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  "Title": "Map Single Cells to Reference Gene Expression Programs",
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  "Date": "2026-01-26",
  "Authors@R": "c(\nperson(\"Zaoqu\", \"Liu\", email = \"liuzaoqu@163.com\", role = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0002-0452-742X\"))\n)",
  "Description": "Maps single-cell RNA sequencing data to reference gene\nexpression programs (GEPs) using non-negative matrix\nfactorization. Enables cell type annotation and state\ncharacterization by projecting query cells onto pre-built or\ncustom reference programs. Includes tools for building\nconsensus references from multiple datasets. Features C++\naccelerated NNLS solvers and built-in machine learning models\nfor cell type prediction.",
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  "URL": "https://github.com/Zaoqu-Liu/CellProgramMapper,\nhttps://zaoqu-liu.github.io/CellProgramMapper/",
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    "message": "fix: 深度修复三个关键问题 + 科学准确性改进\n\n修复问题:\n1. Score 计算失败 - 修复 data.frame 初始化时 rownames 为空导致的 0 行问题\n2. 路径展开失败 - 在所有文件操作函数中添加 path.expand() 处理 ~ 路径\n3. Python 依赖 - 为 TCAT.V1 等参考提供纯 R 内置模型替代 Python sklearn\n\n科学准确性修复:\n- 新增 sklearn_standardize() 函数，使用 population SD (ddof=0, 除以 n)\n- 修复 R scale() 与 sklearn StandardScaler 的不一致问题\n  (R 使用 n-1 分母，sklearn 使用 n 分母)\n- 确保 lineage 预测结果与 Python 实现数值一致\n\n新增测试:\n- sklearn_standardize 与 sklearn StandardScaler 行为一致性验证\n- 边界情况处理 (常量列、单行矩阵)\n- compute_scores 空矩阵和无行名处理\n- ensure_dir 路径展开验证\n\n测试结果: 247 PASS, 0 FAIL\n",
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