diff --git a/.Rbuildignore b/.Rbuildignore
index 540d634..05c9f00 100644
--- a/.Rbuildignore
+++ b/.Rbuildignore
@@ -13,3 +13,4 @@
^build_pkgdown_site\.R$
^CLAUDE\.md$
^\.claude$
+^RELEASENOTES\.md$
diff --git a/DESCRIPTION b/DESCRIPTION
index e50d604..8e031ec 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
Package: splikit
Title: Analysing RNA Splicing in Single-Cell RNA Sequencing Data
-Version: 2.3.1
+Version: 2.3.2
Authors@R:
person("Arsham", "Mikaeili Namini", , "arsham.mikaeilinamini@mail.mcgill.ca", role = c("aut", "cre"),
comment = c(ORCID = "0000-0002-9453-6951"))
diff --git a/NEWS.md b/NEWS.md
index 88dd836..1ed7220 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,3 +1,23 @@
+# splikit 2.3.2
+
+## New Features
+* **Repeated permutation null for `get_pseudo_correlation()`.** The null model
+ is now built by permuting the cells (columns) of `ZDB_matrix` and recomputing
+ the pseudo correlation `permutation_count` times (default `100`), instead of a
+ single permutation. This yields a per-event empirical null and adds the
+ columns `null_sd`, `n_perm_valid`, `emp_pvalue` (two-sided, `(b + 1) / (m + 1)`
+ corrected), and `emp_padj` (Benjamini-Hochberg). The full per-event draws are
+ attached as `attr(result, "null_draws")`.
+* **`permutation_seed` argument** for reproducible permutations. When supplied,
+ the RNG is seeded locally and the caller's global RNG stream is saved and
+ restored, so reproducibility does not perturb downstream randomness.
+* Both additions are exposed on the R6 method `SplikitObject$getPseudoCorrelation()`.
+
+## Behavioural Notes
+* `null_distribution` is now the mean of the per-event null draws; it equals the
+ original single null value when `permutation_count = 1`, preserving backward
+ compatibility. The observed `pseudo_correlation` is unchanged.
+
# splikit 2.3.1
## CRAN Compliance
diff --git a/R/SplikitObject.R b/R/SplikitObject.R
index d31ab82..e8d7b97 100644
--- a/R/SplikitObject.R
+++ b/R/SplikitObject.R
@@ -220,10 +220,20 @@ SplikitObject <- R6::R6Class("SplikitObject",
#' Must have same dimensions as m1.
#' @param metric R-squared metric: "CoxSnell" or "Nagelkerke" (default: "CoxSnell").
#' @param suppress_warnings Suppress computation warnings (default: TRUE).
+ #' @param permutation_count Integer. Number of cell-permutation null draws per
+ #' event used to build the empirical null (default: 100L). Use 1L for the
+ #' original single-permutation behaviour.
+ #' @param permutation_seed Optional single numeric value seeding the
+ #' permutations for reproducibility without disturbing the global RNG
+ #' (default: NULL).
#'
- #' @return A data.table with event names, pseudo_correlation, and null_distribution.
+ #' @return A data.table with event names, pseudo_correlation, null_distribution,
+ #' null_sd, n_perm_valid, emp_pvalue, and emp_padj, plus the full null draws
+ #' in attr(result, "null_draws").
getPseudoCorrelation = function(ZDB_matrix, metric = "CoxSnell",
- suppress_warnings = TRUE) {
+ suppress_warnings = TRUE,
+ permutation_count = 100L,
+ permutation_seed = NULL) {
private$ensureM2Computed()
@@ -249,7 +259,9 @@ SplikitObject <- R6::R6Class("SplikitObject",
m1_inclusion = self$m1,
m2_exclusion = self$m2,
metric = metric,
- suppress_warnings = suppress_warnings
+ suppress_warnings = suppress_warnings,
+ permutation_count = permutation_count,
+ permutation_seed = permutation_seed
)
# Warn about NA removal
diff --git a/R/general_tools.R b/R/general_tools.R
index 1798ff9..b1bca5c 100644
--- a/R/general_tools.R
+++ b/R/general_tools.R
@@ -20,13 +20,31 @@ utils::globalVariables(c("unique_mapped", "i", "j", "x_1", "x_tot", "x_2", "grou
#' @param metric Character string specifying which R² metric to compute. Options are "CoxSnell" (default) or "Nagelkerke".
#' @param suppress_warnings Logical. If \code{TRUE} (default), suppresses warnings during computation (e.g., due to ill-conditioned inputs).
#' @param verbose Logical. If \code{TRUE}, prints progress and informational messages. Default is \code{FALSE}.
