This vignette demonstrates how to store and interact with dimensional reduction information (such as the output from
RunPCA) in Seurat v3.0. For demonstration purposes, we will be using the 2,700 PBMC object that is created in the first guided tutorial. You can download the pre-computed object here.
library(Seurat) pbmc <- readRDS(file = "../data/pbmc3k_final.rds")
In Seurat v3.0, storing and interacting with dimensional reduction information has been generalized and formalized into the
DimReduc object. Each dimensional reduction procedure is stored as a
DimReduc object in the
object@reductions slot as an element of a named list. Accessing these reductions can be done with the
[[ operator, calling the name of the reduction desired. For example, after running a principle component analysis with
object[['pca']] will contain the results of the PCA. By adding new elements to the list, users can add additional, and custom, dimensional reductions. Each stored dimensional reduction contains the following slots:
ProjectDim) are stored in this slot. Note that the cell loadings will remain unchanged after projection but there are now feature loadings for all feature
To access these slots, we provide the
## A dimensional reduction object with key PC_ ## Number of dimensions: 50 ## Projected dimensional reduction calculated: FALSE ## Jackstraw run: TRUE ## Computed using assay: RNA
head(Embeddings(pbmc, reduction = "pca")[, 1:5])
## PC_1 PC_2 PC_3 PC_4 PC_5 ## AAACATACAACCAC-1 -4.7296855 -0.5184265 -0.7623220 -2.3156790 -0.07160006 ## AAACATTGAGCTAC-1 -0.5174029 4.5918957 5.9091921 6.9118856 -1.96243034 ## AAACATTGATCAGC-1 -3.1891063 -3.4695154 -0.8313710 -2.0019985 -5.10442765 ## AAACCGTGCTTCCG-1 12.7933021 0.1007166 0.6310221 -0.3687338 0.21838204 ## AAACCGTGTATGCG-1 -3.1288078 -6.3481412 1.2507776 3.0191026 7.84739502 ## AAACGCACTGGTAC-1 -3.1088963 0.9262125 -0.6482331 -2.3244378 -2.00526763
head(Loadings(pbmc, reduction = "pca")[, 1:5])
## PC_1 PC_2 PC_3 PC_4 PC_5 ## PPBP 0.010990202 0.01148426 -0.15176092 0.10403737 0.003299077 ## LYZ 0.116231706 0.01472515 -0.01280613 -0.04414540 0.049906881 ## S100A9 0.115414362 0.01895146 -0.02368853 -0.05787777 0.085382309 ## IGLL5 -0.007987473 0.05454239 0.04901533 0.06694722 0.004603231 ## GNLY -0.015238762 -0.13375626 0.04101340 0.06912322 0.104558611 ## FTL 0.118292572 0.01871142 -0.00984755 -0.01555269 0.038743505
head(Stdev(pbmc, reduction = "pca"))
##  7.098420 4.495493 3.872592 3.748859 3.171755 2.545292
RunPCA (pca), and
RunTSNE (tsne), and representing dimensional reduction techniques commonly applied to scRNA-seq data. When using these functions, all slots are filled automatically.
We also allow users to add the results of a custom dimensional reduction technique (for example, multi-dimensional scaling (MDS), or zero-inflated factor analysis), that is computed separately. All you need is a matrix with each cell’s coordinates in low-dimensional space, as shown below.
Though not incorporated as part of the Seurat package, its easy to run multidimensional scaling (MDS) in R. If you were interested in running MDS and storing the output in your Seurat object:
# Before running MDS, we first calculate a distance matrix between all pairs of cells. Here we # use a simple euclidean distance metric on all genes, using scale.data as input d <- dist(t(GetAssayData(pbmc, slot = "scale.data"))) # Run the MDS procedure, k determines the number of dimensions mds <- cmdscale(d = d, k = 2) # cmdscale returns the cell embeddings, we first label the columns to ensure downstream # consistency colnames(mds) <- paste0("MDS_", 1:2) # We will now store this as a custom dimensional reduction called 'mds' pbmc[["mds"]] <- CreateDimReducObject(embeddings = mds, key = "MDS_", assay = DefaultAssay(pbmc)) # We can now use this as you would any other dimensional reduction in all downstream functions DimPlot(pbmc, reduction = "mds", pt.size = 0.5)