Single Cell Gene Expression
Gene Expression: RNA-seq
A complete guide to RNA-seq data analysis
NanoString: Gene, miRNA & Protein Expression
A complete guide to NanoString nCounter data analysis and collaboration
Gene Regulation & Anti-Sense: Small RNA-seq
Small RNA-seq data analysis designed for the biologist
Histone Mark & Transcription Factor: ChIP-seq
Comprehensive ChIP-seq data analysis
Chromatin Accessibility: ATAC-Seq
Sharing & Collaboration
Accelerate teamwork by connecting experts and datasets anywhere in the world
Knowledge Graph and Search
Reimagining single cell analysis from import to interpretation as an intuitive web experience
ROSALIND transforms the analysis of Single Cell RNA-Seq with an end-to-end web-based experience for analysis, interpretation and collaboration. Interactive analyses of single cell clusters reveals biology of cells.
Automated clustering provides Cell Ranger and Seurat methods, so you can choose the clustering that fits best
Assisted cell type identification saves time with integrated knowledge bases
Dynamic plots interactively display T-SNE, U-Map, Bar Charts, Donuts and Heatmaps to explore single cell trends
Native collaboration capabilities facilitate data sharing and teamwork
Annotate single cell data seamlessly & interactively
ROSALIND intelligently gathers relevant knowledge to help you best identify & annotate cell types. Notes are securely maintained across cluster methods, so you can capture observations in any context without losing a beat.
Select clusters to see heatmaps with marker genes
Toggle dynamic plots to see cells by sample, or cluster
Adjust settings to see 10, 25 or 50 marker genes, change colors or customize plots
Mouse over donut plots to see sample mix & cell counts
Click on Cell Types to explore knowledge base results for assisted identification
Use mouse gestures or scrolling on the T-SNE & UMAP to zoom and pan across the plot
Visualize expression of marker genes & setup comparisons across any combination of clusters
Dive deeper into single cell clusters to explore gene expression using violin plots and interactive T-SNE and UMAP projections. Define cluster comparisons to see differentially expressed genes & pathway interpretation.
Immersive information on every data point helps to guide scientific inquiry
Select any gene to instantly visualize expression across every cluster
Create new comparisons by selecting desired cell clusters
Perform comparisons for any clustering method to see results from Cell Ranger or Seurat clustering, independently
Customize and download any plot for presentation with publication-ready figures
Analyzing your data is only half the battle. Getting the right plot and image to communicate your findings makes all the difference. ROSALIND simplifies single cell data visualization giving you the plots you need with the controls to adjust fonts, plot sizes, zoom into clusters, hide legends, and see results across all samples.
Customize each plot based on the context of your data to see genes, clusters, or samples
Use the navigation menu in the enhanced plot control center to toggle plot types
Fine-tune your plots using the options provided uniquely for each plot type
Collaborate on single cell analysis & annotation
Single cell provides unprecedented data and granularity into our biology and challenges our very understanding. Accelerate your single cell interpretation by collaborating in real-time with scientists across the globe.
Interactive activity feeds capture contributions and provide attribution to each team member
See changes to cluster annotation, sample annotation, cluster comparisons, gene cut-offs and more
Check out profile details for your collaborators and teammates
Quickly assess each collaborative contribution by mousing over the activity to see highlights
Instantly navigate to any activity by clicking on the item
Analyze single cell clusters just as easily as bulk RNA-Seq with the same powerful analysis and even deeper insights
Enjoy the thrill of seeing your differential expression results come to life as dynamic, interconnected plots and diagrams with gene lists, heatmaps, volcano & MA plots, and advanced covariate correction.
Expand the controls on the left to change color schemes, gene sorting and to create new filters and cut-offs
Use the bottom bar to pertinent gene information at your fingertips, including gene list management
Command-click or Control-Click (PC) to multi-select genes and customize plots
Evaluate top pathways and GO terms amongst 50+ knowledge bases by click on terms to instantly evaluate signature expression levels
Click on magnifiers to access additional details
Dive deeper into pathways & the networks connecting them
Discovery more and save valuable resources through the integrated pathway interpretation provided by the ROSALIND Knowledge Graph and integrated biomedical knowledge bases. Explore interactive pathway diagrams with detailed term descriptions, annotated fold-change colors, and gene heatmaps.