+#' @param permutation_count Integer. Number of times the null model is generated by
+#' permuting the cells (columns) of `ZDB_matrix` and recomputing the pseudo
+#' correlation. Each permutation yields one null value per event, so the n draws
+#' form a per-event empirical null. Defaults to `100L`. Use `1L` to reproduce the
+#' original single-permutation behaviour. Values of 1000 or more are recommended
+#' for reliable empirical FDR estimation (the smallest achievable empirical
+#' p-value is approximately `1 / (permutation_count + 1)`).
+#' @param permutation_seed Optional single numeric value. If supplied, the random
+#' number generator is seeded with it before the permutations are drawn, making
+#' the null distribution (and therefore the empirical p-values) reproducible. The
+#' caller's global RNG stream is saved and restored, so passing a seed does not
+#' perturb downstream randomness. Defaults to `NULL` (use the ambient RNG state).
#'
#' @return A `data.table` with the following columns:
#' \describe{
#' \item{event}{The event names from `ZDB_matrix` rownames.}
#' \item{pseudo_correlation}{The computed pseudo R² correlation values using the specified metric.}
-#' \item{null_distribution}{Null correlation values from a permuted version of `ZDB_matrix`.}
+#' \item{null_distribution}{Mean of the per-event null correlation values across the `permutation_count` permutations (equal to the single null draw when `permutation_count = 1`).}
+#' \item{null_sd}{Standard deviation of the per-event null draws (`NA` when fewer than two valid draws are available).}
+#' \item{n_perm_valid}{Number of permutations that produced a non-`NA` null value for the event.}
+#' \item{emp_pvalue}{Two-sided empirical p-value, `(b + 1) / (n_perm_valid + 1)`, where `b` is the number of valid null draws whose absolute value is greater than or equal to the absolute observed correlation.}
+#' \item{emp_padj}{Benjamini-Hochberg adjusted `emp_pvalue` across the retained events.}
#' }
+#' The full matrix of per-event null draws (events x `permutation_count`) is attached
+#' as `attr(result, "null_draws")` for custom downstream analyses.
#'
#' @examples
#' \donttest{
@@ -40,6 +58,11 @@ utils::globalVariables(c("unique_mapped", "i", "j", "x_1", "x_tot", "x_2", "grou
#' eventdata <- m1_obj$event_data
#' m2_exclusion <- make_m2(m1_inclusion, eventdata)
#'
+#' # subset to a few hundred cells to keep the example quick
+#' cells <- seq_len(min(250, ncol(m1_inclusion)))
+#' m1_inclusion <- m1_inclusion[, cells]
+#' m2_exclusion <- m2_exclusion[, cells]
+#'
#' # creating a dummy ZDB
#' ZDB_matrix <- matrix(rnorm(n = (nrow(m1_inclusion) * ncol(m1_inclusion)), sd = 7),
#' nrow = nrow(m1_inclusion),
@@ -50,20 +73,29 @@ utils::globalVariables(c("unique_mapped", "i", "j", "x_1", "x_tot", "x_2", "grou
#' # Example with dense matrices (backward compatible)
#' m1_dense <- as.matrix(m1_inclusion)
#' m2_dense <- as.matrix(m2_exclusion)
-#' pseudo_r_square_cox <- get_pseudo_correlation(ZDB_matrix, m1_dense, m2_dense)
+#' pseudo_r_square_cox <- get_pseudo_correlation(ZDB_matrix, m1_dense, m2_dense,
+#' permutation_count = 20,
+#' permutation_seed = 1)
#' print(pseudo_r_square_cox)
#'
#' # Example with sparse matrices (more memory efficient)
-#' pseudo_r_square_sparse <- get_pseudo_correlation(ZDB_matrix, m1_inclusion, m2_exclusion)
+#' pseudo_r_square_sparse <- get_pseudo_correlation(ZDB_matrix, m1_inclusion, m2_exclusion,
+#' permutation_count = 20,
+#' permutation_seed = 1)
+#'
+#' # Per-event empirical p-values and the full null draws
+#' head(pseudo_r_square_sparse[, c("event", "pseudo_correlation", "emp_pvalue")])
+#' dim(attr(pseudo_r_square_sparse, "null_draws"))
#'
#' # Example using Nagelkerke R-squared instead of Cox-Snell
#' pseudo_r_square_nagel <- get_pseudo_correlation(ZDB_matrix, m1_inclusion, m2_exclusion,
-#' metric = "Nagelkerke")
+#' metric = "Nagelkerke",
+#' permutation_count = 20)
#' print(pseudo_r_square_nagel)
#' }
#'
#' @export
-get_pseudo_correlation <- function(ZDB_matrix, m1_inclusion = NULL, m2_exclusion = NULL, metric = "CoxSnell", suppress_warnings = TRUE, verbose = FALSE) {
+get_pseudo_correlation <- function(ZDB_matrix, m1_inclusion = NULL, m2_exclusion = NULL, metric = "CoxSnell", suppress_warnings = TRUE, verbose = FALSE, permutation_count = 100L, permutation_seed = NULL) {
# Handle SplikitObject input
if (inherits(ZDB_matrix, "SplikitObject")) {
@@ -88,6 +120,19 @@ get_pseudo_correlation <- function(ZDB_matrix, m1_inclusion = NULL, m2_exclusion
# Validate metric parameter
metric <- match.arg(metric, choices = c("CoxSnell", "Nagelkerke"))
+ # Validate permutation controls
+ if (!is.numeric(permutation_count) || length(permutation_count) != 1L ||
+ is.na(permutation_count) || permutation_count < 1) {
+ stop("permutation_count must be a single positive integer.", call. = FALSE)
+ }
+ permutation_count <- as.integer(permutation_count)
+
+ if (!is.null(permutation_seed) &&
+ (!is.numeric(permutation_seed) || length(permutation_seed) != 1L ||
+ is.na(permutation_seed))) {
+ stop("permutation_seed must be NULL or a single numeric value.", call. = FALSE)
+ }
+
# Check ZDB_matrix (must be dense)
if (!is.matrix(ZDB_matrix)) stop("ZDB_matrix must be a dense matrix.")
