Retain fSuSiE curves and bands while storing each joint fit once

.github/environment/pixi.toml

@@ -9,7 +9,7 @@ platforms = [
 
 [tasks]
 devtools_document = "cd $GITHUB_WORKSPACE; R -e 'devtools::document()'"
-devtools_test = "cd $GITHUB_WORKSPACE; R -e 'devtools::test()'"
+devtools_test = "cd $GITHUB_WORKSPACE; R -e 'devtools::test(stop_on_failure = TRUE)'"
 codecov = "cd $GITHUB_WORKSPACE; R -e 'covr::codecov(quiet = FALSE)'"
 rcmdcheck = "cd $GITHUB_WORKSPACE; R -e 'rcmdcheck::rcmdcheck()'"
 bioccheck_git_clone = "cd $GITHUB_WORKSPACE; R -e 'BiocCheck::BiocCheckGitClone()'"

R/AllGenerics.R

@@ -2281,8 +2281,9 @@ setGeneric("credibleSetSummary", function(x, ...) {
 #' the fitted effect curve and its credible band over the functional grid, one
 #' row per (credible set, grid position). Wraps the upstream fSuSiE band
 #' computation (fsusieR's internal \code{update_cal_credible_band.susiF} +
-#' \code{get_fitted_effect}). Requires an UNtrimmed fit (the wavelet slots are
-#' dropped by trimming); degrades to zero rows for non-fSuSiE or trimmed fits.
+#' \code{get_fitted_effect}). Uses saved bands in trimmed fits, or computes
+#' them from an untrimmed fit. Returns zero rows when the functional fields
+#' are absent.
 #'
 #' @param x A \code{FineMappingRow} or \code{FineMappingResultBase}.
 #' @param ... Ignored.
@@ -2303,8 +2304,8 @@ setGeneric("fsusieCredibleBand", function(x, ...) {
 #' zero (the genomic footprint of each fSuSiE effect), from the upstream
 #' \code{fsusieR::affected_reg}. Returns a \code{GRanges} with the credible-set
 #' label, its purity, and the effect \code{direction}
-#' (\code{"pos"}/\code{"neg"}; upstream discards it). Requires an UNtrimmed fit;
-#' empty otherwise.
+#' (\code{"pos"}/\code{"neg"}; upstream discards it). Uses retained functional
+#' summaries or an untrimmed fit; empty when those fields are absent.
 #'
 #' @param x A \code{FineMappingRow} or \code{FineMappingResultBase}.
 #' @param ... Ignored.

R/GenotypeHandle.R

@@ -161,6 +161,7 @@ setMethod("show", "GenotypeHandle", function(object) {
 #'   \code{VariantAnnotation::readVcf} when the source is a VCF.
 #' @return A \code{GenotypeHandle} object.
 #' @keywords internal
+#' @rdname GenotypeHandle-class
 GenotypeHandle <- function(
     path = NULL,
     plink1Prefix = NULL,

R/fineMappingRow.R

@@ -201,10 +201,10 @@ fineMappingRow <- function(variantIds, susieFit, topLoci, cvResult = NULL) {
 .isFsusieFit <- function(fit) {
     !is.null(fit) &&
         is.list(fit) &&
-        !is.null(fit$fitted_wc2) &&
         !is.null(fit$fitted_func) &&
         !is.null(fit$outing_grid) &&
-        !is.null(fit$alpha)
+        (!is.null(fit$cred_band) ||
+             (!is.null(fit$fitted_wc2) && !is.null(fit$alpha)))
 }
 
 # Chromosome of the fit's variants (matches topLoci's `chrom`, no "chr").

R/fineMappingWrappers.R

@@ -2238,15 +2238,15 @@ trimFinemappingFit <- function(fit, effectIdx, method, csTables) {
     } else {
         common
     }
-    # fSuSiE: keep the precomputed variants x features TWAS weight matrix
-    # (fsusieWeights output, attached as $coef before trimming) so downstream
-    # TWAS can read it without the dropped wavelet slots.
-    withCoef <- if (method == "fsusie" && !is.null(fit$coef)) {
-        list_assign(trimmed, coef = fit$coef)
+    # fSuSiE: retain the functional summaries alongside the precomputed
+    # weights, so a trimmed fit still yields curves, bands and TWAS weights
+    # once the wavelet reconstruction fields are gone.
+    withFsusie <- if (method == "fsusie") {
+        .trimAddFsusie(trimmed, fit)
     } else {
         trimmed
     }
-    `class<-`(withCoef, unique(c(method, "susie")))
+    `class<-`(withFsusie, unique(c(method, "susie")))
 }
 
