Introduction

singleCellTK offers multiple functions to compute and visualize dimensionality reduction results. A summary of the available methods and visualization options are described below.


A brief summary of the two tabs is described below:

tSNE/UMAP

Implemented Algorithms from Packages:

Method Packages Reference
tSNE ? ?
UMAP ? ?
tSNE Seurat Butler et al., Nature Biotechnology 2018 & Stuart, Butler, et al., Cell 2019
UMAP Seurat Butler et al., Nature Biotechnology 2018 & Stuart, Butler, et al., Cell 2019

Visualizations Supported

Method 2-Dimensional Component Plot Elbow Plot JackStraw Plot Heatmap Plot
PCA \(\checkmark\) \(\checkmark\) \(\checkmark\) \(\checkmark\)
ICA \(\checkmark\) x x \(\checkmark\)
tSNE \(\checkmark\) x x x
UMAP \(\checkmark\) x x x

General Worflow


Workflow Guide

In general, the first step is to compute a dimensionality reduction (e.g. PCA) and then the second step is to visualize the computed results. The usage of functions to compute and visualize results is described below.

  1. Compute dimensionality reduction statistics using one of the available functions.
sce <- getTSNE(inSCE = sce,
               useAssay = "normCounts",
               reducedDimName = "tsne",
               perplexity = 30,
               n_iterations = 1000)

sce <- getUMAP(inSCE = sce,
               useAssay = "normCounts",
               reducedDimName = "umap",
               nNeighbors = 30,
               nIterations = 200,
               minDist = 0.01,
               alpha = 1)

sce <- scaterPCA(inSCE = sce,
              useAssay = "scaledCounts",
              reducedDimName = "pca")

sce <- seuratPCA(inSCE = sce,
                 useAssay = "scaledCounts",
                 reducedDimName = "pca",
                 nPCs = 10)

sce <- seuratICA(inSCE = sce,
                 useAssay = "scaledCounts",
                 reducedDimName = "ica",
                 nics = 10)

sce <- seuratRunTSNE(inSCE = sce,
                     useReduction = "pca",
                     reducedDimName = "tsne",
                     dims = 10,
                     perplexity = 30)

sce <- seuratRunUMAP(inSCE = sce,
                     useReduction = "pca",
                     reducedDimName = "umap",
                     dims = 10,
                     minDist = 0.3,
                     nNeighbors = 30,
                     spread = 1)
  1. Visualize the dimensionality reduction results using of the available visualization options.
#To plot a simple 2D component plot for any of the 4 methods i.e. PCA, ICA, tSNE and UMAP
seuratReductionPlot(
  inSCE = sce,
  useReduction = "pca")

#To visualize a JackStraw plot
sce <- seuratComputeJackStraw(
  inSCE = sce,
  useAssay = "scaledCounts"
)

seuratJackStrawPlot(inSCE = sce)

#To visualize heatmap plot
seuratComputeHeatmap(
  inSCE = sce,
  useAssay = "scaledCounts",
  useReduction = "pca",
  dims = 10,
  nfeatures = 10)

#To visualize Elbow plot
seuratElbowPlot(
  inSCE = sce,
  reduction = "pca"
)

#Redundant functions (need appropriate use within a higher wrapper function)
#seuratHeatmapPlot()
#plotDimRed()
#plotHeatmapMulti()