Introduction

singleCellTK offers a convenient way to compute and select the most variable features that show the highest biological variability to use them in the downstream analysis. The available methods to compute the HVG include seuratFindHVG and scranModelGeneVar, both of which essentially compute the variability statistics and store them into the rowData of the input SingleCellExperiment object. The getTopHVG method can retrieve the names of the top variable genes from these statistics from the input object. Furthermore, plotTopHVG method can be used to plot the top most variable genes.

General Workflow

A general workflow for the Feature Selection sub-tab is summarized in the figure below:

Workflow Guide

  1. Compute statistics for the highly variable genes using either seuratFindHVG method or scranModelGeneVar method:
sce <- seuratFindHVG(inSCE = sce, useAssay = "normalizedCounts", hvgMethod = "vst", hvgNumber = 2000)
sce <- scranModelGeneVar(inSCE = sce, assayName = "logNormCounts")
  1. Get names of top genes:
topGenes <- getTopHVG(inSCE = sce,method = "vst", n = 1000)
  1. Visualize top genes:
plotTopHVG(inSCE = sce, method = "vst", hvgList = topGenes, labelsCount = 10)