Single cell RNA sequencing provides unprecedented opportunity to simultaneously explore the transcriptomic and immune receptor diversity of T and B cells. However, there are limited tools available that simultaneously analyse large multi-omics datasets integrated with metadata such as patient and clinical information.Results We developed VDJView, which permits the simultaneous or independent analysis and visualisation of gene expression, immune receptors, and clinical metadata of both T and B cells. This tool is implemented as an easy-to-use R shiny web-application, which integrates numerous gene expression and TCR analysis tools, and accepts data from plate-based sorted or high-throughput single cell platforms. We utilised VDJView to analyse several 10X scRNA-seq datasets, including a recent dataset of 150,000 CD8+ T cells with available gene expression, TCR sequences, quantification of 15 surface proteins, and 44 antigen specificities (across viruses, cancer, and self-antigens). We performed quality control, filtering of tetramer non-specific cells, clustering, random sampling and hypothesis testing to discover antigen specific gene signatures which were associated with immune cell differentiation states and clonal expansion across the pathogen specific T cells. We also analysed 563 single cells (plate-based sorted) obtained from 11 subjects, revealing clonally expanded T and B cells across primary cancer tissues and metastatic lymph-node. These immune cells clustered with distinct gene signatures according to the breast cancer molecular subtype. VDJView has been tested in lab meetings and peer-to-peer discussions, showing effective data generation and discussion without the need to consult bioinformaticians.Conclusions VDJView enables researchers without profound bioinformatics skills to analyse immune scRNA-seq data, integrating and visualising this with clonality and metadata profiles, thus accelerating the process of hypothesis testing, data interpretation and discovery of cellular heterogeneity. VDJView is freely available at https://bitbucket.org/kirbyvisp/vdjview .

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