@@ -108,7 +153,25 @@ get_pseudo_correlation <- function(ZDB_matrix, m1_inclusion = NULL, m2_exclusion
if (verbose) message("Input dimension check passed. Proceeding with computation.")
- # Define the computation function
+ # Seed the RNG for reproducible permutations without disturbing the caller's
+ # global RNG stream (state is saved here and restored on exit).
+ if (!is.null(permutation_seed)) {
+ if (exists(".Random.seed", envir = globalenv(), inherits = FALSE)) {
+ old_seed <- get(".Random.seed", envir = globalenv(), inherits = FALSE)
+ on.exit(assign(".Random.seed", old_seed, envir = globalenv()), add = TRUE)
+ } else {
+ on.exit(
+ if (exists(".Random.seed", envir = globalenv(), inherits = FALSE)) {
+ rm(".Random.seed", envir = globalenv())
+ },
+ add = TRUE
+ )
+ }
+ set.seed(permutation_seed)
+ }
+
+ # Define the computation function. Returns the observed correlations, the event
+ # names, and the matrix of per-event null draws (events x permutation_count).
run_computation <- function() {
# Determine which C++ function to call based on matrix types
is_m1_sparse <- inherits(m1_inclusion, "Matrix")
@@ -126,32 +189,36 @@ get_pseudo_correlation <- function(ZDB_matrix, m1_inclusion = NULL, m2_exclusion
"dMatrix")
}
- if (!is_m1_sparse && !is_m2_sparse) {
- # Both dense - ensure they are matrices
- correls <- cppBetabinPseudoR2(Z = ZDB_matrix,
- m1 = as.matrix(m1_inclusion),
- m2 = as.matrix(m2_exclusion),
- metric = metric)
- } else if (is_m1_sparse && is_m2_sparse) {
- # Both sparse
- correls <- cppBetabinPseudoR2_sparse(Z = ZDB_matrix,
- m1 = m1_inclusion,
- m2 = m2_exclusion,
- metric = metric)
- } else if (is_m1_sparse && !is_m2_sparse) {
- # m1 sparse, m2 dense
- correls <- cppBetabinPseudoR2_mixed1(Z = ZDB_matrix,
- m1 = m1_inclusion,
- m2 = as.matrix(m2_exclusion),
- metric = metric)
- } else {
- # m1 dense, m2 sparse
- correls <- cppBetabinPseudoR2_mixed2(Z = ZDB_matrix,
- m1 = as.matrix(m1_inclusion),
- m2 = m2_exclusion,
- metric = metric)
+ # Dispatch helper: compute the per-event pseudo correlation for a given Z,
+ # selecting the kernel that matches the m1/m2 matrix storage types. Shared
+ # by the observed pass and every null permutation.
+ compute_kernel <- function(Zmat) {
+ if (!is_m1_sparse && !is_m2_sparse) {
+ cppBetabinPseudoR2(Z = Zmat,
+ m1 = as.matrix(m1_inclusion),
+ m2 = as.matrix(m2_exclusion),
+ metric = metric)
+ } else if (is_m1_sparse && is_m2_sparse) {
+ cppBetabinPseudoR2_sparse(Z = Zmat,
+ m1 = m1_inclusion,
+ m2 = m2_exclusion,
+ metric = metric)
+ } else if (is_m1_sparse && !is_m2_sparse) {
+ cppBetabinPseudoR2_mixed1(Z = Zmat,
+ m1 = m1_inclusion,
+ m2 = as.matrix(m2_exclusion),
+ metric = metric)
+ } else {
+ cppBetabinPseudoR2_mixed2(Z = Zmat,
+ m1 = as.matrix(m1_inclusion),
+ m2 = m2_exclusion,
+ metric = metric)
+ }
}
+ # Observed correlation (computed once; independent of the permutations).
