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节点 n27

谱系标记驱动的类型族抽样重加权（心脏↑1.6、边缘组织↓0.25）+ 类型内增殖细胞级加权（β=-1），copy_last 官方阶段，不改表达值。

方法说明?节点程序自带的 METHOD.md：这个程序做了什么、为什么。

来自 programs.git 4e2cf3c209:solution/METHOD.md

调研员的计划

代码改动?这个节点的程序和父节点程序的逐行差别：绿色是新增，红色是删除。

对比：这个提交的上一版（种子程序：相对空仓库）。改动的文件：solution/METHOD.md +31 −0、solution/run.py +196 −0

diff --git a/solution/METHOD.md b/solution/METHOD.mdnew file mode 100644index 0000000..848253a--- /dev/null+++ b/solution/METHOD.md@@ -0,0 +1,31 @@+谱系标记驱动的类型族抽样重加权（心脏↑1.6、边缘组织↓0.25）+ 类型内增殖细胞级加权（β=-1），copy_last 官方阶段，不改表达值。++# 方法（draft 家族：lineage-marker family reweighting）++骨架：copy_last_official —— `inputs_by_time(include_external=False)` 取最新官方输入阶段的全部细胞作为池（proxy/final 为官方阶段；X3 无 external 标记，自动取最新输入 E9.0）。表达值不做任何修改。++三个标记基因集（通用基因功能/谱系知识，不来自保留阶段测量；来源：标准心脏发育生物学——心肌收缩/肌小节基因、Isl1/Tbx1/Wt1 心脏祖细胞轴、Sox/Pax/Krt/Tbx6 等背侧-表面外胚层-体节-胚外标记）：++1. CARDIAC_MARKERS（28 基因，心肌 TF + 肌小节/收缩）：逐基因跨细胞 z 化后取均值得细胞分，按类型求均值再跨类型 z 化。`cardiac_z > 0.5` 的类型 → 家族权重 1+W=1.6。+2. PROG_MARKERS（17 基因，SHF/心外膜祖细胞）：同上，`prog_z > 1.0` → 1.6。+3. EDGE_MARKERS（39 基因，神经管/表面外胚层/体节/胚外）：`edge_z > 0.6` → 0.25（心区解剖时部分被移除的周边组织；edge 覆盖 cardiac/prog，防止 SE/EXEM 因 Bmp4/Hand1 假阳性）。++抽样：类型配额 = counts × fam，最大余数法整数分配；类型内细胞权重 `exp(β_cell·z_cell)`（z_cell 为类型内增殖分 z 化，PROLIF_GENES 40 个细胞周期基因），β_cell=-1；Efraimidis–Spirakis 加权无放回抽样至 `target_n_cells`。全程 `np.random.default_rng(seed)`，确定性。++退化保护：标记基因在面板中命中 <10（edge/cardiac）或 <8（prog）、或类型数 ≤3 时该轴自动关闭 → 纯 copy_last（fam 全 1，X3 上三型均为心脏型，fam 一致即等价 copy_last）。++# 已验证（A 半，seed 0）++- proxy：blanket 非心脏降权（所有 cardiac_z<-0.5 → 0.4）= 45.0，cell_state 崩 → 说明 Foregut/Endothelium/Blood/NCC 不能降权；+- proxy：全局 exp(β_type·z) E-S 抽样（β_type=-4）= 36.0，组成极端塌缩 → 类型级权重必须经配额分配而非全局 E-S；+- proxy：本方案 v2（cardiac/prog/edge 三集 + β_cell=-1）= **54.22**（cell_state 53.7, covariation 51.5, direction 60.3, de 51.0），可复现；+- proxy：v2+Pericardium(prog>0.3)+NT 全删 = 53.52，更差 → 回退 PROG_HI=1.0、不删 NT；+- proxy：v2+类型级增殖 exp(-4·raw) clip±1.5 = 44.27，raw 分尺度大导致 CM 全占 → 类型级增殖轴默认关闭（BETA_TYPE=0）；+- X3 = 50.0（等价 copy_last）；proxy2 / X3 均 vec-check ok（proxy2 输出与 proxy 相同：只用官方阶段）。++# 未验证 / 弱点++- 低于表中增殖重加权节点（57.11）：本 draft 家族单独不及 proliferation 轴；但 direction（60.3 vs 56.2）和 de_recovery（51.0 vs 50）略优，提示 lineage-family 轴与增殖轴可能互补——组合时须用配额分配而非全局 E-S，且类型级增殖分需小尺度（raw×β 约 ±0.5 内，clip 后重扫）。+- edge 阈值 0.6 把 NCC（0.75）也降到 0.25，种子证据提示 NCC 应保持 1.0，未及测试。+- Endothelium/pSHF 未被任何集命中（种子中为心脏侧 ×1.6），数据驱动标记无法覆盖，未测试补充内皮标记集。+- W=1.6/0.25 直接取自方向库种子量级，未做 W 网格。diff --git a/solution/run.py b/solution/run.pynew file mode 100644index 0000000..d58da3d--- /dev/null+++ b/solution/run.py@@ -0,0 +1,196 @@+"""Lineage-family composition reweighting + proliferation two-level sampling.++Draft family: type-level sampling weights come from a cardiac-lineage marker+score computed on the input data itself (not hardcoded type names), combined+with the proven two-level proliferation reweighting (type-level + within-type+cell-level) and Efraimidis-Spirakis weighted sampling without replacement.+Expression values are never modified.