How to Analyze Single Cell Expression with ROSALIND

Empowering Scientists with End-to-End Single Cell Interpretation

WHY STUDY SINGLE CELL GENE EXPRESSION 

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.

OVERVIEW

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

FIVE STEPS TO SUCCESS WITH SINGLE CELL DATA ANALYSIS

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.

1. EXPERIMENT DESIGN

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.

2. SINGLE CELL RNA-SEQ QUALITY CONTROL

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.

3. UNLOCKING RESULTS

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.

4. ANALYSIS & DISCOVERY

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.

5. COLLABORATION & DATA SHARING

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.