Multiomics: Integrating single cell data sets to see the big picture

Posted By: Kariena-10x, on Aug 8, 2019 at 7:30 AM
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Scientists keep innovating to develop single cell technologies beyond measuring RNA. It’s now possible to capture large-scale single cell information about chromatin state, genomes, cell-surface proteins, genetic perturbations, and more. This rise in multiomics requires parallel innovations in single cell data analysis that will allow researchers to integrate and extract biological insights from these complex datasets. 

 

Each single cell data type, or modality, gives a unique perspective on cellular identity that’s complementary to RNA data. Single cell RNA sequencing for gene expression profiling can determine cell types. But to know the regulatory landscape that developed and maintains that cell type, you need measurements of chromatin state. To determine the cell’s functional output, you need to look at proteins. And to understand the cellular interactions, you need to define the spatial context where each cell resides. The technical challenge is to be able to measure as many of these things as possible. The computational challenge is to be able to put together these diverse sources of information into a coherent whole. 

 

At a recent 10x webinar, Dr. Rahul Satija of New York Genome Center and NYU explained how the new updates with Seurat v3 can robustly integrate multiple types of single cell and single nuclei data. First, he addressed how to better sort out technical noise from biological signal, because bulk normalization methods are suboptimal for single cell data. Seurat v3 normalizes single cell datasets in a way that removes technical variation, yet preserves biological heterogeneity. 

 

Next, he demonstrated the ability of Seurat v3 to harmonize different modalities of single cell data across technology, and even across species. The software merges massive single cell datasets by first connecting cells with similar biological states. These connections act as anchors to identify shared sources of variation — and not force alignment if data points are unique. This “apples-to-apples” comparison lets you use single cell gene expression profiles to put complementary biological measures in context. Dr. Satija also introduced Signac, a new extension of Seurat designed specifically for measuring single cell chromatin states. 

 

This webinar is now available for on-demand viewing. Check out the whole presentation, then try these updated tools yourself to study biological questions in your system of interest:

Watch on demand →

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