scholarly journals UBiT2: a client-side web-application for gene expression data analysis

2017 ◽  
Author(s):  
Jean Fan ◽  
David Fan ◽  
Kamil Slowikowski ◽  
Nils Gehlenborg ◽  
Peter Kharchenko
Keyword(s):  
Gene Expression ◽  
Data Analysis ◽  
Single Cell ◽  
Web Application ◽  
Differential Expression Analysis ◽  
Principal Component ◽  
Interactive Visualizations ◽  
Alignment Analysis ◽  
User Friendly ◽  
Client Side

We present a purely client-side web-application, UBiT2 (User-friendly BioInformatics Tools), that provides installation-free, offline alignment, analysis, and visualization of RNA-sequencing as well as qPCR data. Analysis modules were designed with single cell transcriptomic analysis in mind. Using just a browser, users can perform standard analyses such as quality control, filtering, hierarchical clustering, principal component analysis, differential expression analysis, gene set enrichment testing, and more, all with interactive visualizations and exportable publication-quality figures. We apply UBiT2 to recapitulate findings from single cell RNA-seq and Fluidigm Biomark TM multiplex RT-qPCR gene expression datasets. UBiT2 is available at http://pklab.med.harvard.edu/jean/ubit2/index.html with open-source code available at https://github.com/JEFworks/ubit2.

scholarly journals Asc-Seurat: analytical single-cell Seurat-based web application

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
W. J. Pereira ◽  
F. M. Almeida ◽  
D. Conde ◽  
K. M. Balmant ◽  
P. M. Triozzi ◽  
...  
Keyword(s):  
Data Analysis ◽  
Single Cell ◽  
Web Application ◽  
Gene Annotation ◽  
Developmental Trajectories ◽  
Cell Types ◽  
Biological Information ◽  
Graphical Interface ◽  
Peripheral Blood Mononuclear ◽  
User Friendly

Abstract Background Single-cell RNA sequencing (scRNA-seq) has revolutionized the study of transcriptomes, arising as a powerful tool for discovering and characterizing cell types and their developmental trajectories. However, scRNA-seq analysis is complex, requiring a continuous, iterative process to refine the data and uncover relevant biological information. A diversity of tools has been developed to address the multiple aspects of scRNA-seq data analysis. However, an easy-to-use web application capable of conducting all critical steps of scRNA-seq data analysis is still lacking. Summary We present Asc-Seurat, a feature-rich workbench, providing an user-friendly and easy-to-install web application encapsulating tools for an all-encompassing and fluid scRNA-seq data analysis. Asc-Seurat implements functions from the Seurat package for quality control, clustering, and genes differential expression. In addition, Asc-Seurat provides a pseudotime module containing dozens of models for the trajectory inference and a functional annotation module that allows recovering gene annotation and detecting gene ontology enriched terms. We showcase Asc-Seurat’s capabilities by analyzing a peripheral blood mononuclear cell dataset. Conclusions Asc-Seurat is a comprehensive workbench providing an accessible graphical interface for scRNA-seq analysis by biologists. Asc-Seurat significantly reduces the time and effort required to analyze and interpret the information in scRNA-seq datasets.

scholarly journals MicroScope: ChIP-seq and RNA-seq software analysis suite for gene expression heatmaps

2015 ◽  
Author(s):  
Bohdan B. Khomtchouk ◽  
James R. Hennessy ◽  
Claes Wahlestedt
Keyword(s):  
Web Application ◽  
Differential Expression Analysis ◽  
Dynamic Network ◽  
Principal Component ◽  
Rna Seq ◽  
Software Suite ◽  
Software Analysis ◽  
Link Type ◽  
R Shiny ◽  
User Friendly

AbstractWe propose a user-friendly ChIP-seq and RNA-seq software suite for the interactive visualization and analysis of genomic data, including integrated features to support differential expression analysis, interactive heatmap production, principal component analysis, gene ontology analysis, and dynamic network analysis.MicroScope is hosted online as an R Shiny web application based on the D3 JavaScript library: http://microscopebioinformatics.org/. The methods are implemented in R, and are available as part of the MicroScope project at: https://github.com/Bohdan-Khomtchouk/Microscope.

scholarly journals GENAVi: a shiny web application for gene expression normalization, analysis and visualization

