PlotTSNE {scAlign} | R Documentation

## Creates tsne plot

### Description

Helper function to plot aligned data from the results of running scAlign().

### Usage

    PlotTSNE(
      object,
      data.use,
      labels.use = "scAlign.labels",
      cols = NULL,
      title = "",
      legend = "none",
      point.size = 3,
      seed = 1234,
      ...
    )

### Arguments

`object` | scAlign class object with aligned data
`data.use` | Specifies which alignment results to use.
`labels.use` | Specifies "dataset" or "celltype" labeling from object meta.data.
`cols` | Colours for plot
`title` | ggplot title
`legend` | Determines if legend should be drawn
`point.size` | Size of geom_point
`seed` | Random seed for reproducability
`...` | Additional arguments to Rtsne function
`labels` | Object labels

### Value

ggplot2 object

### Examples

     library(SingleCellExperiment)

     ## Input data, 1000 genes x 100 cells
     data = matrix( rnorm(1000*100,mean=0,sd=1), 1000, 100)
     rownames(data) = paste0("gene", seq_len(1000))
     colnames(data) = paste0("cell", seq_len(100))

     age    = c(rep("young",50), rep("old",50))
     labels = c(c(rep("type1",25), rep("type2",25)), c(rep("type1",25), rep("type2",25)))

     ctrl.data = data[,which(age == "young")]
     stim.data = data[,which(age == "old")]

     ## Build the SCE object for input to scAlign using Seurat preprocessing and variable gene selection
     ctrlSCE <- SingleCellExperiment(
                   assays = list(scale.data = data[,which(age == "young")]))

     stimSCE <- SingleCellExperiment(
                   assays = list(scale.data = data[,which(age == "old")]))

     ## Build the scAlign class object and compute PCs
     scAlignHSC = scAlignCreateObject(sce.objects = list("YOUNG"=ctrlSCE,
                                                         "OLD"=stimSCE),
                                      labels = list(labels[which(age == "young")],
                                                    labels[which(age == "old")]),
                                      pca.reduce = FALSE,
                                      cca.reduce = FALSE,
                                      project.name = "scAlign_Kowalcyzk_HSC")

     ## Run scAlign with high_var_genes
     scAlignHSC = scAlign(scAlignHSC,
                        options=scAlignOptions(steps=100,
                                               log.every=100,
                                               norm=TRUE,
                                               early.stop=FALSE),
                        encoder.data="scale.data",
                        supervised='none',
                        run.encoder=TRUE,
                        run.decoder=FALSE,
                        log.results=FALSE,
                        log.dir=file.path(tempdir(),'gene_input'),
                        device="CPU")

     ## Plot alignment for 3 input types
     example_plot = PlotTSNE(scAlignHSC,
                               "ALIGNED-GENE",
                               "scAlign.labels",
                               title="scAlign-Gene",
                               perplexity=30)

[Package scAlign version 1.8.0 Index]