selectTargetGenes {TAPseq} | R Documentation

## Select target genes

### Description

Select target genes that serve as markers for cell populations using a linear model with lasso regularization. How well a selected set of target genes discriminates between cell populations can be assessed in an intuitive way using UMAP visualization.

### Usage

    selectTargetGenes(object, targets = NULL, expr_percentile = c(0.6, 0.99))

    plotTargetGenes(object, target_genes, npcs = 15)

### Arguments

`object` | Seurat object containing single-cell RNA-seq data from which best marker genes for different cell populations should be learned. Needs to contain population identities for all cell.
`targets` | Desired number of target genes. Approximately this many target genes will be returned. If set to NULL, the optimal number of target genes will be estimated using a cross-valdation approach. Warning: The number of target genes might end up being very large!
`expr_percentile` | Expression percentiles that candidate target genes need to fall into. Default is 60% to 99%, which excludes bottom 60% and top 1% expressed genes from markers.
`target_genes` | (character) Target gene names.
`npcs` | (integer) Number of principal components to use for UMAP.

### Value

A character vector containing selected target gene identifiers.

### Examples

    library(Seurat)

    # example of mouse bone marrow 10x gene expression data
    data("bone_marrow_genex")

    # identify approximately 100 target genes that can be used to identify cell populations
    target_genes <- selectTargetGenes(bone_marrow_genex, targets = 100)

    # automatically identify the number of target genes to best identify cell populations using
    # cross-validation. caution: this can lead to very large target gene panels!
    target_genes_cv <- selectTargetGenes(bone_marrow_genex)

    # create UMAP plots to compare cell type identification based on full dataset and selected 100
    # target genes
    plotTargetGenes(bone_marrow_genex, target_genes = target_genes)

[Package TAPseq version 1.4.0 Index]