Explore interactive pathway diagrams to evaluate gene expression levels across pathways
Toggle knowledge bases with the list menu on the left
Click the download button to access publication-ready pathway diagrams as well as the associated heatmap and bar plots
Use breadcrumbs in the header to navigate back to prior levels for gene, term and knowledge base resources
Harness machine learning to analyze single cell clusters across comparisons, experiments and multi-omics datasets
ROSALIND Meta-Analysis combines multi-omics datasets and uses unsupervised machine learning coupled our advanced Knowledge Graph to intelligent uncover and interpret gene signatures
Single cell experiments are easily compared and contrasted with RNA-Seq, NanoString, ATAC-Seq and ChIP-Seq experiments
Up to 50 multi-omics datasets may be merged in a single meta-analysis
Navigate to and explore underlying experiment details by clicking on the comparison
Navigate to meta-analysis details to access advanced interpretation tools by clicking on the genome icon for the desired siganture
The study of gene expression on a cellular level provides valuable insights for complex cell populations, novel cell types, and the effects of treatments on cellular processes by quantifying the activity of RNA in individual cells within a sample. Scientists working in Oncology, Immunology, Regenerative Medicine, Drug Discovery and other areas of research often conduct experiments between healthy and disease states to identify differentially expressed genes and biological pathways to discover therapeutic targets. Comparisons between these differential patterns reveal unique gene signatures valuable for drug and diagnostic development.
ROSALIND is a cloud platform that connects researchers to their data and team members as well as knowledge bases and team members to aid in interpretation. ROSALIND provides intuitive workflows and analysis interfaces for single cell data:
· Experiment design and FASTQ Data Import
· Quality control
· Cluster annotation with assisted cell type identification
· Comparisons between cell clusters or bulk RNASeq data
· Interactive differential expression and pathway exploration
· Seamless collaboration with team members and collaborators
ROSALIND simplifies data analysis and works like a data hub interconnecting every stage of data interpretation. The ROSALIND Single Cell Gene Expression discovery experience enables visual exploration and self-investigation of experiment results to give researchers the freedom to annotate clusters, visualize cell type arrangements, adjust cut-offs, add comparisons, apply covariate corrections, and even find patterns across multiple datasets, without the need for bioinformatic expertise. There are five easy steps to performing Single Cell RNA-seq data analysis on ROSALIND.
Starting an Single Cell RNA-seq data analysis begins with creating a new experiment and capturing the experiment design. ROSALIND walks through the key aspects of an experiment in a guided experience to record biological objectives, sample attributes and analysis parameters. These details become the basis of the experiment discovery dashboard. Researchers who publish papers and work with NCBI public data know the importance of natively supporting NCBI data models. ROSALIND fully supports the NCBI BioProject and BioSample models for metadata assignment and sample attribute descriptions. ROSALIND also enables scientists to create custom attributes to describe biological behaviors in terms relevant to the experiment. Setup of comparisons is simplified by describing and annotating samples using these familiar terms. This methodology minimizes the risk of differential expression errors when selecting samples for comparison.
When analyzing raw FASTQ files, ROSALIND streamlines data analysis using an advanced pipeline for analysis that includes intelligent quality control with automatic contamination detection, identification of differentially expressed cluster markers, and deep pathway interpretation. Visit the technical specifications section to learn more about the ROSALIND Single Cell RNA-seq data analysis pipeline and available reference materials.
For Single Cell RNA-seq data analysis, an analysis pipeline must adjust for sample preparation and proprietary differences in library preparation kits used in the experiment. Not only is the kit selection important for targeting and capturing the desired transcriptomic elements, the analysis pipeline adjusts and optimizes for the kit’s unique characteristics, such as the presence of unique molecular identifiers (UMIs).
ROSALIND integrates and supports a broad library of sample and library preparation kits, automatically calibrating each analysis with the appropriate details. To learn more about supported kits, visit the technical specifications section. Featured kits and instrument partners are also listed below.
Researchers must be confident in the quality control phase before gathering insights from a Single Cell RNA-seq experiment, otherwise, the results of the analysis should not be trusted. Biology’s mysteries are elusive and complex. Time should not be lost chasing corrective measures for outliers, contamination, swapped samples, and the many other errors that can occur in the course of a well-designed experiment.
Some of the most important Quality Control metrics to verify are Q30 scores, alignment rates, ribosomal content, duplicate rates, sample correlation, gene coverage, and multidimensional scaling (MDS) or principal component analysis (PCA) for all samples. When ROSALIND detects low alignment, non-aligning reads are evaluated for possible contamination. Additional information on the number of cells, average reads per cell, and median reads per cell are available to ensure the success of Single Cell experiments.
ROSALIND Quality Control Intelligence identifies potential data quality issues and triages the data before presenting the results. This eliminates the needs for researchers to be experts in Sequencing quality control issues. Researchers can eliminate offending samples and the deleterious effects on results by identifying the sample as an outlier and move confidently into the discovery and exploration phase of results interpretation.
After a researcher has reviewed the quality control phase the interactive presentation of results is ready to begin. The next step is to unlock the experiment. ROSALIND calculates the quantity of Analysis Units (“AU”) required to unlock the results. For Single Cell RNA-seq Experiments, this is generally 2 AU per sample, however, this may differ based on experiment parameters. Single Cell Clustering Analysis results are unlocked at no additional cost. Account balances and quick links for acquiring more AU are directly accessible from the unlock screen. To learn more about Analysis Units, check out the Q&A in the section below, or visit the ROSALIND Store.