+ correls <- compute_kernel(ZDB_matrix)
+
if (is.null(rownames(ZDB_matrix))) {
warning("ZDB_matrix has no row names; assigning default event names.")
events <- paste0("Event_", seq_len(nrow(ZDB_matrix)))
@@ -159,44 +226,77 @@ get_pseudo_correlation <- function(ZDB_matrix, m1_inclusion = NULL, m2_exclusion
events <- rownames(ZDB_matrix)
}
- # Calculate null distribution with the same metric and matrix types
- if (!is_m1_sparse && !is_m2_sparse) {
- null_dist <- cppBetabinPseudoR2(Z = ZDB_matrix[, sample.int(ncol(ZDB_matrix))],
- m1 = as.matrix(m1_inclusion),
- m2 = as.matrix(m2_exclusion),
- metric = metric)
- } else if (is_m1_sparse && is_m2_sparse) {
- null_dist <- cppBetabinPseudoR2_sparse(Z = ZDB_matrix[, sample.int(ncol(ZDB_matrix))],
- m1 = m1_inclusion,
- m2 = m2_exclusion,
- metric = metric)
- } else if (is_m1_sparse && !is_m2_sparse) {
- null_dist <- cppBetabinPseudoR2_mixed1(Z = ZDB_matrix[, sample.int(ncol(ZDB_matrix))],
- m1 = m1_inclusion,
- m2 = as.matrix(m2_exclusion),
- metric = metric)
- } else {
- null_dist <- cppBetabinPseudoR2_mixed2(Z = ZDB_matrix[, sample.int(ncol(ZDB_matrix))],
- m1 = as.matrix(m1_inclusion),
- m2 = m2_exclusion,
- metric = metric)
+ # Null distribution: permute the cells (columns) of Z to break the
+ # cell-to-cell pairing with splicing, recompute, and repeat
+ # permutation_count times. Each column of null_mat is one permutation.
+ n_cells <- ncol(ZDB_matrix)
+ null_mat <- matrix(NA_real_, nrow = nrow(ZDB_matrix), ncol = permutation_count)
+ for (k in seq_len(permutation_count)) {
+ perm_idx <- sample.int(n_cells)
+ null_mat[, k] <- compute_kernel(ZDB_matrix[, perm_idx, drop = FALSE])
}
- data.table::data.table(
- event = events,
- pseudo_correlation = correls,
- null_distribution = null_dist
- )
+ list(correls = correls, events = events, null_mat = null_mat)
}
# Run computation with or without warning suppression
if (suppress_warnings) {
- results <- suppressWarnings(run_computation())
+ comp <- suppressWarnings(run_computation())
} else {
- results <- run_computation()
+ comp <- run_computation()
}
- results <- na.omit(results)
+ correls <- comp$correls
+ events <- comp$events
+ null_mat <- comp$null_mat
+
+ # Per-event null summaries (vectorised over events).
+ n_valid <- rowSums(!is.na(null_mat))
+ s1 <- rowSums(null_mat, na.rm = TRUE)
+ s2 <- rowSums(null_mat^2, na.rm = TRUE)
+
+ null_mean <- s1 / n_valid
+ null_mean[n_valid < 1L] <- NA_real_
+
+ null_var <- (s2 - (s1^2) / n_valid) / (n_valid - 1)
+ # Clamp tiny negative variances from floating-point cancellation to avoid
+ # spurious sqrt() NaN warnings.
+ null_var[is.finite(null_var) & null_var < 0] <- 0
+ null_sd <- sqrt(null_var)
+ null_sd[n_valid < 2L] <- NA_real_
+
+ # Two-sided empirical p-value with the (b + 1) / (m + 1) correction, comparing
+ # absolute null draws to the absolute observed correlation. NA observed values
+ # and events with no valid null draws yield NA.
+ obs_abs <- abs(correls)
+ exceed <- rowSums(abs(null_mat) >= obs_abs, na.rm = TRUE)
+ emp_pvalue <- (exceed + 1) / (n_valid + 1)
+ emp_pvalue[is.na(correls) | n_valid < 1L] <- NA_real_
+
+ results <- data.table::data.table(
+ event = events,
+ pseudo_correlation = correls,
+ null_distribution = null_mean,
+ null_sd = null_sd,
+ n_perm_valid = as.integer(n_valid),
+ emp_pvalue = emp_pvalue
+ )
+
+ # Retain events with a valid observed correlation and at least one valid null
+ # draw (mirrors the previous na.omit() coupling of observed and null values).