+"""++from __future__ import annotations++import argparse++from pathlib import Path++import numpy as np+from scipy import sparse++from src.task1_temporal import view_io++# --- parameters (scanned; defaults set to best measured) ---+import os++BETA_TYPE = float(os.environ.get("N27_BETA_TYPE", 0.0))+BETA_CELL = float(os.environ.get("N27_BETA_CELL", -1.0))+W_CARDIAC = float(os.environ.get("N27_W", 0.6))   # family bonus for cardiac types+NONCARDIAC_FLOOR = float(os.environ.get("N27_FLOOR", 0.2))+CARDIAC_HI = float(os.environ.get("N27_HI", 0.5))+PROG_HI = float(os.environ.get("N27_PROG_HI", 1.0))+EDGE_EXTREME = float(os.environ.get("N27_EDGE_EXT", 99.0))+EDGE_HI = float(os.environ.get("N27_EDGE_HI", 0.6))+EDGE_WEIGHT = float(os.environ.get("N27_EDGE_W", 0.25))++# Core cardiac-lineage markers (general gene-function knowledge: cardiac TFs,+# sarcomere / contraction genes; sources: standard cardiac developmental+# biology, e.g. Nkx2-5/Tbx5/Gata4 cardiac TF network, sarcomeric myosins).+CARDIAC_MARKERS = [+    "Nkx2-5", "Tbx5", "Gata4", "Isl1", "Hand1", "Hand2", "Mef2c", "Ttn",+    "Tnnt1", "Tnnt2", "Tnni1", "Tnni2", "Tnnc1", "Myh6", "Myh7", "Myl4",+    "Myl7", "Myl9", "Actc1", "Actn2", "Nppa", "Ryr2", "Slc8a1", "Casq2",+    "Bmp10", "Mybpc3", "Corin", "Ankrd1",+]++# Core cell-cycle / proliferation genes (general knowledge: mitotic cyclins,+# CDKs, replication licensing, chromatin condensation).+# Dorsal / surface-ectodermal / extraembryonic markers: tissues partially+# removed by heart-centred dissection (general anatomy knowledge).+EDGE_MARKERS = [+    "Sox2", "Sox1", "Sox3", "Pax3", "Pax6", "Pax7", "Otx2", "Irx3", "Lhx5",+    "Zic1", "Zic2", "Tubb3", "Ncam1", "Elavl2", "Elavl3", "Msx1", "Gbx2",+    "Tfap2a", "Trp63", "Krt14", "Krt5", "Grhl1", "Grhl3", "Dlx3", "Tbx6",+    "Msgn1", "Dll1", "Dll3", "Lfng", "Hes7", "Ripply2", "Foxf1", "Hand1",+    "Bmp4", "Gata3", "Prrx1", "Twist1", "Alx4", "Alx1",+]++# Second-heart-field / proepicardial progenitor markers (Isl1, Tbx1, Wt1,+# Tbx18 axis; general cardiac development knowledge).+PROG_MARKERS = [+    "Isl1", "Tbx1", "Fgf10", "Fgf8", "Wt1", "Tbx18", "Upk3b", "Msln",+    "Lrrn4", "Gata5", "Bmp4", "Six2", "Sema3d", "Kdr", "Lhx2", "Nkx2-6",+    "Cited1",+]++PROLIF_GENES = [+    "Mki67", "Top2a", "Cdk1", "Ccna2", "Ccnb1", "Ccne1", "Ccnd1", "Ccnd2",+    "Ccnd3", "Pcna", "Mcm2", "Mcm3", "Mcm4", "Mcm5", "Mcm6", "Mcm7", "Rrm1",+    "Rrm2", "Birc5", "Aurka", "Aurkb", "Plk1", "Bub1", "Cenpf", "Cenpe",+    "Hist1h1c", "Hmgb2", "Tyms", "Pola1", "Prim1", "Cdc20", "Kif11", "Nusap1",+    "Ube2c", "Tpx2", "Kif23", "Anln", "Cdca3", "Aspm", "Cdkn3",+]+++def _zscore_cols(X: sparse.csr_matrix, idx_cols: np.ndarray) -> np.ndarray:+    """Per-cell mean of column-z-scored expression for the given gene columns."""+    sub = X[:, idx_cols]+    mean = np.asarray(sub.mean(axis=0)).ravel()+    sq = np.asarray(sub.multiply(sub).mean(axis=0)).ravel()+    sd = np.sqrt(np.maximum(sq - mean ** 2, 0))+    keep = sd > 1e-8+    if keep.sum() == 0:+        return np.zeros(X.shape[0], dtype=np.float64)+    sub = sub[:, keep].tocsc()+    m = mean[keep]+    s = sd[keep]+    sub.data = (sub.data - np.repeat(m, np.diff(sub.indptr))) / np.repeat(s, np.diff(sub.indptr))+    return np.asarray(sub.mean(axis=1)).ravel()+++def _gene_cols(genes: list[str], wanted: list[str]) -> np.ndarray:+    pos = {g: i for i, g in enumerate(genes)}+    return np.array([pos[g] for g in wanted if g in pos], dtype=np.int64)+++def _z(x: np.ndarray) -> np.ndarray:+    s = x.std()+    return (x - x.mean()) / s if s > 1e-12 else np.zeros_like(x)+++def _largest_remainder(weights: np.ndarray, n: int) -> np.ndarray:+    weights = np.clip(np.asarray(weights, dtype=np.float64), 0, None)+    if n <= 0 or weights.sum() <= 0:+        return np.zeros(len(weights), dtype=int)+    raw = weights / weights.sum() * n+    out = np.floor(raw).astype(int)+    short = int(n - out.sum())+    order = np.argsort(-(raw - out))+    for i in order[:short]:+        out[i] += 1+    return out+++def main() -> None:+    ap = argparse.ArgumentParser()+    ap.add_argument("--data", required=True)+    ap.add_argument("--out", required=True)+    ap.add_argument("--seed", type=int, default=0)+    args = ap.parse_args()++    view = Path(args.data)+    manifest = view_io.load_manifest(view)+    genes = view_io.panel_genes(view, manifest)++    inputs = view_io.inputs_by_time(manifest, include_external=False)+    if not inputs:+        inputs = view_io.inputs_by_time(manifest, include_external=True)+    last = inputs[-1]+    adata = view_io.read_stage(view, last, genes)+    X = adata.X.tocsr()+    labels = view_io.labels_of(adata)++    rng = np.random.default_rng(args.seed)+    n_out = view_io.target_n_cells(manifest, X.shape[0])++    types = np.unique(labels)+    type_of = np.searchsorted(types, labels)++    # --- axis 1 (draft family): lineage-marker type-family weights ---+    def _type_z(markers, min_genes):+        cols = _gene_cols(genes, markers)+        if cols.size < min_genes or len(types) <= 3:+            return None+        sc = _zscore_cols(X, cols)+        return _z(np.array([sc[type_of == i].mean() for i in range(len(types))]))++    fam = np.ones(len(types), dtype=np.float64)+    card_z = _type_z(CARDIAC_MARKERS, 10)+    if card_z is not None:+        fam[card_z > CARDIAC_HI] = 1.0 + W_CARDIAC+    prog_z = _type_z(PROG_MARKERS, 8)+    if prog_z is not None:+        fam[prog_z > PROG_HI] = 1.0 + W_CARDIAC+    edge_z = _type_z(EDGE_MARKERS, 10)+    if edge_z is not None:+        fam[edge_z > EDGE_HI] = EDGE_WEIGHT          # overrides cardiac/prog+        fam[edge_z > EDGE_EXTREME] = 0.02            # tissue absent at target++    # --- axis 2: proliferation (type level on raw scale + within-type cell) ---+    cell_z = np.zeros(X.shape[0], dtype=np.float64)+    prolif_tw = np.ones(len(types), dtype=np.float64)+    pcols = _gene_cols(genes, PROLIF_GENES)+    if pcols.size >= 5:+        prolif = _zscore_cols(X, pcols)+        tm = np.array([prolif[type_of == i].mean() for i in range(len(types))])+        prolif_tw = np.exp(np.clip(BETA_TYPE * (tm - prolif.mean()), -1.5, 1.5))+        for i in range(len(types)):+            m = type_of == i+            cell_z[m] = _z(prolif[m])+    logw_cell = np.clip(BETA_CELL * cell_z, -8, 8)++    # type allocation: counts x family weight (largest remainder),+    # then within each type E-S weighted sampling without replacement+    counts = np.array([(type_of == i).sum() for i in range(len(types))], dtype=np.float64)+    n_out = min(n_out, X.shape[0])+    alloc = _largest_remainder(counts * fam * prolif_tw, n_out)+    blocks = []+    for i in range(len(types)):+        n = int(alloc[i])+        if n <= 0:+            continue+        idx = np.flatnonzero(type_of == i)+        w = np.exp(logw_cell[idx])+        if n <= idx.size:+            u = rng.random(idx.size)+            key = np.log(np.maximum(u, 1e-300)) / np.maximum(w, 1e-12)+            sel = idx[np.argpartition(-key, n - 1)[:n]]+        else:+            p = w / w.sum()+            sel = rng.choice(idx, size=n, replace=True, p=p)+        blocks.append(sel)+    take = np.sort(np.concatenate(blocks)) if blocks else np.zeros(0, dtype=np.int64)+    out_X = X[take]+    view_io.write_prediction(out_X, genes, args.out, seed=args.seed)+++if __name__ == "__main__":+    main()

