update to 1.5.2

DESCRIPTION

@@ -10,7 +10,7 @@ Description: BreastSubtypeR provides an assumption-aware, multi-method framework
     (iBreastSubtypeR) is included for interactive analyses and to support users
     without programming experience.
 Encoding: UTF-8
-Version: 1.3.3
+Version: 1.5.1
 biocViews: RNASeq, Software, GeneExpression, Classification, Preprocessing, Visualization
 Authors@R: c(
     person(given = "Qiao", family = "Yang", 
@@ -59,4 +59,4 @@ License: GPL-3
 VignetteBuilder: knitr
 Roxygen: list(markdown = TRUE, roclets = c("rd", "namespace", "collate"))
 LazyData: FALSE
-RoxygenNote: 7.3.3
+RoxygenNote: 8.0.0

NEWS.md

@@ -1,65 +1,73 @@
-# BreastSubtypeR 1.3.2
+# BreastSubtypeR 1.5.1
+
+## Bug fixes
+
+- Fixed TNBC cohort detection in cohort-specific method selection (#133). Cohorts are now classified as TNBC only when all evaluable non-missing `TN` annotations indicate TN. Mixed cohorts containing both TN and nonTN samples are no longer routed to the TNBC-specific branch solely because some samples are TN.
+
+## Tests
+
+- Added synthetic phenotype-table tests for TN-only, mixed, UNC-like mixed, and missing or partly missing TN annotations.
+
+# BreastSubtypeR 1.5.0
 
 ## Highlights (from v1.1.3 onward)
 
--   Paper published in *NAR Genomics and Bioinformatics* (2025), **Editor’s Choice** (DOI: 10.1093/nargab/lqaf131).
--   Support for raw RNA-seq counts (requires gene lengths).
--   **iBreastSubtypeR** refresh: cleaner UX, smarter AUTO guidance, consistent exports.
+- Paper published in *NAR Genomics and Bioinformatics* (2025), **Editor's Choice** (DOI: 10.1093/nargab/lqaf131).
+- Support for raw RNA-seq counts (requires gene lengths).
+- **iBreastSubtypeR** refresh: cleaner UX, smarter AUTO guidance, consistent exports.
 