+ keep <- !is.na(results$pseudo_correlation) & !is.na(results$null_distribution)
+ results <- results[keep, ]
+ null_mat <- null_mat[keep, , drop = FALSE]
+
+ # Benjamini-Hochberg adjusted empirical p-values across the retained events.
+ results$emp_padj <- stats::p.adjust(results$emp_pvalue, method = "BH")
+
+ # Attach the full per-event null draws for custom downstream analyses.
+ if (nrow(null_mat) > 0L) {
+ rownames(null_mat) <- results$event
+ colnames(null_mat) <- paste0("perm_", seq_len(permutation_count))
+ }
+ data.table::setattr(results, "null_draws", null_mat)
if (verbose) message("Computation completed successfully.")
return(results)
diff --git a/RELEASENOTES.md b/RELEASENOTES.md
new file mode 100644
index 0000000..d4beb2e
--- /dev/null
+++ b/RELEASENOTES.md
@@ -0,0 +1,39 @@
+# splikit — Release Logbook
+
+A chronological logbook of notable changes. Each entry is dated and signed by the
+person who made the change, with a short summary of what was done and why. For the
+canonical, CRAN-facing changelog see `NEWS.md`.
+
+---
+
+## 2.3.2 — 2026-06-09
+
+**Author:** Arsham Mikaeili Namini
+
+**Summary — Repeated permutation null for pseudo correlation.**
+
+- Extended `get_pseudo_correlation()` (and the R6 method
+ `SplikitObject$getPseudoCorrelation()`) to build the null model from multiple
+ cell-permutations instead of a single one.
+- Added two arguments:
+ - `permutation_count` (default `100`): number of times the cells (columns) of
+ `ZDB_matrix` are permuted and the pseudo correlation recomputed, producing a
+ per-event empirical null. `permutation_count = 1` reproduces the original
+ single-permutation behaviour.
+ - `permutation_seed` (default `NULL`): seeds the permutations for reproducible
+ nulls and empirical p-values. The caller's global RNG stream is saved and
+ restored, so a seed here does not disturb downstream randomness.
+- The observed correlation is computed once; only the null pass scales with
+ `permutation_count`.
+- New output columns: `null_sd`, `n_perm_valid`, `emp_pvalue` (two-sided,
+ `(b + 1) / (m + 1)` corrected), `emp_padj` (Benjamini-Hochberg). The full
+ per-event null draws are attached as `attr(result, "null_draws")`.
+- `null_distribution` is now the mean of the per-event null draws (equal to the
+ single draw when `permutation_count = 1`); `pseudo_correlation` is unchanged.
+- Tests: added a dedicated permutation test (validity of empirical p-values,
+ seed reproducibility, global-RNG isolation, `permutation_count = 1`
+ equivalence, input validation); the full-data R6 test runs with a small
+ `permutation_count` to stay fast.
+- Version bumped to 2.3.2; `NEWS.md` updated.
+
+— Arsham Mikaeili Namini
diff --git a/man/SplikitObject.Rd b/man/SplikitObject.Rd
index ce44ade..f3993b2 100644
--- a/man/SplikitObject.Rd
+++ b/man/SplikitObject.Rd
@@ -201,7 +201,9 @@ Compute pseudo-correlation between splicing and external data.
\if{html}{\out{}}\preformatted{SplikitObject$getPseudoCorrelation(
ZDB_matrix,
metric = "CoxSnell",
- suppress_warnings = TRUE
+ suppress_warnings = TRUE,
+ permutation_count = 100L,
+ permutation_seed = NULL
)}\if{html}{\out{
}}
}
@@ -214,11 +216,21 @@ Must have same dimensions as m1.}
\item{\code{metric}}{R-squared metric: "CoxSnell" or "Nagelkerke" (default: "CoxSnell").}
\item{\code{suppress_warnings}}{Suppress computation warnings (default: TRUE).}
+
+\item{\code{permutation_count}}{Integer. Number of cell-permutation null draws per
+event used to build the empirical null (default: 100L). Use 1L for the
+original single-permutation behaviour.}
+
+\item{\code{permutation_seed}}{Optional single numeric value seeding the
+permutations for reproducibility without disturbing the global RNG
+(default: NULL).}
}
\if{html}{\out{}}
}
\subsection{Returns}{
-A data.table with event names, pseudo_correlation, and null_distribution.
