Data Science & Machine Learning

Comparison of Single Cell Data Visualization Tools

Kriti Srivastava
April 10, 2023

Visualizing the massive amount of data generated by scRNA-sequencing techniques is crucial for extracting meaningful insights, such as cell subpopulation identification, differential expression analysis, allelic expression exploration etc. With so many single-cell data visualization tools available, deciding which ones best suit your specific research questions can be challenging. This blog will compare some of the most popular single-cell data visualization tools, including their many features, such as open-source application integration with other omics technologies, etc.

Problems in Visualizing scRNA-seq Data

scRNA-sequencing enables high granularity and visualization of changes in the expression pattern between cell types and during different states. However, this generates data that has high variability, errors, and background noise. The problems and challenges arising in analyzing such data require specialized computational tools and annotation processes. Lack of data standardization and arbitrary methodologies are other hindrances to making single-cell RNA seq a more robust and reliable tool for genomic or transcriptomic research.

Widely used scRNA seq data visualization tools

Let’s look at a few of the most popular GUI-based tools (biologists and non-coders, rejoice!) for visualizing scRNA-sequencing data.

  • CellxGene VIP
  • Cellenics
  • Rosalind
  • Loupe Cell Browser

CellxGene VIP

CellxGene (pronounced "cell-by-gene") is an interactive data explorer for single-cell transcriptomics datasets. It provides t-SNE and UMAP visualization, with customization options for color coding based on gene expression, clustering of cells based on gene expression, and filtering cells based on specific criteria. CellxGene VIP (Visualization In Plugin) is a frontend interactive visualization plugin of cellxGene framework, which has dramatically expanded the capabilities of the base tool, such as generating quality control and high-resolution analytical plots with highly customizable settings in real-time.


Cellenics is a cloud-based single-cell RNA-seq analysis software that allows users to explore and analyze their dataset without prior programming knowledge. It has a user-friendly graphical interface divided into four components – data management, data processing, data exploration, and plots and tables.


Rosalind is a cloud-based bioinformatics platform designed for life science researchers. The software is optimized for 10x Genomics Chromium single-cell library kits. ROSALIND also supports the analysis of cell clusters created in the 10x Loupe Browser.

Loupe Cell Browser

Loupe Browser is a desktop application that provides interactive visualization functionality to analyze data from different 10x Genomics solutions. Loupe Browser allows users to quickly interrogate different views of 10x Genomics data to quickly gain insights into the underlying biology.

Feature Cellxgene VIP Cellenics Rosalind Loupe Cell Browser
Type of Application Desktop/Web Web Web Desktop
Open source version available Yes Yes No No
Data import format h5ad .tsv and .mtx Raw FASTQ files and processed counts data .cloupe
Multiple single cell technologies supported 10X only Yes 10X only 10X only
Species Supported All Human (Homo Sapiens), Mouse (Mus Musculus) Human (Homo Sapiens), Mouse (Mus Musculus), and Rat (Rattus Norvegicus) Human (Homo Sapiens), Mouse (Mus Musculus)
Plots that can be generated UMAP/t-SNE, bivariate plots, violin, heatmap, dot plot, track plot, density plot, density scatter, SanKey diagram, DEG and pre-computed DEG. Frequency plot for cell sets, volcano plot, dot plot, violin plot, marker heatmap, and custom heatmap UMAP/t-SNE, feature plots in 2D and 3D, correlation plots, heatmap, and violin plot UMAP/t-SNE, heatmap, violin plot, feature plot.
Data sharing among researchers Yes Limited Yes No
In-depth data exploration Yes Yes Limited Yes
Other features Command Line Interface (CLI) by programming in Python and/or R directly. Integration with NextFlow
  • Deep data exploration available via 50+ knowledge bases
  • Virtual rooms allow real-time collaboration between researchers
Supports integration with ATAC-seq, CITE-seq, and VDJ sequencing data

Despite the intimidating nature of scRNA-seq analysis, working with single-cell data is simple, even for those without coding knowledge if you have the right tools. Most importantly, there is no universal decision when choosing a single-cell data analysis software, as they all have pros and cons.

However, CellxGene VIP on Elucidata’s Polly can accelerate scRNA-seq analysis as Polly has the world’s most extensive collection of highly curated ML-ready single-cell data. These single-cell datasets have been metadata harmonized, which smoothens the scRNA-seq data visualization and analysis process. The input file for the CellxGene application (h5ad format) is hosted on our platform for each dataset. Researchers can use GUI or programmatic interface at their convenience to access, visualize and analyze these highly curated datasets using CellxGene through the Polly platform.

If you want to find out more, reach out to us.

Blog Categories

Blog Categories

Request Demo