ROSALIND allows the identification and characterization of mixed cell populations through a Single Cell discovery experience complete with cluster proportion comparisons between samples, interactive non-linear dimensional reduction plots, (T-SNE/UMAP), identification of differentially expressed cluster biomarkers, and automated cell type classification. Researchers may annotate clusters from multiple clustering methods to discover new patterns within their samples and easily set up comparisons between multiple cell clusters within or across samples.
ROSALIND moves beyond the typical CSV file of differentially expressed genes by providing a comprehensive dashboard for differential expression analysis and interpretation of RNA-seq data. Convenient on-screen controls are easily accessible for visualizing clusters, modifying filters, adding covariant corrections, applying gene lists and signatures, and adjusting plot color palettes. The ROSALIND gene expression discovery experience features deep interpretation of top pathways, gene ontology diseases and drug interactions, as rich interactive plots that fill the screen and respond to interactions from the scientist, showing customizable heatmaps, volcano and MA plots as well as box and bar plots.
New comparisons and meta-analysis may be added at any time. Meta-analyses created can be cross experiments and multi-omic. Each of these perspectives are available within minutes of setup, reducing internal bioinformatic workload and enabling scientists to react fluidly by focusing directly on the science of the experiment.
The discovery process rarely ends with a single point of view from a single researcher’s opinion. ROSALIND Spaces enables true scientist-to-scientist collaboration through virtual data rooms where scientists and collaborators can come together on related datasets anywhere in the world to interactively explore shared experiments much like working with Google Docs. Researchers access a consistent version of the data, without the need to transfer unwieldy files or reinterpret origin files. All changes are interactive, instantly available, and viewable everywhere in the world (as authorized by the organization) with real-time activity feeds and historical reports. Spaces participants can add experiments, explore pathways, change cut-offs, add meta-analyses and add new comparisons all within the shared collaborative environment.
Spaces are virtual meeting rooms where scientists meet with niche experts, clients, and supporting teams to maximize the discovery value of every experiment and prepare for the next one.
Designed for the Scientist, so you can focus on the biology without having to wait months, or learn bioinformatics & command-line programming
Analyzes thousands of single cells in parallel utilizing massively scalable cloud-computing
Utilizing a clean, intuitive and immersive user interface, Scientists new to the platform ramp quickly with little training to focus on discovery
Graph-accelerated knowledge bases identify cell types and assist users in annotating cell clusters
Compare cell clusters across samples, experiments and multi-omic datasets to reveal deeper biological understanding.
Explore experiment results in high-quality, publiction-ready, interactive diagrams and plots
ROSALIND is designed for the Scientist, so you can focus on the biology and science without having to invest months and months trying to learn bioinformatics, programming or biostatistics
Built-in pipelines are tuned to utilize industry standard, widely published bioinformatics tools. For more information, review the ROSALIND specifications and method section
Easily share & collaborate across team members with secure, permissions-controlled spaces
Every communication and data transfer on ROSALIND is encrypted and secured. Multiple layers of data protection ensure availability
I am not a bioinformatician. Can I really perform my own analysis?
Absolutely and other scientists just like you run their own analyses on ROSALIND every day. To learn more how to get started, check out the ROSALIND Quick Start Guide here.
What types of Single Cell experiments are supported?
The ROSALIND Single Cell discovery experience currently supports RNA-seq Gene Expression experiments.
Can I import custom cluster annotations?
Yes. Custom Clusters can be imported into ROSALIND from 10X Genomics Loupe Cell Browser, Seurat, or other clustering programs.
What types of input files are supported?
For Single Cell Gene Expression experiments, FASTQ files are supported. Compressed FASTQs will have faster upload time. Supported file types: .FASTQ, .FASTQ.GZ
What is an Analysis Unit and how is it used on ROSALIND?
Samples that are processed on ROSALIND require Analysis Units to unlock the ROSALIND discovery experience. Analysis Units are already included in most subscriptions on ROSALIND. Additional Analysis Units may be purchased in packs of 10 or 50 from the ROSALIND Store. Analysis Units do not expire. A current subscription is required to utilize Analysis Units. Enterprise Subscriptions provide additional flexibility for high-volume environments. Please contact sales to learn more firstname.lastname@example.org
What is considered a Sample?
Any sample that is prepared for processing on an instrument is considered a Sample for ROSALIND. If a Scientist takes two (2) aliquots of an original sample to have replicates and prepares a library for each, this would be considered two (2) Samples on ROSALIND. On the other hand, a Sample may have multiple files associated with it, depending on how sequencing is performed. A single sample may be single-end, paired-end, and also multi-lane and will still be considered as one (1) Sample.
Can I download my results and plots?
Yes. All plots, diagrams, source and results files are downloadable on ROSALIND. Look for the Download buttons to access publication-ready figures as well as to download all experiment datasets.
Do you have an API for programmatic interfacing?
Yes. We provide API integration for Enterprise customers. This allows production teams to automate the upload, processing, and distribution of genomic datasets. API integration also includes Single-Sign-On (SSO) support.