Single Cell Gene Expression Analysis

ROSALIND for Single Cell Gene Expression Analysis is a cloud-based, end-to-end web platform that transforms how scientists process, interpret, and collaborate on single cell RNA-seq data. Optimized for 10X Genomics Chromium Single Cell Library Kits and designed to handle data from FASTQ files through to cell cluster interpretation, ROSALIND empowers researchers in oncology, immunology, regenerative medicine, drug discovery, and related fields to identify differentially expressed genes, novel cell types, and biological pathways — all without requiring bioinformatics expertise or command-line programming.

The platform reimagines single cell analysis as an intuitive, immersive web experience, connecting researchers to their data, knowledge bases, and team members at every stage of the workflow. ROSALIND supports multi-omic comparisons, enabling single cell data to be analyzed alongside bulk RNA-seq, ATAC-seq, and ChIP-seq datasets for deeper biological understanding.

Single Cell Analysis Capabilities

• Optimized for 10X Genomics Chromium Single Cell Library Kits with support for a broad library of sample and library preparation kits
• Fully automated processing of FASTQ files through an advanced analysis pipeline
• Intelligent quality control with automated contamination detection and outlier identification
• Guided experiment design wizard or attribute file upload via CSV
• Metadata recording with NCBI BioProject and BioSample model support, plus custom attribute creation
• Seurat and K-Means clustering methods with automated gene clustering in differential expression heatmaps
• Assisted cell type identification based on top marker gene expression using integrated knowledge bases
• Setup of cluster comparisons using biological attributes across samples, experiments, and multi-omic datasets
• Covariate and batch correction capabilities
• Gene filters to adjust expression cut-offs
• Interactive non-linear dimensional reduction plots including T-SNE and UMAP
• Rich visualizations including bar charts, donut charts, heatmaps, volcano plots, MA plots, and box plots
• Interpretation via Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA)
• Exploration of pathways, cell types, gene ontology, diseases, and drug interactions through 50+ integrated knowledge bases
• Multi-omic analyses across experiment and assay types
• Download of publication-ready figures
• Secure storage of results and raw data files
• Real-time collaboration and results sharing

Five-Step Workflow

1. Experiment Design: Researchers create a new experiment using a guided wizard that captures biological objectives, sample attributes, and analysis parameters. ROSALIND supports NCBI BioProject and BioSample models and allows custom attributes to minimize differential expression errors. The platform automatically calibrates the analysis pipeline to the specific library preparation kit used, accounting for unique characteristics such as unique molecular identifiers (UMIs).
2. Quality Control: ROSALIND's Quality Control Intelligence automatically evaluates key metrics including Q30 scores, alignment rates, ribosomal content, duplicate rates, sample correlation, gene coverage, MDS, and PCA. Non-aligning reads are assessed for contamination, and additional single cell metrics such as number of cells, average reads per cell, and median reads per cell are provided. Researchers can flag and remove outlier samples before proceeding to discovery.
3. Accessing Results: Following quality control review, all experiment results — including comprehensive clustering analysis — are fully accessible with a valid access subscription. No additional unlock steps are required, allowing researchers to move directly into visualization and interpretation.
4. Analysis and Discovery: ROSALIND provides a comprehensive single cell discovery dashboard featuring cluster proportion comparisons, interactive T-SNE/UMAP plots, identification of differentially expressed cluster biomarkers, and automated cell type classification. Researchers can annotate clusters from multiple clustering methods, set up comparisons between cell clusters within or across samples, apply gene lists and signatures, and explore top pathways, gene ontology, diseases, and drug interactions. New comparisons and meta-analyses — including cross-experiment and multi-omic analyses — can be added at any time and are available within minutes.
5. Collaboration and Data Sharing: ROSALIND Spaces provides virtual data rooms where scientists and collaborators worldwide can interactively explore shared experiments in real time, similar to working with Google Docs. Participants can add experiments, explore pathways, change cut-offs, add meta-analyses, and create new comparisons within the shared environment. Real-time activity feeds and historical reports keep all team members aligned without the need to transfer large files.

Platform Highlights

• Designed for scientists, eliminating the need to learn bioinformatics, command-line programming, or biostatistics
• Massively scalable cloud computing capable of analyzing thousands of single cells in parallel
• Clean, intuitive user interface enabling rapid onboarding with minimal training
• Graph-accelerated knowledge bases for intelligent cell type identification and cluster annotation
• Built-in, industry-standard bioinformatics pipelines with published methodologies
• Permissions-controlled collaborative spaces built for team science
• All communications and data transfers are encrypted and secured

ROSALIND is available to pharma, biotech, and academic researchers through Enterprise, Professional, and Academic subscriptions, as well as specialized licenses for service providers, contract research organizations (CROs), and academic core labs. The platform integrates with 50+ knowledge bases and supports multi-omic dataset exploration, making it a comprehensive hub for single cell and genomics research.