Generate Contrasts from a Feature Matrix (Optional PCA)
make_feature_contrasts.RdCreates contrasts based on a matrix where rows represent conditions and columns represent features (e.g., neural network embeddings, semantic features). Optionally performs PCA to reduce dimensionality.
Usage
make_feature_contrasts(
features,
labels = NULL,
use_pca = TRUE,
centre_pca = TRUE,
scale_pca = FALSE,
pve = 0.9,
n_pcs = NULL,
prefix = "Feat_"
)Arguments
- features
A numeric matrix (K x P) where K is the number of conditions and P is the number of features. Row names, if present, should correspond to condition labels. Column names are recommended.
- labels
Optional character vector of condition labels. If provided, rows of `features` matrix will be reordered to match this order. If NULL, the order from `rownames(features)` is used (if available).
- use_pca
Logical. If TRUE (default), performs Principal Component Analysis (PCA) on the features. If FALSE, uses the raw features directly.
- centre_pca
Logical. If `use_pca = TRUE`, should features be centered before PCA? (Default: TRUE) Note: Reordering of rows based on `labels` argument happens *before* PCA.
- scale_pca
Logical. If `use_pca = TRUE`, should features be scaled to unit variance before PCA? (Default: FALSE, as scaling can affect variance explained). Note: Reordering of rows based on `labels` argument happens *before* PCA.
- pve
Numeric (0 to 1). If `use_pca = TRUE`, selects the minimum number of principal components (PCs) needed to explain at least this proportion of variance. Ignored if `n_pcs` is specified. (Default: 0.9)
- n_pcs
Integer. If `use_pca = TRUE`, selects exactly this number of principal components. Takes precedence over `pve`. (Default: NULL)
- prefix
Character string to prepend to column names of the output matrix (e.g., "Feat_", "PCA_"). (Default: "Feat_")
Value
A numeric matrix (K x Q) where K matches the number of conditions/labels and Q is the number of selected features or principal components. Rows are ordered according to `labels` or `rownames(features)`. Columns are named using the `prefix` and either the original feature names (if `use_pca=FALSE`) or component numbers (e.g., "PCA_PC1", "PCA_PC2").
Examples
# Example feature matrix (4 conditions, 5 features)
feat_mat <- matrix(rnorm(20), nrow = 4,
dimnames = list(paste0("Cond", 1:4), paste0("F", 1:5)))
# Use raw features (first 3)
C_raw <- make_feature_contrasts(feat_mat[, 1:3], use_pca = FALSE, prefix="RawFeat_")
print(C_raw)
#> RawFeat_F1 RawFeat_F2 RawFeat_F3
#> Cond1 -0.1192940 2.67029247 -1.1008429
#> Cond2 0.2962191 0.07558749 -0.9032616
#> Cond3 -0.3867527 -0.27008920 1.0620304
#> Cond4 -0.5690379 0.56529799 0.0182128
# Use PCA, selecting top 2 PCs
C_pca <- make_feature_contrasts(feat_mat, use_pca = TRUE, n_pcs = 2, prefix="PCA_")
print(C_pca)
#> PCA_PC1 PCA_PC2
#> Cond1 2.72244071 -0.001983375
#> Cond2 -0.66942072 -1.129628508
#> Cond3 -2.11147684 0.376485036
#> Cond4 0.05845684 0.755126847
# Use PCA, selecting >= 80% variance explained
C_pca_pve <- make_feature_contrasts(feat_mat, use_pca = TRUE, pve = 0.8, prefix="PCA_")
print(C_pca_pve)
#> PCA_PC1
#> Cond1 2.72244071
#> Cond2 -0.66942072
#> Cond3 -2.11147684
#> Cond4 0.05845684
# Reorder based on labels
C_pca_reorder <- make_feature_contrasts(feat_mat, labels=c("Cond3", "Cond1", "Cond4", "Cond2"),
use_pca = TRUE, n_pcs = 2, prefix="PCA_")
print(C_pca_reorder)
#> PCA_PC1 PCA_PC2
#> Cond3 2.11147684 -0.376485036
#> Cond1 -2.72244071 0.001983375
#> Cond4 -0.05845684 -0.755126847
#> Cond2 0.66942072 1.129628508