Identify Variable Genes in Expression Data — find_variable

Identifies variable genes from a gene expression dataset using specified selection criteria. Supports multiple methods, including expression thresholding and variability estimation via median absolute deviation (MAD).

Usage

find_variable_genes(
  eset,
  data_type = c("count", "normalized"),
  methods = c("low", "mad"),
  prop = 0.7,
  quantile = c(0.75, 0.5, 0.25),
  min.mad = 0.1,
  feas = NULL
)

Arguments

eset: Numeric matrix. Gene expression data (genes as rows, samples as columns).
data_type: Character. Type of data: `"count"` or `"normalized"`. Default is `"count"`.
methods: Character vector. Methods for gene selection: `"low"`, `"mad"`. Default is `c("low", "mad")`.
prop: Numeric. Proportion of samples in which a gene must be expressed. Default is 0.7.
quantile: Numeric. Quantile threshold for minimum MAD (0.25, 0.5, 0.75). Default is 0.75.
min.mad: Numeric. Minimum allowable MAD value. Default is 0.1.
feas: Character vector or `NULL`. Additional features to include. Default is `NULL`.

Value

Matrix subset of `eset` containing variable genes.

Author

Dongqiang Zeng

Examples

eset_tme_stad <- load_data("eset_tme_stad")
eset <- find_variable_genes(
  eset = eset_tme_stad,
  data_type = "normalized",
  methods = "mad",
  quantile = 0.25
)
#> ℹ Log2 transformation not necessary (data appears to already be log-scaled)
#> ℹ min.mad = 0.1
#> ℹ Range of MAD: 0.1 to 2.22
#> ℹ 25% of variables will be filtered out...