BMC Genomics ◽  
2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Alberto Luiz P. Reyes ◽  
Tiago C. Silva ◽  
Simon G. Coetzee ◽  
Jasmine T. Plummer ◽  
Brian D. Davis ◽  
...  
Keyword(s):  
Gene Expression ◽  
Web Application ◽  
Differential Expression Analysis ◽  
Rna Seq ◽  
Data Analyst ◽  
Reproducible Analysis ◽  
Programming Knowledge ◽  
Next Generation Sequencing Ngs ◽  
User Friendly ◽  
Feature Count

Abstract Background The development of next generation sequencing (NGS) methods led to a rapid rise in the generation of large genomic datasets, but the development of user-friendly tools to analyze and visualize these datasets has not developed at the same pace. This presents a two-fold challenge to biologists; the expertise to select an appropriate data analysis pipeline, and the need for bioinformatics or programming skills to apply this pipeline. The development of graphical user interface (GUI) applications hosted on web-based servers such as Shiny can make complex workflows accessible across operating systems and internet browsers to those without programming knowledge. Results We have developed GENAVi (Gene Expression Normalization Analysis and Visualization) to provide a user-friendly interface for normalization and differential expression analysis (DEA) of human or mouse feature count level RNA-Seq data. GENAVi is a GUI based tool that combines Bioconductor packages in a format for scientists without bioinformatics expertise. We provide a panel of 20 cell lines commonly used for the study of breast and ovarian cancer within GENAVi as a foundation for users to bring their own data to the application. Users can visualize expression across samples, cluster samples based on gene expression or correlation, calculate and plot the results of principal components analysis, perform DEA and gene set enrichment and produce plots for each of these analyses. To allow scalability for large datasets we have provided local install via three methods. We improve on available tools by offering a range of normalization methods and a simple to use interface that provides clear and complete session reporting and for reproducible analysis. Conclusion The development of tools using a GUI makes them practical and accessible to scientists without bioinformatics expertise, or access to a data analyst with relevant skills. While several GUI based tools are currently available for RNA-Seq analysis we improve on these existing tools. This user-friendly application provides a convenient platform for the normalization, analysis and visualization of gene expression data for scientists without bioinformatics expertise.

scholarly journals SHAMAN: a user-friendly website for metataxonomic analysis from raw reads to statistical analysis

2020 ◽  
Author(s):  
Stevenn Volant ◽  
Pierre Lechat ◽  
Perrine Woringer ◽  
Laurence Motreff ◽  
Christophe Malabat ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Data Processing ◽  
Web Application ◽  
Graphical Representation ◽  
Statistical Modelling ◽  
Metagenomic Data ◽  
Sequencing Data ◽  
Microbiome Research ◽  
Interactive Visualizations ◽  
User Friendly

Abstract BackgroundComparing the composition of microbial communities among groups of interest (e.g., patients vs healthy individuals) is a central aspect in microbiome research. It typically involves sequencing, data processing, statistical analysis and graphical representation of the detected signatures. Such an analysis is normally obtained by using a set of different applications that require specific expertise for installation, data processing and in some case, programming skills. ResultsHere, we present SHAMAN, an interactive web application we developed in order to facilitate the use of (i) a bioinformatic workflow for metataxonomic analysis, (ii) a reliable statistical modelling and (iii) to provide among the largest panels of interactive visualizations as compared to the other options that are currently available. SHAMAN is specifically designed for non-expert users who may benefit from using an integrated version of the different analytic steps underlying a proper metagenomic analysis. The application is freely accessible at http://shaman.pasteur.fr/, and may also work as a standalone application with a Docker container (aghozlane/shaman), conda and R. The source code is written in R and is available at https://github.com/aghozlane/shaman. Using two datasets (a mock community sequencing and published 16S rRNA metagenomic data), we illustrate the strengths of SHAMAN in quickly performing a complete metataxonomic analysis. ConclusionsWe aim with SHAMAN to provide the scientific community with a platform that simplifies reproducible quantitative analysis of metagenomic data.

