scholarly journals Functional interpretation of single cell similarity maps

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
David DeTomaso ◽  
Matthew G. Jones ◽  
Meena Subramaniam ◽  
Tal Ashuach ◽  
Chun J. Ye ◽  
...  
Keyword(s):  
Single Cell ◽  
Case Studies ◽  
Data Dissemination ◽  
Low Latency ◽  
Rna Seq ◽  
Functional Interpretation ◽  
Meta Data ◽  
Web Based ◽  
Similarity Maps ◽  
Sources Of Variation

Abstract We present Vision, a tool for annotating the sources of variation in single cell RNA-seq data in an automated and scalable manner. Vision operates directly on the manifold of cell-cell similarity and employs a flexible annotation approach that can operate either with or without preconceived stratification of the cells into groups or along a continuum. We demonstrate the utility of Vision in several case studies and show that it can derive important sources of cellular variation and link them to experimental meta-data even with relatively homogeneous sets of cells. Vision produces an interactive, low latency and feature rich web-based report that can be easily shared among researchers, thus facilitating data dissemination and collaboration.

scholarly journals Functional Interpretation of Single-Cell Similarity Maps

2018 ◽  
Author(s):  
David DeTomaso ◽  
Matthew Jones ◽  
Meena Subramaniam ◽  
Tal Ashuach ◽  
Chun J. Ye ◽  
...  
Keyword(s):  
B Cells ◽  
Single Cell ◽  
Low Latency ◽  
Healthy Controls ◽  
Rna Seq ◽  
Functional Interpretation ◽  
Web Based ◽  
Homogeneous Set ◽  
Similarity Maps ◽  
Sources Of Variation

AbstractWe present VISION, a tool for annotating the sources of variation in single cell RNA-seq data in an automated, unbiased and scalable manner. VISION operates directly on the manifold of cell-cell similarity and employs a flexible annotation approach that can operate either with or without preconceived stratification of the cells into groups or along a continuum. We demonstrate the utility of VISION using a relatively homogeneous set of B cells from a cohort of lupus patients and healthy controls and show that it can derive important sources of cellular variation and link them to clinical phenotypes in a stratification free manner. VISION produces an interactive, low latency and feature rich web-based report that can be easily shared amongst researchers.

scholarly journals ASAP 2020 update: an open, scalable and interactive web-based portal for (single-cell) omics analyses

2020 ◽  
Vol 48 (W1) ◽  
pp. W403-W414
Author(s):  
Fabrice P A David ◽  
Maria Litovchenko ◽  
Bart Deplancke ◽  
Vincent Gardeux
Keyword(s):  
Big Data ◽  
Single Cell ◽  
Single Cell Analysis ◽  
Omics Data ◽  
Cell Analysis ◽  
Rna Seq ◽  
Analysis Pipeline ◽  
Web Based ◽  
Data Analyses ◽  
The Web

Abstract Single-cell omics enables researchers to dissect biological systems at a resolution that was unthinkable just 10 years ago. However, this analytical revolution also triggered new demands in ‘big data’ management, forcing researchers to stay up to speed with increasingly complex analytical processes and rapidly evolving methods. To render these processes and approaches more accessible, we developed the web-based, collaborative portal ASAP (Automated Single-cell Analysis Portal). Our primary goal is thereby to democratize single-cell omics data analyses (scRNA-seq and more recently scATAC-seq). By taking advantage of a Docker system to enhance reproducibility, and novel bioinformatics approaches that were recently developed for improving scalability, ASAP meets challenging requirements set by recent cell atlasing efforts such as the Human (HCA) and Fly (FCA) Cell Atlas Projects. Specifically, ASAP can now handle datasets containing millions of cells, integrating intuitive tools that allow researchers to collaborate on the same project synchronously. ASAP tools are versioned, and researchers can create unique access IDs for storing complete analyses that can be reproduced or completed by others. Finally, ASAP does not require any installation and provides a full and modular single-cell RNA-seq analysis pipeline. ASAP is freely available at https://asap.epfl.ch.

scholarly journals scTPA: A web tool for single-cell transcriptome analysis of pathway activation signatures

2020 ◽  
Author(s):  
Yan Zhang ◽  
Yaru Zhang ◽  
Jun Hu ◽  
Ji Zhang ◽  
Fangjie Guo ◽  
...  
Keyword(s):  
Data Analysis ◽  
Single Cell ◽  
Single Cells ◽  
Cell Types ◽  
Biological Pathways ◽  
Rna Seq ◽  
Web Tool ◽  
Functional Interpretation ◽  
Cell Functions ◽  
Pathway Activation