调研来源?调研员查到并用到的知识条目和文献检索结果（只列标题和编号）。

用到的知识库条目

分析结果?分析员写的 ANALYSIS.json：改了什么、各组分数怎么变、假设是否成立、经验和下一步建议。

对话摘要?每个角色和大模型对话的统计：轮数、工具调用、用时、token 数和最后的回答摘录；原始记录只给路径。

只给统计和最后回答的摘录；完整对话请到原始记录位置里列出的文件看。

分析员

ANALYSIS.json written

工程师

**draft 完成。** `solution/run.py` + `METHOD.md` 已就位，三视图（proxy/proxy2/X3）均跑通并通过 vec-check，运行约 4 秒。

- **做了什么**：新家族——谱系标记驱动的类型族抽样重加权。三个标记基因集（心肌、SHF/心外膜祖细胞、背侧/表面/胚外边缘组织）在输入数据上现场算类型级 z 分 → 心脏族 ×1.6、边缘族 ×0.25 配额分配，叠加类型内增殖细胞级加权（β_cell=-1，E-S 无放回），copy_last 官方阶段、不改表达。
- **查分（A 半，seed 0）**：proxy **54.22**（cell_state 53.7 / cov 51.5 / direction 60.3），X3 50.0，proxy2 输出与 proxy 相同（预计 54.2）；节点分估计 ≈52.8。5 次查分中排除了三条死路：blanket 非心脏降权（45.0）、全局 E-S 类型级 exp 权重（36.0）、raw 尺度类型级增殖（44.3）。
- **下一步最值得试**：把本 lineage-family 轴（direction 60.3 全表最高）与表中已验证的增殖两级重加权（57.11）**在配额分配框架下**组合，类型级增殖分限制在 ±0.5 小尺度；以及把 NCC 排除出 edge 降权、补内皮标记集覆盖 Endothelium/pSHF。

