Do Neftel-style GBM state calls replicate across platforms? A GlioState re-analysis (draft)Neftel 式 GBM 状态判定能否跨平台复现？GlioState 再分析（初稿）
Review pending待审阅 Version 0.1.0 · status: draft · last expert review: not yet reviewed版本 0.1.0 · 状态：初稿 · 最近专家审阅：尚未审阅
Research question研究问题
- Do GBM cell-state calls replicate on an independent platform, and does the state structure go beyond the signature genes?GBM 细胞状态判定能否在独立平台上复现，状态结构是否超出特征基因本身？
- How much does a trivial baseline recover?简单基线能还原多少？
Current understanding当前认识
In a GlioState re-analysis of public data (GSE131928), tumor-level state composition replicated moderately across Smart-seq2 and 10X (Spearman ρ=0.82 over 24 tumor×state values in 6 shared tumors; dominant state matched in 4 of 6), and a classifier using no signature genes recovered signature-based state calls on held-out 10X cells with balanced accuracy 0.75 (chance 0.25; single-marker baseline 0.53). This tests the reproducibility of state definitions only; it is not a test of any therapy-response or virtual-cell model, and the labels are not ground truth.在 GlioState 对公开数据（GSE131928）的再分析中，肿瘤水平的状态构成在 Smart-seq2 与 10X 之间中等程度复现（6 个共有肿瘤、24 个肿瘤×状态值的 Spearman ρ=0.82；优势状态 6 例中 4 例一致）；不使用任何特征基因的分类器在留出的 10X 细胞上还原了基于特征基因的状态判定，平衡准确率 0.75（随机水平 0.25；单标志基因基线 0.53）。这只检验状态定义的可重复性，并非对任何治疗反应或虚拟细胞模型的检验，且标签不是金标准。
Why this question: the library contains no GBM-specific benchmark of state-prediction models, and perturbation-prediction benchmarks in other systems report that deep models do not yet reliably beat simple baselines. A virtual cell for GBM would first need state labels that are reproducible across datasets and platforms.选择该问题的原因：文献库中没有 GBM 特异的状态预测模型基准；其他体系的扰动预测基准报告深度模型尚不能稳定超越简单基线。GBM 虚拟细胞首先需要跨数据集、跨平台可复现的状态标签。 VC0048 VC0049 VC0050 VC0059
Result in brief: state calls based on the Neftel meta-modules carry transcriptome-wide structure that transfers across platforms, clearly above chance and above a single-marker baseline, but with substantial tumor-to-tumor variability (per-tumor balanced accuracy 0.50–0.86) and notable confusion of OPC-like and AC-like cells with MES-like cells.结果概要：基于 Neftel 元模块的状态判定带有可跨平台迁移的全转录组结构，明显高于随机水平和单标志基因基线，但肿瘤间差异很大（单肿瘤平衡准确率 0.50–0.86），且 OPC 样、AC 样细胞常被误判为 MES 样。 VC0059
Evidence map证据图谱
Primary studies & datasets原始研究与数据集
- VC0059 An Integrative Model of Cellular States, Plasticity, and Genetics for Glioblastoma. — Neftel C et al., Cell, 2019
- VC0060 Charting the single-cell and spatial landscape of IDH-wild-type glioblastoma with GBmap. — Ruiz-Moreno C et al., Neuro-oncology, 2025
- VC0069 A single-cell atlas of glioblastoma evolution under therapy reveals cell-intrinsic and cell-extrinsic therapeutic targets. — Wang L, Jung J, Babikir H, Shamardani K, Jain S, Feng X, Gupta N, Rosi S, Chang S, Raleigh D, Solomon D, Phillips JJ, Diaz AA., Nature cancer, 2022
- VC0092 · preprint · 预印本 Mapping the spatial architecture of glioblastoma from core to edge delineates niche-specific tumor cell states and intercellular interactions — Khan SM et al., bioRxiv, 2025
Related models & tools相关模型与工具
- VC0048 Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines. — Ahlmann-Eltze C, Huber W, Anders S., Nature methods, 2025
- VC0049 Benchmarking foundation cell models for post-perturbation RNA-seq prediction. — Csendes G, Sanz G, Szalay KZ, Szalai B., BMC genomics, 2025
- VC0050 Zero-shot evaluation reveals limitations of single-cell foundation models. — Kedzierska KZ, Crawford L, Amini AP, Lu AX., Genome biology, 2025
Perturbation & validation evidence扰动与验证证据
- VC0070 In vivo perturb-seq of cancer and microenvironment cells dissects oncologic drivers and radiotherapy responses in glioblastoma. — Liu SJ et al., Genome biology, 2024
8 library items cited on this page; 1 are preprints (not peer reviewed).本页引用文献库条目 8 条，其中预印本（未经同行评审）1 条。
Methods and results (our own analysis)方法与结果（本站自行分析）
