Cerebro: Interactive visualization of scRNA-seq data

Hillje, Roman; Pelicci, Pier Giuseppe; Luzi, Lucilla

doi:10.1101/631705

Cited by 18 publications

(21 citation statements)

References 10 publications

(10 reference statements)

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“…Additionally, there are several efforts to enrich visualisations by either using a 3D plot instead of 2D (Hillje et al 2019;Gardeux et al 2017) or even by using virtual reality (Legetth et al 2018) . The third dimension may allow resolution of cell-types not visible in two dimensions.…”

Section: Discussionmentioning

confidence: 99%

Comparison of visualisation tools for single-cell RNAseq data

Cakir

Prete

Huang

et al. 2020

Preprint

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In the last decade, single cell RNAseq (scRNAseq) datasets have grown from a single cell to millions of cells. Due to its high dimensionality, the scRNAseq data contains a lot of valuable information, however, it is not always feasible to visualise and share it in a scientific report or an article publication format. Recently, a lot of interactive analysis and visualisation tools have been developed to address this issue and facilitate knowledge transfer in the scientific community. In this study, we review and compare several of the currently available analysis and visualisation tools and benchmark those that allow to visualize the scRNAseq data on the web and share it with others. To address the problem of format compatibility for most visualisation tools, we have also developed a user-friendly R package, sceasy , which allows users to convert their own scRNAseq datasets into a specific data format for visualisation.

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Section: Discussionmentioning

confidence: 99%

Comparison of visualisation tools for single-cell RNAseq data

Cakir

Prete

Huang

et al. 2020

Preprint

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“…For instance, Pagoda2 (Fan et al, 2016) comes with a simple intuitive web app but is limited to data processed with Pagoda2. Cerebro (Hillje, Pelicci & Luzi, 2019) is a Shiny web app and provides relatively rich functionality such as gene set enrichments and quality control statistics, but the input is limited to Seurat objects, similar to the Single Cell Viewer (SCV; Wang et al, 2019) which also relies on Shiny. CellexalVR (Legetth et al, 2018) provides an immersive virtual reality platform for the visualization and analysis of scRNA-seq data, but requires special hardware and runs only on Windows 10.…”

Section: State Of the Artmentioning

confidence: 99%

SCelVis: exploratory single cell data analysis on the desktop and in the cloud

Obermayer

Holtgrewe

Nieminen

et al. 2020

PeerJ

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Background Single cell omics technologies present unique opportunities for biomedical and life sciences from lab to clinic, but the high dimensional nature of such data poses challenges for computational analysis and interpretation. Furthermore, FAIR data management as well as data privacy and security become crucial when working with clinical data, especially in cross-institutional and translational settings. Existing solutions are either bound to the desktop of one researcher or come with dependencies on vendor-specific technology for cloud storage or user authentication. Results To facilitate analysis and interpretation of single-cell data by users without bioinformatics expertise, we present SCelVis, a flexible, interactive and user-friendly app for web-based visualization of pre-processed single-cell data. Users can survey multiple interactive visualizations of their single cell expression data and cell annotation, define cell groups by filtering or manual selection and perform differential gene expression, and download raw or processed data for further offline analysis. SCelVis can be run both on the desktop and cloud systems, accepts input from local and various remote sources using standard and open protocols, and allows for hosting data in the cloud and locally. We test and validate our visualization using publicly available scRNA-seq data. Methods SCelVis is implemented in Python using Dash by Plotly. It is available as a standalone application as a Python package, via Conda/Bioconda and as a Docker image. All components are available as open source under the permissive MIT license and are based on open standards and interfaces, enabling further development and integration with third party pipelines and analysis components. The GitHub repository is https://github.com/bihealth/scelvis.

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“…A widely used strategy is to reduce the high dimension into a low space of two-or three-dimensions, and in which, data analysis, visualization and interpretation could be further performed. For fast browsing scRNA-seq data, applications such as SCV (Wang, et al, 2019) and Cerebro (Hillje, et al, 2019) have been developed. However, they require local installation and implementation, as well as a multi-step pre-processing procedure including raw data processing, dimension reduction and data input standardization.…”

Section: Introductionmentioning

confidence: 99%