Identify Variable Genes in Expression Data

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 = 0.75,
  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

# Simulate data
set.seed(123)
sim_eset <- matrix(rnorm(100 * 20), 100, 20)
rownames(sim_eset) <- paste0("Gene", 1:100)
colnames(sim_eset) <- paste0("Sample", 1:20)

# Identify variable genes
eset_var <- find_variable_genes(
  eset = sim_eset,
  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: 0.41 to 1.72
#> ℹ 25% of variables will be filtered out...
head(eset_var)
#>           Sample1     Sample2    Sample3    Sample4     Sample5     Sample6
#> Gene3  1.55870831 -0.24669188 -0.2651451 -0.9385387 -0.63474826  1.02678506
#> Gene4  0.07050839 -0.34754260  0.5431941 -1.0525133 -0.02884155  0.75106130
#> Gene5  0.12928774 -0.95161857 -0.4143399 -0.4371595  0.67069597 -1.50916654
#> Gene6  1.71506499 -0.04502772 -0.4762469  0.3311792 -1.65054654 -0.09514745
#> Gene7  0.46091621 -0.78490447 -0.7886028 -2.0142105 -0.34975424 -0.89594782
#> Gene8 -1.26506123 -1.66794194 -0.5946173  0.2119804  0.75640644 -2.07075107
#>           Sample7    Sample8     Sample9    Sample10    Sample11    Sample12
#> Gene3 -0.03333034 -0.6930946  0.85520221  0.29959368 -0.01798024 -0.93656903
#> Gene4 -1.51606762  0.1188494  1.15293623  1.63905191 -0.13217513 -1.40078743
#> Gene5  0.79038534 -1.3647095  0.27627456  1.08461701 -2.54934277  0.16027754
#> Gene6 -0.21073418  0.5899827  0.14410466 -0.62456747  1.04057346 -0.27396237
#> Gene7 -0.65674293  0.2893440 -0.07562508  0.82592290  0.24972574 -0.98553911
#> Gene8 -1.41202579 -0.9042150  2.16141585 -0.04856835  2.41620737  0.08393068
#>         Sample13   Sample14   Sample15   Sample16   Sample17   Sample18
#> Gene3  0.8515247 -1.1477708  0.7148484 -0.9020980 -0.4539977 -3.0478609
#> Gene4 -0.7479300  0.3543522 -0.4316611  0.6270687 -0.5938646  1.8686555
#> Gene5  0.6302398  0.4247998  0.2276149  1.1203550 -1.7103797  1.7904242
#> Gene6  1.0966616  0.6483474  1.2949458  2.1272136 -0.2094484 -1.1010817
#> Gene7 -0.9884429 -1.2198100  0.5783349  0.3661144  2.4787458 -0.1681075
#> Gene8  1.1079950  0.1072350  1.3646728 -0.8747814  0.9897022  1.3752753
#>         Sample19    Sample20
#> Gene3 -0.0573241 -0.03265845
#> Gene4  1.2567478  1.63675735
#> Gene5  1.5874541 -0.32904197
#> Gene6  0.3194815 -2.60403817
#> Gene7  0.3815916  0.51398379
#> Gene8 -0.2436449 -0.88646801