ABSTRACTThe most fundamental challenge in current single-cell RNA-seq data analysis is functional interpretation and annotation of cell clusters. The biological pathways in distinct cell types have different activation patterns, which facilitates understanding cell functions in single-cell transcriptomics. However, no effective web tool has been implemented for single-cell transcriptomic data analysis based on prior biological pathway knowledge. Here, we introduce scTPA (http://sctpa.bio-data.cn/sctpa), which is a web-based platform providing pathway-based analysis of single-cell RNA-seq data in human and mouse. scTPA incorporates four widely-used gene set enrichment methods to estimate the pathway activation scores of single cells based on a collection of available biological pathways with different functional and taxonomic classifications. The clustering analysis and cell-type-specific activation pathway identification were provided for the functional interpretation of cell types from pathway-oriented perspective. An intuitive interface allows users to conveniently visualize and download single-cell pathway signatures. Together, scTPA is a comprehensive tool to identify pathway activation signatures for dissecting single cell heterogeneity.

scholarly journals SC1: A Tool for Interactive Web-Based Single Cell RNA-Seq Data Analysis

2021 ◽  
Author(s):  
Marmar Moussa ◽  
Ion Mandoiu
Keyword(s):  
Data Analysis ◽  
Single Cell ◽  
Differential Expression Analysis ◽  
Analysis Tool ◽  
Rna Seq ◽  
Web Based ◽  
Inverse Document Frequency ◽  
Sequencing Technologies ◽  
Document Frequency ◽  
Novel Method

Single cell RNA-Seq (scRNA-Seq) is critical for studying cellular function and phenotypic heterogeneity as well as the development of tissues and tumors. Here, we present SC1 a web-based highly interactive scRNA-Seq data analysis tool publicly accessible at https://sc1.engr.uconn.edu. The tool presents an integrated workflow for scRNA-Seq analysis, implements a novel method of selecting informative genes based on Term-Frequency Inverse-Document-Frequency (TF-IDF) scores, and provides a broad range of methods for clustering, differential expression analysis, gene enrichment, interactive visualization, and cell cycle analysis. The tool integrates other single cell omics data modalities like TCR-Seq and supports several single cell sequencing technologies. In just a few steps, researchers can generate a comprehensive analysis and gain powerful insights from their scRNA-Seq data.

scholarly journals ASAP: A web-based platform for the analysis and interactive visualization of single-cell RNA-seq data

2016 ◽  
Author(s):  
Vincent Gardeux ◽  
Fabrice David ◽  
Adrian Shajkofci ◽  
Petra C Schwalie ◽  
Bart Deplancke
Keyword(s):  
Single Cell ◽  
Single Cell Analysis ◽  
Transcriptome Profiling ◽  
Cell Types ◽  
Complete Analysis ◽  
Marker Genes ◽  
Specific Marker ◽  
Rna Seq ◽  
Web Based ◽  
Wide Range

AbstractMotivationSingle-cell RNA-sequencing (scRNA-seq) allows whole transcriptome profiling of thousands of individual cells, enabling the molecular exploration of tissues at the cellular level. Such analytical capacity is of great interest to many research groups in the world, yet, these groups often lack the expertise to handle complex scRNA-seq data sets.ResultsWe developed a fully integrated, web-based platform aimed at the complete analysis of scRNA-seq data post genome alignment: from the parsing, filtering, and normalization of the input count data files, to the visual representation of the data, identification of cell clusters, differentially expressed genes (including cluster-specific marker genes), and functional gene set enrichment. This Automated Single-cell Analysis Pipeline (ASAP) combines a wide range of commonly used algorithms with sophisticated visualization tools. Compared with existing scRNA-seq analysis platforms, researchers (including those lacking computational expertise) are able to interact with the data in a straightforward fashion and in real time. Furthermore, given the overlap between scRNA-seq and bulk RNA-seq analysis workflows, ASAP should conceptually be broadly applicable to any RNA-seq dataset. As a validation, we demonstrate how we can use ASAP to simply reproduce the results from a single-cell study of 91 mouse cells involving five distinct cell types.AvailabilityThe tool is freely available at http://[email protected]

Targeted Single-Cell RNA-seq Identifies Minority Cell Types of Kidney Distal Nephron

2021 ◽  
pp. ASN.2020101407
Author(s):  
Lihe Chen ◽  
Chun-Lin Chou ◽  
Mark A. Knepper
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
The Other ◽  
Electrolyte Balance ◽  
Thick Ascending Limb ◽  
Rna Seq ◽  
Distal Nephron ◽  
Web Based ◽  
Proximal Tubule Cells ◽  
Variable Expression

