Meet the Guided scRNAseq Analysis

Meet the Guided scRNAseq Analysis
Christopher Li
CEO / Co-founder
Meet the Guided scRNAseq Analysis
Single Cell RNA sequencing (scRNAseq) is a powerful technique that has ushered in a new era of transcriptomic research. By exploring the transcriptomic profile at a cellular resolution, scientists are able to tackle new challenges in fields such as cancer research and developmental biology.

Analyzing scRNAseq data is a time and resource intensive operation. Processing the raw sequencing data requires strong programming skills and access to computing infrastructure. Both are not easily accessible by scientists who want to work with scRNAseq data. However, with scRNAseq becoming increasingly popular in the research community, we are seeing a rise in open source and commercial tools created to help scientists process their raw data and generate the expression matrix without having to manage servers or write code.

But processing data is not the same as analyzing data.

Generating the matrix is just one step, and arguably the easiest step, of the entire scRNAseq analysis. As it turns out, you need to explore, evaluate, iterate, and make critical decisions about your data, before you even get to the final results.

So let’s focus on the downstream analysis.

When analyzing scRNAseq data, each subsequent step is affected by data driven decisions made in the previous step. For example, during data cleaning, we need to evaluate quality control metrics to figure out how we should set cutoffs and this can impact statistical analyses and downstream visualizations. In traditional black box solutions, it is a challenge to go back and change the parameters once they’ve been set. This constrains scientists to complete the entire analysis, only to realize that the first set of parameters they chose were the incorrect ones. But imagine if you could jump between steps in the analysis, modify parameters and observe results in real-time?

Introducing the Guided scRNAseq Analysis

An interactive step-by-step analysis that will take you from raw scRNAseq data to interpretation in a single platform.  Although we are marrying the steps to process and analyze this data from beginning to end, the true star of the show is the analysis itself.

We wanted to offer researchers the ability to immerse themselves in a dynamic interface where they can quickly iterate upon their findings and adjust parameters as needed, without ever having to re-do an analysis from scratch. And to top it off, the solution is designed for both experienced users and scientists who are just starting to work with scRNAseq data.

Jumping into the features

To start, we’re supporting household names like Cell Ranger and Seurat to analyze 10X Single Cell Expression data. Let's see what you can accomplish:

Set your Cell Ranger parameters before launching your pipeline

Generate an expression matrix from raw reads using Cell Ranger

Launch a pipeline to align your raw sequencing reads, without writing a single line of code. These pipelines are ready-to-use and sets smart default parameters based on your experimental design. And for those of you who are familiar with these tools, you can customize parameters as needed.

Filter unique feature counts, percent mitochondrial counts and reads per cell

Quality control and data cleaning using Seurat

scRNAseq data is noisy and requires data cleaning. Our solution takes your through a series of steps that streamlines data cleaning, and most importantly, provides you with transparency and control over how the data is cleaned.

Generate an elbow plot and choose the number of PCs and cluster resolution you'd like to proceed with

Compare any combination of clusters for differential expression testing

Identify meaningful cell clusters using Seurat

Automatically perform comparisons and differential expression testing between any combination of clusters. Apply dimensionality reduction techniques like PCA, t-SNE and UMAP to begin identifying meaningful groups of cells based on expression profiles. During this process, you will have an opportunity to evaluate, iterate, and try different analysis methods that best suits your research needs, without having to restart from scratch.

Explore cell identity using popular public knowledge bases, which are directly integrated into the platform

Assign cell identity to your clusters

Classify and assign cell clusters

Take a deep dive into your data and classify your cell clusters in an immersive and interactive interface. Perform enrichment analyses and compare cluster expression signatures against more than 50 knowledge bases including Reactome, KEGG, Gene Ontology and more, in real-time.

The Guided scRNAseq Analysis is designed to take you through each layer of the analysis and help you make informed decisions with confidence. You can run your first scRNAseq Guided Analysis within your 30-day free trial on the BioBox Platform (Sign Up Here). If you're new to scRNAseq analysis, we recommend emailing one of our team members at support@biobox.io to help you get started. To learn more about our Guided Analysis, read our Getting Started guide here.

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