+A data.table with event names, pseudo_correlation, null_distribution,
+null_sd, n_perm_valid, emp_pvalue, and emp_padj, plus the full null draws
+in attr(result, "null_draws").
}
}
\if{html}{\out{
}}
diff --git a/man/get_pseudo_correlation.Rd b/man/get_pseudo_correlation.Rd
index 3414efa..407a935 100644
--- a/man/get_pseudo_correlation.Rd
+++ b/man/get_pseudo_correlation.Rd
@@ -10,7 +10,9 @@ get_pseudo_correlation(
m2_exclusion = NULL,
metric = "CoxSnell",
suppress_warnings = TRUE,
- verbose = FALSE
+ verbose = FALSE,
+ permutation_count = 100L,
+ permutation_seed = NULL
)
}
\arguments{
@@ -25,14 +27,34 @@ get_pseudo_correlation(
\item{suppress_warnings}{Logical. If \code{TRUE} (default), suppresses warnings during computation (e.g., due to ill-conditioned inputs).}
\item{verbose}{Logical. If \code{TRUE}, prints progress and informational messages. Default is \code{FALSE}.}
+
+\item{permutation_count}{Integer. Number of times the null model is generated by
+permuting the cells (columns) of \code{ZDB_matrix} and recomputing the pseudo
+correlation. Each permutation yields one null value per event, so the n draws
+form a per-event empirical null. Defaults to \code{100L}. Use \code{1L} to reproduce the
+original single-permutation behaviour. Values of 1000 or more are recommended
+for reliable empirical FDR estimation (the smallest achievable empirical
+p-value is approximately \code{1 / (permutation_count + 1)}).}
+
+\item{permutation_seed}{Optional single numeric value. If supplied, the random
+number generator is seeded with it before the permutations are drawn, making
+the null distribution (and therefore the empirical p-values) reproducible. The
+caller's global RNG stream is saved and restored, so passing a seed does not
+perturb downstream randomness. Defaults to \code{NULL} (use the ambient RNG state).}
}
\value{
A \code{data.table} with the following columns:
\describe{
\item{event}{The event names from \code{ZDB_matrix} rownames.}
\item{pseudo_correlation}{The computed pseudo R² correlation values using the specified metric.}
-\item{null_distribution}{Null correlation values from a permuted version of \code{ZDB_matrix}.}
+\item{null_distribution}{Mean of the per-event null correlation values across the \code{permutation_count} permutations (equal to the single null draw when \code{permutation_count = 1}).}
+\item{null_sd}{Standard deviation of the per-event null draws (\code{NA} when fewer than two valid draws are available).}
+\item{n_perm_valid}{Number of permutations that produced a non-\code{NA} null value for the event.}
+\item{emp_pvalue}{Two-sided empirical p-value, \code{(b + 1) / (n_perm_valid + 1)}, where \code{b} is the number of valid null draws whose absolute value is greater than or equal to the absolute observed correlation.}
+\item{emp_padj}{Benjamini-Hochberg adjusted \code{emp_pvalue} across the retained events.}
}
+The full matrix of per-event null draws (events x \code{permutation_count}) is attached
+as \code{attr(result, "null_draws")} for custom downstream analyses.
}
\description{
This function calculates a pseudo R²-like correlation metric using a beta-binomial model
@@ -52,6 +74,11 @@ m1_inclusion <- m1_obj$m1_inclusion_matrix
eventdata <- m1_obj$event_data
m2_exclusion <- make_m2(m1_inclusion, eventdata)
+# subset to a few hundred cells to keep the example quick
+cells <- seq_len(min(250, ncol(m1_inclusion)))
+m1_inclusion <- m1_inclusion[, cells]
+m2_exclusion <- m2_exclusion[, cells]
+
# creating a dummy ZDB
ZDB_matrix <- matrix(rnorm(n = (nrow(m1_inclusion) * ncol(m1_inclusion)), sd = 7),
nrow = nrow(m1_inclusion),
@@ -62,15 +89,24 @@ rownames(ZDB_matrix) <- rownames(m1_inclusion)
# Example with dense matrices (backward compatible)
m1_dense <- as.matrix(m1_inclusion)
m2_dense <- as.matrix(m2_exclusion)
-pseudo_r_square_cox <- get_pseudo_correlation(ZDB_matrix, m1_dense, m2_dense)
+pseudo_r_square_cox <- get_pseudo_correlation(ZDB_matrix, m1_dense, m2_dense,