GEView (Gene Expression View) Tool for Intuitive and High Accessible Visualization of Expression Data for Non-Programmer Biologists

2020 ◽  
pp. 580-592
Author(s):  
Libi Hertzberg ◽  
Assif Yitzhaky ◽  
Metsada Pasmanik-Chor
Keyword(s):  
Gene Expression ◽  
Quality Control ◽  
User Interface ◽  
High Throughput ◽  
Graphical User Interface ◽  
Differential Expression Analysis ◽  
Biological Data ◽  
Expression Data ◽  
Batch Correction ◽  
User Friendly

This article describes how the last decade has been characterized by the production of huge amounts of different types of biological data. Following that, a flood of bioinformatics tools have been published. However, many of these tools are commercial, or require computational skills. In addition, not all tools provide intuitive and highly accessible visualization of the results. The authors have developed GEView (Gene Expression View), which is a free, user-friendly tool harboring several existing algorithms and statistical methods for the analysis of high-throughput gene, microRNA or protein expression data. It can be used to perform basic analysis such as quality control, outlier detection, batch correction and differential expression analysis, through a single intuitive graphical user interface. GEView is unique in its simplicity and highly accessible visualization it provides. Together with its basic and intuitive functionality it allows Bio-Medical scientists with no computational skills to independently analyze and visualize high-throughput data produced in their own labs.

scholarly journals TISCH: a comprehensive web resource enabling interactive single-cell transcriptome visualization of tumor microenvironment

2020 ◽  
Vol 49 (D1) ◽  
pp. D1420-D1430
Author(s):  
Dongqing Sun ◽  
Jin Wang ◽  
Ya Han ◽  
Xin Dong ◽  
Jun Ge ◽  
...  
Keyword(s):  
Gene Expression ◽  
Tumor Microenvironment ◽  
Single Cell ◽  
Large Scale ◽  
Differential Expression Analysis ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Multiple Cancer ◽  
Web Resource ◽  
Cancer Types

Abstract Cancer immunotherapy targeting co-inhibitory pathways by checkpoint blockade shows remarkable efficacy in a variety of cancer types. However, only a minority of patients respond to treatment due to the stochastic heterogeneity of tumor microenvironment (TME). Recent advances in single-cell RNA-seq technologies enabled comprehensive characterization of the immune system heterogeneity in tumors but posed computational challenges on integrating and utilizing the massive published datasets to inform immunotherapy. Here, we present Tumor Immune Single Cell Hub (TISCH, http://tisch.comp-genomics.org), a large-scale curated database that integrates single-cell transcriptomic profiles of nearly 2 million cells from 76 high-quality tumor datasets across 27 cancer types. All the data were uniformly processed with a standardized workflow, including quality control, batch effect removal, clustering, cell-type annotation, malignant cell classification, differential expression analysis and functional enrichment analysis. TISCH provides interactive gene expression visualization across multiple datasets at the single-cell level or cluster level, allowing systematic comparison between different cell-types, patients, tissue origins, treatment and response groups, and even different cancer-types. In summary, TISCH provides a user-friendly interface for systematically visualizing, searching and downloading gene expression atlas in the TME from multiple cancer types, enabling fast, flexible and comprehensive exploration of the TME.

scholarly journals CHARTS: A web application for characterizing and comparing tumor subpopulations in publicly available single-cell RNA-seq datasets

2020 ◽  
Author(s):  
Matthew N. Bernstein ◽  
Zijian Ni ◽  
Michael Collins ◽  
Mark E. Burkard ◽  
Christina Kendziorski ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Web Application ◽  
Cellular Heterogeneity ◽  
Rna Seq ◽  
Individual Gene ◽  
Genome Wide ◽  
Progression Of Disease ◽  
Cell Subpopulations ◽  
Available Information

AbstractBackgroundSingle-cell RNA-seq (scRNA-seq) enables the profiling of genome-wide gene expression at the single-cell level and in so doing facilitates insight into and information about cellular heterogeneity within a tissue. Perhaps nowhere is this more important than in cancer, where tumor and tumor microenvironment heterogeneity directly impact development, maintenance, and progression of disease. While publicly available scRNA-seq cancer datasets offer unprecedented opportunity to better understand the mechanisms underlying tumor progression, metastasis, drug resistance, and immune evasion, much of the available information has been underutilized, in part, due to the lack of tools available for aggregating and analysing these data.ResultsWe present CHARacterizing Tumor Subpopulations (CHARTS), a computational pipeline and web application for analyzing, characterizing, and integrating publicly available scRNA-seq cancer datasets. CHARTS enables the exploration of individual gene expression, cell type, malignancy-status, differentially expressed genes, and gene set enrichment results in subpopulations of cells across multiple tumors and datasets.ConclusionCHARTS is an easy to use, comprehensive platform for exploring single-cell subpopulations within tumors across the ever-growing collection of public scRNA-seq cancer datasets. CHARTS is freely available at charts.morgridge.org.