 ### Enhancements
 
--   **ssBC/ssBC.v2: singleton subgroup robustness:** Subgroups with `n=1` no longer error:
-    -   Keeps matrix shape (`drop=FALSE`) and hardens dimnames/types.
-    -   Primary path: original `sspPredict()`.\
-        Fallback: nearest-centroid (Spearman) when needed.
-    -   If there are **0 common PAM50 genes**, returns `NA` labels with shaped `distances`/`dist.RORSubtype` to avoid downstream errors.
-    -   ROR computation guarded for incomplete inputs.
--   **SSPBC output now "full":** `BS_sspbc()` and Shiny "sspbc" runs return a full metrics table (not calls-only).
-    -   Exports map core label columns to the standard names (`Call_5class` / `Call_4class` when applicable).
--   **Shiny: "Load example data…" button**
-    -   One-click load of a small demo dataset from `inst/RshinyTest/` to explore the UI without uploads.\
-    -   Shows a notification on success; users can immediately run **Preprocess & map** and analyses.
--   **AUTO preflight UI (Shiny):** Now detects cohort kind (`TN`, `ER/HER2`, `ER`-only, `HER2`-only) and shows compact stats:
-    -   ER/HER2 subgroups: **ER+/HER2-**, **ER-/HER2-**, **ER+/HER2+**, **ER-/HER2+**\
-    -   TN cohorts: **TN** vs **nonTN**\
-    -   Readiness uses the same minimums used by AUTO (sourced programmatically; no duplicated thresholds).
--   **Shorter notifications.**\
-    -Routine toasts (e.g., "Step 1 complete. Proceed to Step 2.") now auto-dismiss sooner to reduce UI clutter.
--   **Phenodata normalization (Mapping):** Accepts flexible ER/HER2/TN encodings and normalizes to canonical forms (`ER+/ER-`, `HER2+/HER2-`, `TN/nonTN`). Ambiguous `HER2="2+"` remains as-is and raises a warning.
+- **ssBC/ssBC.v2: singleton subgroup robustness:** Subgroups with `n=1` no longer error:
+  - Keeps matrix shape (`drop=FALSE`) and hardens dimnames/types.
+  - Primary path: original `sspPredict()`.
+    Fallback: nearest-centroid (Spearman) when needed.
+  - If there are **0 common PAM50 genes**, returns `NA` labels with shaped `distances`/`dist.RORSubtype` to avoid downstream errors.
+  - ROR computation guarded for incomplete inputs.
+- **SSPBC output now "full":** `BS_sspbc()` and Shiny "sspbc" runs return a full metrics table (not calls-only).
+  - Exports map core label columns to the standard names (`Call_5class` / `Call_4class` when applicable).
+- **Shiny: "Load example data..." button**
+  - One-click load of a small demo dataset from `inst/RshinyTest/` to explore the UI without uploads.
+  - Shows a notification on success; users can immediately run **Preprocess & map** and analyses.
+- **AUTO preflight UI (Shiny):** Now detects cohort kind (`TN`, `ER/HER2`, `ER`-only, `HER2`-only) and shows compact stats:
+  - ER/HER2 subgroups: **ER+/HER2-**, **ER-/HER2-**, **ER+/HER2+**, **ER-/HER2+**
+  - TN cohorts: **TN** vs **nonTN**
+  - Readiness uses the same minimums used by AUTO (sourced programmatically; no duplicated thresholds).
+- **Shorter notifications.**
+  - Routine toasts (e.g., "Step 1 complete. Proceed to Step 2.") now auto-dismiss sooner to reduce UI clutter.
+- **Phenodata normalization (Mapping):** Accepts flexible ER/HER2/TN encodings and normalizes to canonical forms (`ER+/ER-`, `HER2+/HER2-`, `TN/nonTN`). Ambiguous `HER2="2+"` remains as-is and raises a warning.
 
 ### Bug fixes
 
--   **TN cohorts + ssBC**: `BS_Multi()` now respects TN cohorts when methods are specified manually;
-`ssBC`/`ssBC.v2` switch to `s = "TN"` / `"TN.v2"` when a `TN` column indicates a TN cohort.
-Falls back to `s = "ER"` / `"ER.v2"` otherwise.
--   **AUTO**: Fixed a crash in BS_Multi(methods="AUTO") when ER and/or HER2 contained missing values (NA).
--   **AUTO internals**: fixed variable name typo (`samples_ERHER2.icd`).
--   **Mapping():** Robust ENTREZID coercion (from `as.character()` to `as.integer()` with suppressed warnings).
--   **cIHC.itr**: `outList$distances` now returned as numeric matrix.
+- **TN cohorts + ssBC**: `BS_Multi()` now respects TN cohorts when methods are specified manually; `ssBC`/`ssBC.v2` switch to `s = "TN"` / `"TN.v2"` when a `TN` column indicates a TN cohort. Falls back to `s = "ER"` / `"ER.v2"` otherwise.
+- **AUTO**: Fixed a crash in `BS_Multi(methods = "AUTO")` when ER and/or HER2 contained missing values (NA).
+- **AUTO internals**: fixed variable name typo (`samples_ERHER2.icd`).
+- **Mapping():** Robust ENTREZID coercion (from `as.character()` to `as.integer()` with suppressed warnings).
+- **cIHC.itr**: `outList$distances` now returned as numeric matrix.
 