调研员

{"name":"谱系标记驱动的类型族组成重加权+增殖细胞级精调抽样","motivation":"当前最优节点(18/21/22, rank3≈54.66)全部基于增殖重加权(β_type=-4, β_cell=-1)，cell_state≈57.6但covariation≈52.4、de_recovery≈51.7仍弱。方向库T1-01种子heart_jcf_peri用手工类型族权重(心脏×1.6、外胚层×0.25)在proxy达55.97，说明类型族级别的组成偏移是独立于增殖轴的有效信号。实验表中无任何节点尝试过基于心脏谱系标记的类型族权重；节点19/21的Reactome Developmental Biology轴是在表达调整层面用的，不是抽样权重层面。本方案将谱系归属作为类型级抽样权重的来源，与已验证的增殖细胞级权重正交组合。","approach":"步骤：\n1. 读取最新官方输入阶段，用view_io获取细胞与类型标签。若视图无官方阶段或退化（X3），直接copy_last。\n2. 定义心脏谱系基因集：从prior/Reactome取'Cardiac muscle contraction'、'Heart development'相关通路基因（约50-200个）；若无prior文件则用硬编码核心心脏TF列表（Nkx2-5, Gata4, Tbx5, Mef2c, Isl1, Hand1, Hand2, T）作为种子。\n3. 对每个类型计算谱系得分：该类型细胞中心脏基因集的平均表达z-score（类型内标准化后取均值）。得分>0的类型归为cardiac家族，得分<-0.5归为non-cardiac，其余为neutral。\n4. 类型级权重：cardiac家族 w_type=1+W（W初始0.6，搜索范围[0.3,1.2]步长0.3）；non-cardiac w_type=max(0.2, 1-W)；neutral w_type=1.0。权重归一化使总和不变。\n5. 细胞级权重（已验证机制）：在类型内用增殖基因集（Reactome Cell Cycle）打分，w_cell=exp(-β_cell·prolif_score)，β_cell=-1（继承树中验证值）。若单阶段无增殖区分度则β_cell=0。\n6. 最终抽样权重=w_type×w_cell，用Efraimidis-Spirakis加权无放回抽样，目标细胞数=输入细胞数（保持总量不变）。\n7. 输出抽样后的细胞，表达值不做任何修改。\n\n单输入阶段退路（proxy）：步骤2-6正常执行，只用E8.5一个阶段。\n两输入阶段（proxy2/final）：仍只用最新官方阶段做抽样；外部Qiu E9.0心脏细胞仅用于辅助验证心脏基因集表达的合理性，不作为输出。\n\n快速筛选：先在proxy上跑3组W值{0.3,0.6,1.0}×β_cell∈{-1,0}，用vec-score查A半，选最优组合后在proxy2和X3上验证。预期X3退化为copy_last（50.0），proxy/proxy2有增量。若W=0.6+β_cell=-1的proxy分数≤55.5（低于种子55.97+噪声），则说明类型族权重定义不当，回退纯增殖方案。","expected_groups":["cell_state","direction"],"risks":"1. 心脏基因集覆盖不足或类型标签粒度不够导致谱系得分区分度差——Engineer应在步骤3后打印各类型得分，若所有类型得分差异<0.3则改用更宽的mesoderm基因集。2. 类型族权重与增殖权重方向冲突（心脏类型恰好高增殖）——若出现，尝试β_cell=0只保留类型级权重。3. X3视图类型集合不同可能导致基因集匹配失败——必须加保护：若心脏基因集在视图基因中覆盖<10个则回退copy_last。4. 权重过强导致细胞多样性丧失、covariation下降——监控抽样后类型数，若低于输入的60%则降低W。","sources":[]}