Interoperability between single-cell object formats

Converting to/from SingleCellExperiment

SingleCellExperiment is a class for storing single-cell experiment data, created by Davide Risso, Aaron Lun, and Keegan Korthauer, and is used by many Bioconductor analysis packages. Here we demonstrate converting the Seurat object produced in our 3k PBMC tutorial to SingleCellExperiment for use with Davis McCarthy’s scater package.

# download from satija lab https://www.dropbox.com/s/kwd3kcxkmpzqg6w/pbmc3k_final.rds?dl=0
pbmc <- readRDS(file = "../data/pbmc3k_final.rds")
pbmc.sce <- as.SingleCellExperiment(pbmc)
p1 <- plotExpression(pbmc.sce, features = "MS4A1", x = "ident") + theme(axis.text.x = element_text(angle = 45, 
    hjust = 1))
p2 <- plotPCA(pbmc.sce, colour_by = "ident")
CombinePlots(plots = list(p1, p2))

Seurat also allows conversion from SingleCellExperiment objects to Seurat objects; we demonstrate this on some publicly available data downloaded from a repository maintained by Martin Hemberg’s group.

# download from hemberg lab
# https://scrnaseq-public-datasets.s3.amazonaws.com/scater-objects/manno_human.rds
manno <- readRDS(file = "../data/manno_human.rds")
manno <- runPCA(manno)
manno.seurat <- as.Seurat(manno, counts = "counts", data = "logcounts")
# gives the same results; but omits defaults provided in the last line
manno.seurat <- as.Seurat(manno)
Idents(manno.seurat) <- "cell_type1"
p1 <- DimPlot(manno.seurat, reduction = "PCA", group.by = "Source") + NoLegend()
p2 <- RidgePlot(manno.seurat, features = "ACTB", group.by = "Source")
CombinePlots(plots = list(p1, p2))

Converting to/from loom

The loom format is a file structure imposed on HDF5 files designed by Sten Linnarson’s group. It is designed to efficiently hold large single-cell genomics datasets. For more details about the loom format, please see the loom file format specification.

pbmc.loom <- as.loom(pbmc, filename = "../output/pbmc3k.loom", verbose = FALSE)
pbmc.loom

## Class: loom
## Filename: /home/butlera/online_workflows/output/pbmc3k.loom
## Access type: H5F_ACC_RDWR
## Attributes: version, chunks, LOOM_SPEC_VERSION, assay, last_modified
## Listing:
##        name    obj_type dataset.dims dataset.type_class
##   col_attrs   H5I_GROUP         <NA>               <NA>
##  col_graphs   H5I_GROUP         <NA>               <NA>
##      layers   H5I_GROUP         <NA>               <NA>
##      matrix H5I_DATASET 2638 x 13714          H5T_FLOAT
##   row_attrs   H5I_GROUP         <NA>               <NA>
##  row_graphs   H5I_GROUP         <NA>               <NA>

# Always remember to close loom files when done
pbmc.loom$close_all()

Seurat can also read in loom files connected via loomR into a Seurat object; we demonstrate this on a subset of the Mouse Brain Atlas created by the Linnarson lab.

# download from linnarson lab
# https://storage.googleapis.com/linnarsson-lab-loom/l6_r1_immune_cells.loom
l6.immune <- connect(filename = "../data/l6_r1_immune_cells.loom", mode = "r")
l6.immune

## Class: loom
## Filename: /home/butlera/online_workflows/data/l6_r1_immune_cells.loom
## Access type: H5F_ACC_RDONLY
## Attributes: CreationDate, last_modified
## Listing:
##        name    obj_type  dataset.dims dataset.type_class
##   col_attrs   H5I_GROUP          <NA>               <NA>
##  col_graphs   H5I_GROUP          <NA>               <NA>
##      layers   H5I_GROUP          <NA>               <NA>
##      matrix H5I_DATASET 14908 x 27998          H5T_FLOAT
##   row_attrs   H5I_GROUP          <NA>               <NA>
##  row_graphs   H5I_GROUP          <NA>               <NA>

l6.seurat <- as.Seurat(l6.immune)
VlnPlot(l6.seurat, features = c("Sparc", "Ftl1", "Junb", "Ccl4"), ncol = 2, pt.size = 0.1)

# Always remember to close loom files when done
l6.immune$close_all()

For more details about interacting with loom files in R and Seurat, please see loomR on GitHub.

Converting to/from AnnData

AnnData provides a Python class, created by Alex Wolf and Philipp Angerer, that can be used to store single-cell data. This data format is also use for storage in their Scanpy package for which we now support interoperability. Seurat has a new function ReadH5AD to read the data from the H5AD files that AnnData uses.

# download from satija lab https://www.dropbox.com/s/ngs3p8n2i8y33hj/pbmc3k.h5ad?dl=0
pbmc3k <- ReadH5AD(file = "../data/pbmc3k.h5ad")
Idents(pbmc3k) <- "louvain"
p1 <- DimPlot(pbmc3k, label = TRUE) + NoLegend()
p2 <- VlnPlot(pbmc3k, features = c("CST3", "NKG7", "PPBP"), combine = FALSE)
CombinePlots(plots = c(list(p1), p2), ncol = 2, legend = "none")

We currently do not support writing to an H5AD file at this time; however, transferring data from Seurat to Scanpy can be done via loom files