 ### Shiny
 
--   **Surface method warnings as toasts:**
-    -   Runs are wrapped in a warning handler; package warnings (e.g., ssBC.v2 singleton fallbacks) appear as yellow notifications.\
-    -   Warnings include subgroup, `n`, and example sample IDs for quick triage.
--   **Shiny preflight reset**:
-    -   Fixed a stale cohort summary after switching data sources (manual uploads ↔ example). The preflight panel now revalidates once inputs change.
+- **Surface method warnings as toasts:**
+  - Runs are wrapped in a warning handler; package warnings (e.g., ssBC.v2 singleton fallbacks) appear as yellow notifications.
+  - Warnings include subgroup, `n`, and example sample IDs for quick triage.
+- **Shiny preflight reset**:
+  - Fixed a stale cohort summary after switching data sources (manual uploads <-> example). The preflight panel now revalidates once inputs change.
 
 ### Developer notes
 
--   Added lightweight internal logger `._msg()` and replaced scattered `message()` calls in AUTO to standardize package output without affecting CRAN/Bioc checks.
+- Added lightweight internal logger `._msg()` and replaced scattered `message()` calls in AUTO to standardize package output without affecting CRAN/Bioc checks.
 
 ### Documentation
 
--   **README/vignette:** brief note on the example-data button and expected file locations.
--   **Mapping(): Column metadata clarified.** Added explicit requirements for receptor fields used by AUTO and ER/HER2/TN-dependent methods (`ssBC`, `cIHC`/`cIHC.itr`, `PCAPAM50`) and for ROR covariates (`TSIZE`, `NODE` as numeric 0/1). Documented preferred coding and automatic normalization behavior.
+- **README/vignette:** brief note on the example-data button and expected file locations.
+- **Mapping(): Column metadata clarified.** Added explicit requirements for receptor fields used by AUTO and ER/HER2/TN-dependent methods (`ssBC`, `cIHC`/`cIHC.itr`, `PCAPAM50`) and for ROR covariates (`TSIZE`, `NODE` as numeric 0/1). Documented preferred coding and automatic normalization behavior.
 
 ### Compatibility Notes
 
--   SSPBC "full" output keeps previous columns for calls; additional metrics may appear.
+- SSPBC "full" output keeps previous columns for calls; additional metrics may appear.
 
 ## Upgrade Notes
 
--   Raw RNA-seq counts are supported **from v1.1.3 onward** (requires gene lengths).
--   If you previously parsed `BS` / `BS.Subtype`, switch to **`Call_5class` / `Call_4class`**.
--   Package API unchanged.
+- Raw RNA-seq counts are supported **from v1.1.3 onward** (requires gene lengths).
+- If you previously parsed `BS` / `BS.Subtype`, switch to **`Call_5class` / `Call_4class`**.
+- Package API unchanged.

R/BreastSubtypeR_methods.R

@@ -89,9 +89,10 @@ NULL
 #'   - **Column metadata**: sample-level metadata in `colData()`. Specifically:
 #'     - `PatientID` (**required**).
 #'     - **Receptor fields required by specific methods** (and by `BS_Multi()` / AUTO):
-#'       * `ER` — required for `ssBC`, `cIHC` / `cIHC.itr`, `PCAPAM50`, and AUTO.
-#'       * `HER2` — required for `ssBC` with `s="ER.v2"` and for AUTO.
-#'       * `TN` — required for `ssBC` with `s="TN"` / `"TN.v2"` and for AUTO when TN logic is used.
+#'       * `ER` - required for `ssBC`, `cIHC` / `cIHC.itr`, `PCAPAM50`, and AUTO.
+#'       * `HER2` - required for `ssBC` with `s="ER.v2"` and for AUTO.
+#'       * `TN` - per-sample annotation required for `ssBC` with `s="TN"` / `"TN.v2"`;
+#'         AUTO uses TNBC-specific cohort handling only when all evaluable TN annotations indicate TN.
 #'     - **Clinical covariates for ROR (NC-based methods only)**:
 #'       * `TSIZE`: Tumor size (0 = \eqn{\le 2}{<= 2} cm; 1 = \eqn{> 2}{> 2} cm). Must be numeric (0/1).
 #'       * `NODE`: Lymph node status (0 = negative; \eqn{\ge 1}{>= 1} = positive). Must be numeric (0/1).
@@ -1109,14 +1110,15 @@ BS_Multi <- function(data_input,
 
