Feature Selection sub-tab in Feature Selection & Dimensionality Reduction tab 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 tab offers multiple methods (specified below) to compute the variable genes, which can be visualized in the comparison to the remaining features through a plot on right side.
|Seurat - vst||Butler et al., Nature Biotechnology 2018 & Stuart, Butler, et al., Cell 2019|
|Seurat - mean.var.plot||Butler et al., Nature Biotechnology 2018 & Stuart, Butler, et al., Cell 2019|
|Seurat - dispersion||Butler et al., Nature Biotechnology 2018 & Stuart, Butler, et al., Cell 2019|
|Scran - modelGeneVar||Lun ATL, McCarthy DJ, Marioni JC (2016). “A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor.” F1000Res., 5, 2122.|
Select Feature Selection & Dimensionality Reduction tab from the top menu. This workflow guide assumes that the data as been previously uploaded, filtered and normalized before proceeding with this tab.
Select Feature Selection sub-tab (selected by default) to open up the feature selection user-interface.
The Feature Selection sub-tab is divided into three panels namely, a) Compute HVG, b) Select and Subset, and c) Plot.
The working of sections a, b and c are described below:
The Compute HVG window allows the processing of highly variable genes by selecting an appropriate method either from
A numeric value indicating the number of features to identify must be set (default is
2000) and an
assay must be selected from the list of available
Once the highly variable genes have been computed in (a), subset of these features can be selected for downstream analysis. A numeric value (default is
100) can be input to set the number of genes that should be displayed in (b), labeled (highlighted in red) in the plot (c) and selected for further analysis in the succeeding tabs as a subset.