BackgroundProximal tubule cells dominate the kidney parenchyma numerically, although less abundant cell types of the distal nephron have disproportionate roles in water and electrolyte balance.MethodsCoupling of a FACS-based enrichment protocol with single-cell RNA-seq profiled the transcriptomes of 9099 cells from the thick ascending limb (CTAL)/distal convoluted tubule (DCT) region of the mouse nephron.ResultsUnsupervised clustering revealed Slc12a3+/Pvalb+ and Slc12a3+/Pvalb− cells, identified as DCT1 and DCT2 cells, respectively. DCT1 cells appear to be heterogeneous, with orthogonally variable expression of Slc8a1, Calb1, and Ckb. An additional DCT1 subcluster showed marked enrichment of cell cycle–/cell proliferation–associated mRNAs (e.g., Mki67, Stmn1, and Top2a), which fit with the known plasticity of DCT cells. No DCT2-specific transcripts were found. DCT2 cells contrast with DCT1 cells by expression of epithelial sodium channel β- and γ-subunits and much stronger expression of transcripts associated with calcium transport (Trpv5, Calb1, S100g, and Slc8a1). Additionally, scRNA-seq identified three distinct CTAL (Slc12a1+) cell subtypes. One of these expressed Nos1 and Avpr1a, consistent with macula densa cells. The other two CTAL clusters were distinguished by Cldn10 and Ptger3 in one and Cldn16 and Foxq1 in the other. These two CTAL cell types were also distinguished by expression of alternative Iroquois homeobox transcription factors, with Irx1 and Irx2 in the Cldn10+ CTAL cells and Irx3 in the Cldn16+ CTAL cells.ConclusionsSingle-cell transcriptomics revealed unexpected diversity among the cells of the distal nephron in mouse. Web-based data resources are provided for the single-cell data.

scholarly journals SymSim: simulating multi-faceted variability in single cell RNA sequencing

2018 ◽  
Author(s):  
Xiuwei Zhang ◽  
Chenling Xu ◽  
Nir Yosef
Keyword(s):  
Single Cell ◽  
Method Development ◽  
Capture Rate ◽  
Ground Truth ◽  
R Package ◽  
Primary Sources ◽  
Rna Seq ◽  
Sources Of Variation ◽  
Low Sensitivity

The abundance of new computational methods for processing and interpreting transcriptomes at a single cell level raises the need for in-silico platforms for evaluation and validation. Simulated datasets which resemble the properties of real datasets can aid in method development and prioritization as well as in questions in experimental design by providing an objective ground truth. Here, we present SymSim, a simulator software that explicitly models the processes that give rise to data observed in single cell RNA-Seq experiments. The components of the SymSim pipeline pertain to the three primary sources of variation in single cell RNA-Seq data: noise intrinsic to the process of transcription, extrinsic variation that is indicative of different cell states (both discrete and continuous), and technical variation due to low sensitivity and measurement noise and bias. Unlike other simulators, the parameters that govern the simulation process directly represent meaningful properties such as mRNA capture rate, the number of PCR cycles, sequencing depth, or the use of unique molecular identifiers. We demonstrate how SymSim can be used for benchmarking methods for clustering and differential expression and for examining the effects of various parameters on their performance. We also show how SymSim can be used to evaluate the number of cells required to detect a rare population and how this number deviates from the theoretical lower bound as the quality of the data decreases. SymSim is publicly available as an R package and allows users to simulate datasets with desired properties or matched with experimental data.

scholarly journals Scalable latent-factor models applied to single-cell RNA-seq data separate biological drivers from confounding effects

2016 ◽  
Author(s):  
Florian Buettner ◽  
Naruemon Pratanwanich ◽  
John C. Marioni ◽  
Oliver Stegle
Keyword(s):  
Single Cell ◽  
Real Data ◽  
Rna Seq ◽  
Biological Factors ◽  
Computationally Efficient ◽  
Pathway Annotation ◽  
Large Populations ◽  
Sources Of Variation ◽  
Latent Factor Models ◽  
Gene Expression Levels

Single-cell RNA-sequencing (scRNA-seq) allows heterogeneity in gene expression levels to be studied in large populations of cells. Such heterogeneity can arise from both technical and biological factors, thus making decomposing sources of variation extremely difficult. We here describe a computationally efficient model that uses prior pathway annotation to guide inference of the biological drivers underpinning the heterogeneity. Moreover, we jointly update and improve gene set annotation and infer factors explaining variability that fall outside the existing annotation. We validate our method using simulations, which demonstrate both its accuracy and its ability to scale to large datasets with up to 100,000 cells. Moreover, through applications to real data we show that our model can robustly decompose scRNA-seq datasets into interpretable components and facilitate the identification of novel sub-populations.

scholarly journals Computational cell cycle analysis of single cell RNA-Seq data

2020 ◽  
Author(s):  
Marmar Moussa ◽  
Ion I. Măndoiu
Keyword(s):  
Cell Cycle ◽  
Single Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Analysis Tool ◽  
Rna Seq ◽  
Cell Type ◽  
Computational Cell ◽  
Analysis Pipeline ◽  
Web Based

AbstractThe variation in gene expression profiles of cells captured in different phases of the cell cycle can interfere with cell type identification and functional analysis of single cell RNA-Seq (scRNA-Seq) data. In this paper, we introduce SC1CC (SC1 Cell Cycle analysis tool), a computational approach for clustering and ordering single cell transcriptional profiles according to their progression along cell cycle phases. We also introduce a new robust metric, Gene Smoothness Score (GSS) for assessing the cell cycle based order of the cells. SC1CC is available as part of the SC1 web-based scRNA-Seq analysis pipeline, publicly accessible at https://sc1.engr.uconn.edu/.

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