     # Detect true TN cohort
     has_TN_col <- "TN" %in% colnames(pheno)
-    is_TN_cohort <- has_TN_col && all(na.omit(pheno$TN) == "TN") && nrow(pheno) > 0
+    tn_summary <- if (has_TN_col) .tn_only_summary(pheno$TN) else NULL
+    is_TN_cohort <- has_TN_col && isTRUE(tn_summary$is_tn_only)
 
     # manual vs AUTO
     is_manual <- !(length(methods) == 1 && methods[1] == "AUTO")
 
     # Only tell users in MANUAL mode that ssBC routing will use TN/TN.v2
     if (is_manual && is_TN_cohort && any(methods %in% c("ssBC", "ssBC.v2"))) {
-        n_tn <- sum(na.omit(pheno$TN) == "TN")
+        n_tn <- tn_summary$n_tn
         .msg("Detected pure TN cohort (TN=100%%, n=%d). Routing ssBC with s='TN' and ssBC.v2 with s='TN.v2'.",
             n_tn,
             origin = "MANUAL"

R/Utilities.R

@@ -212,6 +212,25 @@ prepare_ssp_matrix <- function(x, genes.s, RawCounts, samplenames, verbose) {
   df
 }
 
+#' Summarize TN annotations for cohort-level TN-only detection
+#'
+#' @keywords internal
+#' @noRd
+.tn_only_summary <- function(tn_flag) {
+    tn_chr <- toupper(trimws(as.character(tn_flag)))
+    tn_evaluable <- !is.na(tn_chr) & nzchar(tn_chr)
+    tn_truthy <- tn_chr %in% c("TN", "TRUE", "T", "YES", "Y", "1")
+
+    n_tn <- sum(tn_truthy & tn_evaluable)
+    n_evaluable_tn <- sum(tn_evaluable)
+
+    list(
+        n_tn = n_tn,
+        n_evaluable_tn = n_evaluable_tn,
+        is_tn_only = n_evaluable_tn > 0L && n_tn == n_evaluable_tn
+    )
+}
+
 
 #' Map Gene IDs and Handle missing data
 #'
@@ -376,11 +395,11 @@ get_methods <- function(pheno) {
 
         ## ---- TNBC handling ------------------------------------------------
         if ("TN" %in% colnames(pheno)) {
-            # Accept common truthy markers in TN (case-insensitive)
-            tn_flag <- toupper(trimws(as.character(pheno$TN)))
-            n_TN <- sum(tn_flag %in% c("TN", "TRUE", "T", "YES", "Y", "1"), na.rm = TRUE)
+            # Use TNBC cohort handling only when all evaluable TN annotations are TN.
+            tn_summary <- .tn_only_summary(pheno$TN)
+            n_TN <- tn_summary$n_tn
 
-            if (n_TN > 0) {
+            if (tn_summary$is_tn_only) {
                 cohort.select <- "TNBC"
 
                 if (n_TN >= n_TN_threshold) {
@@ -392,8 +411,11 @@ get_methods <- function(pheno) {
                     .msg("Running methods: AIMS, sspbc", origin = "AUTO")
                     methods <- c("AIMS", "sspbc")
                 }
+            } else if (tn_summary$n_evaluable_tn > 0L) {
+                .msg("TN column detected but not all evaluable samples are TN;
+              proceeding with non-TNBC logic.", origin = "AUTO")
             } else {
-                .msg("TN column detected but no samples flagged as TN;
+                .msg("TN column detected but no evaluable samples flagged as TN;
               proceeding with non-TNBC logic.", origin = "AUTO")
             }
         }