scholarly journals Analysis of Single Cell Transcriptomics in Paired Pediatric T-ALL Samples Collected at Diagnosis and Following End of Induction Therapy Reveals an MRD-Associated Stem Cell Signature

Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 1311-1311
Author(s):  
Swati S Bhasin ◽  
Beena E Thomas ◽  
Ryan J Summers ◽  
Debasree Sarkar ◽  
Hope L Mumme ◽  
...  
Keyword(s):  
Gene Expression ◽  
Stem Cells ◽  
B Cell ◽  
Single Cell ◽  
Communication Networks ◽  
Residual Disease ◽  
Differential Expression Analysis ◽  
Gene Signature ◽  
Patient Specific ◽  
Immune Microenvironment

Abstract Introduction Despite recent improvement in outcomes for de novo disease, pediatric T-cell acute lymphoblastic leukemia (T-ALL) remains challenging to treat at relapse. Investigation into genomic markers of treatment response and therapy resistance offers an opportunity to further enhance outcomes for these patients. We previously identified a T-ALL blast-associated gene signature at diagnosis (Dx) and characterized the immune microenvironment in Dx T-ALL marrow samples using single cell transcriptome analysis (Bhasin et al. Blood 2020(ASH)). This approach allowed us to generate a granular expression map of both the T-ALL landscape and the Dx bone marrow (BM) immune microenvironment. Here we expand this work by evaluating samples collected from the same patients Dx and End of Induction (EOI) BM samples from pediatric T-ALL patients. The use of paired samples provides insight into treatment-induced changes in the microenvironment. Further, the inclusion of both minimal residual disease (MRD) positive and MRD negative samples allowed us to compare differences between these groups. Methods Using the 10X genomics platform, we profiled the single cell transcriptome of ~18,000 BM and immune microenvironment cells from viably frozen samples collected from T-ALL patients at Dx or EOI. Five paired Dx and EOI samples and one EOI sample from a patient with relapsed T-ALL were evaluated, for a total of 11 samples. Three paired samples were MRD positive at EOI and two were MRD negative; the relapsed sample was MRD negative. Cell clustering was performed using the Seurat package and differential expression analysis was performed using R/Bioconductor packages (Hao et al. Cell 2021). Cell communication analysis was conducted using the CellChat R tool (v 1.0.0) to infer cell-cell communication within the EOI MRD positive and MRD negative subsets and compare their communication networks (Jin et al. Nature Comm 2021). Results Using our previously described blast-associated gene signature (Bhasin et al. ASH 2020) we were able to identify residual blast populations at EOI in MRD-positive samples. Comparative analysis of gene profiles at Dx and EOI showed significant changes in the microenvironment cell populations with highest increase in erythroid cell populations after induction therapy. The gene expression profiles were significantly different for immune cells at Dx and EOI and the relapsed sample had greater similarity to the Dx samples indicating a persistent immunosuppressive environment. Clustering analysis of the EOI samples (3 MRD positive and 2 MRD negative) demonstrated the presence of patient specific blast cells in MRD positive samples that retained patient-specific transcriptomeheterogeneity at EOI (Fig.1A). Analysis of communication networks between different cell types based on receptor and ligand expression levels between different cell types identified a CD34 + cluster of stem cells that had different interactions with other immune populations in the MRD positive and negative subsets. Differential expression analysis between the MRD positive and MRD negative cells in this CD34 + stem cell cluster identified higher expression of myeloid associated genes such as CEBPB, CEBPD, AZU1 in the MRD negative group relative to the MRD positive cells, which showed higher expression of B-cell related genes such as IGHM, VPREB1, CD79A/ B along with upregulation of P13K signaling in B-lymphocytes, B-cell receptor signaling and autophagy pathways. Analysis of upstream regulators based on the differential gene signature between the MRD positive and MRD negative group demonstrated upregulation of MYC and TCF3 activity and inhibition of TGFB1, CSF3 and CEBPA in MRD positive compared to MRD negative samples (Fig.1B). Conclusions: Leukemic blasts exhibit patient-specific gene expression signatures that are present at EOI in MRD positive samples. Exploration of the impact of minimal residual disease at EOI revealed differential gene expression patterns in stem cells from MRD positive samples, characterized by activation of B cell related signaling pathways and regulators such as MYC and TCF3. In contrast, a more myeloid-like expression signature was observed in stem cells from MRD negative samples. These findings open the avenues for exploration of therapeutic targets of T-ALL progression. Figure 1 Figure 1. Disclosures DeRyckere: Meryx: Other: Equity ownership. Graham: Meryx: Membership on an entity's Board of Directors or advisory committees, Other: Equity ownership.

