New Features in Seurat 2.3.1
Support for UMAP
We now support Uniform Manifold Approximation and Projection (UMAP) for dimensional reduction and visualization. To run, you must first install the
umap-learn python package. Details on this package can be found here and for a more in depth discussion of the mathematics underlying UMAP, please see the arXiv paper here.
# download link: # https://www.dropbox.com/s/kwd3kcxkmpzqg6w/pbmc3k_final.rds?dl=0 pbmc <- readRDS("~/Downloads/pbmc3k_final.rds") pbmc <- RunUMAP(pbmc, reduction.use = "pca", dims.use = 1:10) DimPlot(pbmc, reduction.use = "umap")
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.
# install scater # https://bioconductor.org/packages/release/bioc/html/scater.html library(scater) library(cowplot)
# download from satija lab pbmc_sce <- Convert(from = pbmc, to = "sce") p1 <- plotExpression(object = pbmc_sce, features = "MS4A1", x = "ident") + theme(axis.text.x = element_text(angle = 45, hjust = 1)) p2 <- plotPCA(object = pbmc_sce, colour_by = "ident") plot_grid(p1, p2)