SignacX and Seurat / SPRING integration

Source: vignettes/springintegration.Rmd

Usage

To make life easier, SignacX was integrated with Seurat (versions 3 and 4), and with SPRING. We provide a few vignettes:

SPRING

In the pre-print, we often used Signac integrated with SPRING. To reproduce our findings and to generate new results with SPRING, please visit the SPRING repository which has example notebooks and installation instructions, particularly for processing CITE-seq and scRNA-seq data from 10X Genomics. Briefly, Signac is integrated seamlessly with the output files of SPRING in R, requiring only a few functions:

# dir points to the "FullDataset_v1" directory generated by the SPRING Jupyter notebook
dir = "./FullDataset_v1" 

# load the expression data
E = CID.LoadData(dir)

# generate cellular phenotype labels
labels = Signac(E, spring.dir = dir)
celltypes = GenerateLabels(labels, E = E, spring.dir = dir)

# write cell types and Louvain clusters to SPRING
dat <- CID.writeJSON(celltypes, spring.dir = dir)

After running the above functions, cellular phenotypes and Louvain clusters are ready to be visualized with SPRING Viewer, which can be setup locally as described here.

Seurat

Another way to use Signac is with Seurat. In this vignette, we performed multi-modal analysis of CITE-seq PBMCs from 10X Genomics using Signac integrated with Seurat.

Note:

  • This same data set was also processed using SPRING in this notebook, and subsequently classified with Signac, which was used to generate SPRING layouts for these data in the pre-print (Figures 2-4), which is available for interactive exploration here.