+ permutation_count = 20,
+ permutation_seed = 1)
print(pseudo_r_square_cox)
# Example with sparse matrices (more memory efficient)
-pseudo_r_square_sparse <- get_pseudo_correlation(ZDB_matrix, m1_inclusion, m2_exclusion)
+pseudo_r_square_sparse <- get_pseudo_correlation(ZDB_matrix, m1_inclusion, m2_exclusion,
+ permutation_count = 20,
+ permutation_seed = 1)
+
+# Per-event empirical p-values and the full null draws
+head(pseudo_r_square_sparse[, c("event", "pseudo_correlation", "emp_pvalue")])
+dim(attr(pseudo_r_square_sparse, "null_draws"))
# Example using Nagelkerke R-squared instead of Cox-Snell
pseudo_r_square_nagel <- get_pseudo_correlation(ZDB_matrix, m1_inclusion, m2_exclusion,
- metric = "Nagelkerke")
+ metric = "Nagelkerke",
+ permutation_count = 20)
print(pseudo_r_square_nagel)
}
diff --git a/tests/testthat/test-SplikitObject.R b/tests/testthat/test-SplikitObject.R
index adb8b74..d3a3adf 100644
--- a/tests/testthat/test-SplikitObject.R
+++ b/tests/testthat/test-SplikitObject.R
@@ -659,9 +659,13 @@ test_that("SplikitObject$getPseudoCorrelation executes correctly", {
set.seed(42)
ZDB_matrix <- matrix(rnorm(n_events * n_cells), nrow = n_events, ncol = n_cells)
- res <- obj$getPseudoCorrelation(ZDB_matrix = ZDB_matrix, suppress_warnings = TRUE)
+ # Keep permutation_count small here: this runs on the full 2000 x 2000 toy data.
+ res <- obj$getPseudoCorrelation(ZDB_matrix = ZDB_matrix, suppress_warnings = TRUE,
+ permutation_count = 3, permutation_seed = 1)
expect_true(data.table::is.data.table(res))
# With pure random data, some might be NA and removed, check columns
- expect_true(all(c("event", "pseudo_correlation", "null_distribution") %in% names(res)))
+ expect_true(all(c("event", "pseudo_correlation", "null_distribution",
+ "null_sd", "n_perm_valid", "emp_pvalue", "emp_padj") %in% names(res)))
+ expect_true(all(res$emp_pvalue > 0 & res$emp_pvalue <= 1))
})
diff --git a/tests/testthat/test-general_tools.R b/tests/testthat/test-general_tools.R
index 89686d4..30222b1 100644
--- a/tests/testthat/test-general_tools.R
+++ b/tests/testthat/test-general_tools.R
@@ -35,6 +35,68 @@ test_that("get_pseudo_correlation works on valid data", {
expect_equal(res_obj$pseudo_correlation, res$pseudo_correlation)
})
+test_that("get_pseudo_correlation permutation null is valid and reproducible", {
+ n_events <- 6
+ n_cells <- 120
+ set.seed(11)
+
+ m1 <- matrix(rbinom(n_events * n_cells, size = 10, prob = 0.5), nrow = n_events)
+ m2 <- matrix(rbinom(n_events * n_cells, size = 10, prob = 0.5), nrow = n_events)
+ ZDB <- matrix(rnorm(n_events * n_cells), nrow = n_events, ncol = n_cells)
+ rownames(m1) <- rownames(m2) <- rownames(ZDB) <- paste0("event_", seq_len(n_events))
+
+ res <- get_pseudo_correlation(ZDB, m1, m2,
+ permutation_count = 50, permutation_seed = 123)
+
+ # New columns are present and well formed
+ expect_true(all(c("null_sd", "n_perm_valid", "emp_pvalue", "emp_padj") %in% names(res)))
+ expect_true(all(res$emp_pvalue > 0 & res$emp_pvalue <= 1))
+ expect_true(all(res$emp_padj >= 0 & res$emp_padj <= 1))
+ expect_true(all(res$n_perm_valid <= 50L))
+ # Smallest achievable empirical p-value is 1 / (n_perm_valid + 1)
+ expect_true(all(res$emp_pvalue >= 1 / (res$n_perm_valid + 1) - 1e-9))
+
+ # Full null draws attached as an attribute, correctly shaped
+ draws <- attr(res, "null_draws")
+ expect_true(is.matrix(draws))
+ expect_equal(ncol(draws), 50L)
+ expect_equal(nrow(draws), nrow(res))
+
+ # Same seed -> identical null; observed correlation is seed-independent
+ res2 <- get_pseudo_correlation(ZDB, m1, m2,
+ permutation_count = 50, permutation_seed = 123)
+ expect_equal(res$null_distribution, res2$null_distribution)
+ expect_equal(res$emp_pvalue, res2$emp_pvalue)
+ expect_equal(res$pseudo_correlation, res2$pseudo_correlation)
+
+ # Different seed -> different draws, but the same observed correlation
+ res3 <- get_pseudo_correlation(ZDB, m1, m2,
+ permutation_count = 50, permutation_seed = 999)