 # The minimal always-kept subset of a susie fit (pip, credible sets, effect
@@ -2341,6 +2341,30 @@ trimFinemappingFit <- function(fit, effectIdx, method, csTables) {
 
 # mvsusie-specific slots: per-effect mu2_diag, the coefficient matrix, and the
 # conditional lfsr array.
+# fSuSiE: the fields the wavelet reconstruction would otherwise take with
+# it. The base trimmed fit already keeps alpha, lbf_variable and the sets for
+# coloc; these carry the fitted curve, its credible band and the functional
+# grid. The band is populated BEFORE trimming because
+# .fsusiePopulateCredibleBand() needs the reconstruction fields that trimming
+# removes. intersect() rather than a per-field guard: an absent field is
+# simply not retained.
+# @noRd
+.trimAddFsusie <- function(trimmed, fit) {
+    populated <- if (.isFsusieFit(fit)) {
+        .fsusiePopulateCredibleBand(fit)
+    } else {
+        fit
+    }
+    keep <- intersect(
+        c(
+            "coef", "fitted_func", "cred_band", "outing_grid", "cs",
+            "csd_X", "lfsr_func"
+        ),
+        names(populated)
+    )
+    list_assign(trimmed, !!!populated[keep])
+}
+
 .trimAddMvsusie <- function(trimmed, fit, effectIdx) {
     list_assign(
         trimmed,
@@ -5550,6 +5574,12 @@ krigingOutlierQc <- function(
 # @noRd
 #' @importFrom rlang try_fetch
 .fsusiePopulateCredibleBand <- function(fit) {
+    # A native post_processing = "none" run can leave zero-filled placeholder
+    # bands, so only reuse a band once trimming has removed the
+    # reconstruction fields -- otherwise recompute it.
+    if (is.null(fit$fitted_wc2) && !is.null(fit$cred_band)) {
+        return(fit)
+    }
     fn <- try_fetch(
         get("update_cal_credible_band.susiF", envir = asNamespace("fsusieR")),
         error = function(cnd) NULL

R/jointEngine.R

@@ -277,7 +277,7 @@ setMethod(
 )
 
 # fsusie joint fit (functional SuSiE over the trait domain; individual-level,
-# cross-trait). One per-condition entry per trait, with an optional CV slice.
+# cross-trait). Keep the first post-processed entry for this joint fit.
 # @noRd
 .jointFitFsusie <- function(group, Xc, Yc, nCond, cfg, args) {
     if (length(.jgTraitPos(group)) != nCond) {
@@ -295,7 +295,7 @@ setMethod(
     )
     raw <- exec(fitFsusie, !!!.splitMethodArgs(fitFsusie, fitArgs))
     # Collapse the functional fit to a variants x features weight matrix now
-    # (trimming later drops fitted_wc/csd_X); store on $coef so a trimmed fit
+    # (trimming later drops fitted_wc); store on $coef so a trimmed fit
     # can still yield TWAS weights.
     fit <- list_assign(
         raw,
@@ -306,14 +306,27 @@ setMethod(
     ) |>
         .setFinemappingFitClass("fsusie")
     cvM <- .jointFsusieCv(Xc, Yc, group, cfg, args, verbose)
-    map(
-        seq_len(nCond),
-        .jointFsusieEntry,
+    entry <- .jointFsusieEntry(
+        r = 1L,
         fit = fit,
         cvM = cvM,
         Xc = Xc,
         cfg = cfg
     )
+    # ONE entry for the whole joint fit, unlike mvsusie's one-per-condition.
+    # mvsusie's posteriors carry a conditions axis, so .fmSliceCond() gives
+    # each condition its own 2-D slice and N rows partition the fit at no
+    # cost. An fsusie fit has no such axis: each trait is a POSITION on a
+    # shared functional grid, so `fitted_func` / `cred_band` / `outing_grid`
+    # describe all traits at once and cannot be sliced per trait. One row per
+    # trait would therefore store N copies of the same curve.
+    #
+    # The trait list itself is not lost -- every row carries the full joint
+    # membership in its `jointTraits` column. Only the grid POSITIONS are
+    # unrecoverable from that, and they are what maps a curve position back
+    # to a trait, so they ride on the fit.
+    entry@susieFit$trait_positions <- .jgTraitPos(group)
+    list(entry)
 }
 
 # Per-fold fsusie CV slice, or NULL when CV is disabled.