tests/testthat/test-tn-cohort-detection.R

@@ -0,0 +1,87 @@
+make_pheno <- function(er, her2, tn = NULL) {
+    pheno <- data.frame(
+        PatientID = paste0("S", seq_along(er)),
+        ER = er,
+        HER2 = her2,
+        stringsAsFactors = FALSE
+    )
+    if (!is.null(tn)) {
+        pheno$TN <- tn
+    }
+    rownames(pheno) <- pheno$PatientID
+    pheno
+}
+
+expect_auto_panel <- function(pheno, methods, cohort) {
+    old <- options(BreastSubtypeR.verbose = FALSE)
+    on.exit(options(old), add = TRUE)
+    out <- BreastSubtypeR:::get_methods(pheno)
+    expect_identical(out$methods, methods)
+    expect_identical(out$cohort.select, cohort)
+}
+
+tn_panel <- c("ssBC", "ssBC.v2", "AIMS", "sspbc")
+full_mixed_panel <- c(
+    "parker.original", "genefu.scale", "genefu.robust",
+    "ssBC", "ssBC.v2", "cIHC", "cIHC.itr", "PCAPAM50", "AIMS", "sspbc"
+)
+
+test_that("TN-only cohorts with a TN column select the TNBC panel", {
+    pheno <- make_pheno(
+        er = rep("ER-", 20),
+        her2 = rep("HER2-", 20),
+        tn = rep("TN", 20)
+    )
+
+    expect_auto_panel(pheno, tn_panel, "TNBC")
+})
+
+test_that("mixed cohorts with some TN samples are not treated as TNBC", {
+    pheno <- make_pheno(
+        er = c(rep("ER+", 50), rep("ER-", 30)),
+        her2 = c(rep("HER2-", 30), rep("HER2+", 20), rep("HER2-", 20), rep("HER2+", 10)),
+        tn = c(rep("nonTN", 60), rep("TN", 20))
+    )
+
+    expect_auto_panel(pheno, full_mixed_panel, "mixed")
+})
+
+test_that("UNC-like mixed cohorts with TN annotations are not treated as TNBC", {
+    pheno <- make_pheno(
+        er = c(rep("ER+", 62), rep("ER-", 38)),
+        her2 = c(rep("HER2+", 9), rep("HER2-", 53), rep("HER2+", 6), rep("HER2-", 32)),
+        tn = c(rep("nonTN", 70), rep("TN", 30))
+    )
+
+    expect_auto_panel(pheno, full_mixed_panel, "mixed")
+})
+
+test_that("missing or empty TN values do not trigger TNBC handling", {
+    pheno <- make_pheno(
+        er = c(rep("ER+", 25), rep("ER-", 25)),
+        her2 = c(rep("HER2-", 20), rep("HER2+", 5), rep("HER2-", 20), rep("HER2+", 5)),
+        tn = rep(c(NA_character_, "", " "), length.out = 50)
+    )
+
+    expect_auto_panel(pheno, full_mixed_panel, "mixed")
+})
+
+test_that("partly missing TN values with TN and nonTN remain non-TNBC", {
+    pheno <- make_pheno(
+        er = c(rep("ER+", 25), rep("ER-", 25)),
+        her2 = c(rep("HER2-", 20), rep("HER2+", 5), rep("HER2-", 20), rep("HER2+", 5)),
+        tn = c(rep("TN", 10), rep("nonTN", 10), rep(NA_character_, 10), rep("", 10), rep(" ", 10))
+    )
+
+    expect_auto_panel(pheno, full_mixed_panel, "mixed")
+})
+
+test_that("partly missing TN values select TNBC when all evaluable values are TN", {
+    pheno <- make_pheno(
+        er = rep("ER-", 20),
+        her2 = rep("HER2-", 20),
+        tn = c(rep("TN", 18), NA_character_, "")
+    )
+
+    expect_auto_panel(pheno, tn_panel, "TNBC")
+})