+ expect_false(isTRUE(all.equal(attr(res, "null_draws"), attr(res3, "null_draws"))))
+ expect_equal(res$pseudo_correlation, res3$pseudo_correlation)
+
+ # permutation_seed must not perturb the caller's global RNG stream
+ set.seed(7); a <- runif(3)
+ invisible(get_pseudo_correlation(ZDB, m1, m2,
+ permutation_count = 10, permutation_seed = 42))
+ set.seed(7); b <- runif(3)
+ expect_equal(a, b)
+
+ # permutation_count = 1 reproduces the single-draw behaviour: the mean null
+ # equals the single column of draws
+ res1 <- get_pseudo_correlation(ZDB, m1, m2,
+ permutation_count = 1, permutation_seed = 5)
+ expect_equal(ncol(attr(res1, "null_draws")), 1L)
+ expect_equal(unname(res1$null_distribution),
+ unname(attr(res1, "null_draws")[, 1]))
+
+ # Input validation
+ expect_error(get_pseudo_correlation(ZDB, m1, m2, permutation_count = 0),
+ "permutation_count")
+ expect_error(get_pseudo_correlation(ZDB, m1, m2, permutation_seed = c(1, 2)),
+ "permutation_seed")
+})
+
test_that("get_pseudo_correlation error handling works", {
m1 <- matrix(1, nrow = 2, ncol = 2)
m2 <- matrix(1, nrow = 2, ncol = 2)
diff --git a/vignettes/methods.Rmd b/vignettes/methods.Rmd
index a0ab14d..f9e47f7 100644
--- a/vignettes/methods.Rmd
+++ b/vignettes/methods.Rmd
@@ -25,7 +25,7 @@ Splice junctions are grouped into *local junction variants* — junctions sharin
## Event-covariate association
-`get_pseudo_correlation()` fits a per-event binomial logistic GLM of the inclusion ratio on a target covariate by iteratively reweighted least squares, and reports a Cox-Snell / Nagelkerke pseudo-R-squared computed from the residual deviance. This quantifies how strongly each event tracks the covariate (e.g. a cluster label or a gene's expression).
+`get_pseudo_correlation()` fits a per-event binomial logistic GLM of the inclusion ratio on a target covariate by iteratively reweighted least squares, and reports a Cox-Snell / Nagelkerke pseudo-R-squared computed from the residual deviance. This quantifies how strongly each event tracks the covariate (e.g. a cluster label or a gene's expression). A per-event empirical null is built by permuting the cells (columns) of the covariate `permutation_count` times (default 100) and recomputing the statistic; this yields a two-sided empirical p-value (`emp_pvalue`) and a Benjamini-Hochberg adjustment (`emp_padj`). The permutations can be made reproducible with `permutation_seed` without disturbing the global RNG stream.
## Implementation
diff --git a/vignettes/splikit_manual.Rmd b/vignettes/splikit_manual.Rmd
index 18ccda6..fdb1f6e 100644
--- a/vignettes/splikit_manual.Rmd
+++ b/vignettes/splikit_manual.Rmd
@@ -681,8 +681,9 @@ length(intersect(hvg_vst[1:500]$events, hvg_dev[1:500]$events))
**Function Signature**:
```r
-get_pseudo_correlation(ZDB_matrix, m1_inclusion, m2_exclusion,
- metric = "CoxSnell", suppress_warnings = TRUE)
+get_pseudo_correlation(ZDB_matrix, m1_inclusion, m2_exclusion,
+ metric = "CoxSnell", suppress_warnings = TRUE,
+ permutation_count = 100L, permutation_seed = NULL)
```
**Parameters**:
@@ -690,6 +691,10 @@ get_pseudo_correlation(ZDB_matrix, m1_inclusion, m2_exclusion,
- `m1_inclusion`, `m2_exclusion`: Can be either sparse or dense matrices
- `metric`: R² metric to use - "CoxSnell" (default) or "Nagelkerke"
- `suppress_warnings`: Whether to suppress computational warnings
+- `permutation_count`: Number of cell-permutation null draws per event (default `100`); use `1` for the original single-permutation behaviour, and `1000`+ for reliable empirical FDR
+- `permutation_seed`: Optional seed for reproducible permutations (does not disturb the global RNG)
+
+**Output columns**: `event`, `pseudo_correlation`, `null_distribution` (mean of the per-event null draws), `null_sd`, `n_perm_valid`, `emp_pvalue` (two-sided empirical p-value), and `emp_padj` (BH-adjusted). The full per-event null draws are attached as `attr(result, "null_draws")`.
**Use Cases**:
- Correlating splicing patterns with gene expression