Select features with significant trends

Usage

select_trend_features(
  x = NULL,
  pseudotime = NULL,
  method = c("pretsa", "gam"),
  padjust_threshold = 0.05,
  n_candidates = NULL,
  statistics = NULL,
  ...
)

Arguments

x: Numeric matrix with observations in rows and features in columns, or NULL when supplying statistics.
pseudotime: Numeric coordinate per observation.
method: Fitting method: "pretsa" or "gam".
padjust_threshold: Strict adjusted P-value cutoff between zero and one.
n_candidates: Maximum number of selected features, or NULL for no cap.
statistics: Precomputed table with feature row names and padjust (or pvalue if padjust is absent).
...: Numerical options passed to thisutils::fit_trends().

Value

A list with features, statistics, and fit (NULL for precomputed input). Nonfinite P-values are excluded. Input order is retained unless the cap is exceeded; then the smallest P-values are selected with stable ties.

Examples

t <- seq(0, 1, length.out = 40)
x <- cbind(a = sin(t * 5), b = cos(t * 3))
select_trend_features(x, t)$features
#> [1] "a" "b"