scholarly journals ID: 1027 High-Throughput, Single Cell Whole Transcriptome Sequencing Analysis of Cancer Cells with the New BD FACSMelody™ Cell Sorter and BD™ Precise assay

2017 ◽  
Vol 4 (S) ◽  
pp. 102
Author(s):  
Xiaoyang (Alice) Wang ◽  
Chip Lomas ◽  
Craig Betts ◽  
Aaron Walker ◽  
Christina Fan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Cancer Cells ◽  
Cell Line ◽  
Selection Criteria ◽  
Single Cells ◽  
Principal Component ◽  
Jurkat Cells ◽  
Sequencing Analysis ◽  
Whole Transcriptome

Gene expression studies performed on bulk samples might obscure the understanding of complex samples. Gene expression analyses performed on single cells, however, can offer a powerful method to resolve sample heterogeneity and reveal hidden biology. Optimal sample preparation is critical to obtain high quality gene expression data from single cells.Historically, single cells or small numbers of cells were isolated and prepared by limiting dilutions, laser capture microdissection, or microfluidics technologies, or fluorescence-activated cell sorting (FACS). FACS sorting enables highthroughput processing of a heterogeneous mixture of cells and ensures the delivery of single cells or a small number ofcells into a chosen receptacle to meet the selection criteria at a purity level that is unmatched by other approaches.Furthermore, by FACS, the single cell selection criteria can be based on surface marker expression, cell size, and granularity(represented by scatter). Sorted cells can be used for any downstream application including next generation sequencing(NGS).In this study, the new, easy-to-use BD FACSMelody™ sorter was applied to sort individual cancer cells. Jurkat cells (a Tleukemia cell line), and T47D cells (a breast cancer cell line) were mixed, stained, analyzed, and sorted on a BD FACSMelody system. The individual cell’s whole transcriptome was interrogated using BD™ Precise Single Cell WTA (whole transcriptome amplification) Assay. Principal component analysis was applied to cluster the sorted Jurkat and T47D-cell populations.

scholarly journals Subpopulation identification for single-cell RNA-sequencing data using functional data analysis

2019 ◽  
Author(s):  
Kyungmin Ahn ◽  
Hironobu Fujiwara
Keyword(s):  
Gene Expression ◽  
Data Analysis ◽  
Single Cell ◽  
Gene Expression Data ◽  
Functional Data Analysis ◽  
Functional Data ◽  
Clustering Algorithms ◽  
Expression Data ◽  
Clustering Methods ◽  
Single Cell Rna Sequencing

AbstractBackgroundIn single-cell RNA-sequencing (scRNA-seq) data analysis, a number of statistical tools in multivariate data analysis (MDA) have been developed to help analyze the gene expression data. This MDA approach is typically focused on examining discrete genomic units of genes that ignores the dependency between the data components. In this paper, we propose a functional data analysis (FDA) approach on scRNA-seq data whereby we consider each cell as a single function. To avoid a large number of dropouts (zero or zero-closed values) and reduce the high dimensionality of the data, we first perform a principal component analysis (PCA) and assign PCs to be the amplitude of the function. Then we use the index of PCs directly from PCA for the phase components. This approach allows us to apply FDA clustering methods to scRNA-seq data analysis.ResultsTo demonstrate the robustness of our method, we apply several existing FDA clustering algorithms to the gene expression data to improve the accuracy of the classification of the cell types against the conventional clustering methods in MDA. As a result, the FDA clustering algorithms achieve superior accuracy on simulated data as well as real data such as human and mouse scRNA-seq data.ConclusionsThis new statistical technique enhances the classification performance and ultimately improves the understanding of stochastic biological processes. This new framework provides an essentially different scRNA-seq data analytical approach, which can complement conventional MDA methods. It can be truly effective when current MDA methods cannot detect or uncover the hidden functional nature of the gene expression dynamics.

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