This is an exploratory re-analysis of public data run by GlioState, not a peer-reviewed result. Outputs: 这是 GlioState 对公开数据的探索性再分析，并非同行评审结论。输出文件：results/results.json · results/E1_shared_tumor_composition.csv · README.md (the analysis script itself is not published on this site)（分析脚本本身未在本站发布）
Data and licence数据与许可
Data: GEO GSE131928 (Neftel et al.; processed TPM only): Smart-seq2 7,930 cells (GSM3828672, ~311 MB gz) and 10X 16,201 cells (GSM3828673, ~358 MB gz) from 28 tumors; 6 tumors (MGH102, 105, 115, 124, 125, 143) were profiled on both platforms. Public GEO data; reuse terms were not independently checked. Signatures: a third-party GitHub transcription of the Neftel meta-modules (no licence file; not checked against the paper’s supplementary table). Marker genes quoted in the paper text were confirmed present in the expected modules; the AC module has 39 genes.数据：GEO GSE131928（Neftel 等；仅处理后 TPM）：Smart-seq2 7,930 个细胞（GSM3828672，约 311 MB）与 10X 16,201 个细胞（GSM3828673，约 358 MB），来自 28 例肿瘤；其中 6 例（MGH102、105、115、124、125、143）两种平台均有。GEO 公开数据，未独立核查再使用条款。特征基因：第三方 GitHub 对 Neftel 元模块的转录（无许可文件，未对照论文补充表核验）。已确认论文正文提到的标志基因位于预期模块，AC 模块仅 39 个基因。 VC0059
Design (fixed before viewing results)设计（查看结果前确定）
Cells were scored with a Tirosh-style expression-matched control-subtracted mean for four collapsed states (MES, AC, OPC, NPC) and assigned to the highest score. Immune-marker cells (PTPRC, CD14 or CD68 >2 on the log2(TPM/10+1) scale) were removed; no copy-number-based malignant-cell calling was done. E1: composition concordance between platforms in the 6 shared tumors. E2: a logistic-regression classifier trained on Smart-seq2 cells from the 22 tumors not profiled on 10X, using the 1,500 most variable genes excluding all signature and marker genes, tested on all 7,930 retained 10X cells (external platform; platforms standardised separately, no test labels used). Baselines: majority class, single marker per state (GFAP, OLIG1, DCX, VIM), label-permutation null (100 permutations); uncertainty by tumor-cluster bootstrap and leave-one-tumor-out.方法：对四个合并状态（MES、AC、OPC、NPC）采用 Tirosh 式“表达量匹配对照基因扣除”的均值评分，取最高分为状态。去除免疫标志基因细胞（PTPRC、CD14 或 CD68 在 log2(TPM/10+1) 尺度 >2）；未做基于拷贝数的恶性细胞识别。E1：6 个共有肿瘤在两平台间的构成一致性。E2：用 10X 中未出现的 22 例肿瘤的 Smart-seq2 细胞训练逻辑回归分类器，使用除全部特征基因和标志基因外变异最大的 1,500 个基因，在全部 7,930 个保留的 10X 细胞上测试（外部平台；两平台分别标准化，未使用测试标签）。基线：多数类、每状态单一标志基因（GFAP、OLIG1、DCX、VIM）、标签置换（100 次）；不确定性用按肿瘤聚类的 bootstrap 与留一肿瘤交叉验证。
Results结果
| Metric | Value |
|---|---|
| E1 composition concordance (6 shared tumors, 24 values) | Spearman ρ = 0.82; tumor-shuffle permutation p = 0.004; dominant state matched in 4/6 tumors |
| E2 balanced accuracy, signature-free classifier → 10X | 0.746 (tumor-cluster bootstrap 95% CI 0.67–0.84); macro-F1 0.69; Cohen κ 0.59 |
| Majority-class baseline | 0.25 (κ 0) |
| Single-marker baseline (GFAP/OLIG1/DCX/VIM) | 0.53 (macro-F1 0.46; κ 0.27) |
| Label-permutation null | mean 0.24; 95th percentile 0.27 |
| Leave-one-tumor-out within Smart-seq2 (22 tumors) | median 0.69 (IQR 0.58–0.73) |
| Per-tumor balanced accuracy on 10X (9 tumors) | range 0.50 (MGH126) – 0.86 (MGH115) |
| Cells with small score margin (<0.1; n=393) | balanced accuracy 0.40 vs 0.76 for confident cells |
Effect sizes should be read with the failures: in MGH105 the MES-like fraction was 26% on Smart-seq2 but 72% on 10X; in MGH125 the NPC-like fraction was 66% vs 35%; MGH124 OPC-like 29% vs 7%. The classifier labelled 632 of 2,667 reference AC-like cells and 591 of 1,805 reference OPC-like cells as MES-like. Overall MES-like fraction was higher on 10X (36%) than on Smart-seq2 (16%).效应量需与失败情形一并解读：MGH105 的 MES 样比例在 Smart-seq2 为 26%，在 10X 为 72%；MGH125 的 NPC 样比例为 66% 对 35%；MGH124 的 OPC 样为 29% 对 7%。分类器将 2,667 个参考 AC 样细胞中的 632 个、1,805 个参考 OPC 样细胞中的 591 个判为 MES 样。10X 的 MES 样总体比例（36%）高于 Smart-seq2（16%）。
A first run contained an error (10X data not log-transformed, immune filter too aggressive); it was corrected, and the flawed first-run output is not published on this site.首次运行存在错误（10X 数据未取对数、免疫过滤过严），已更正；错误版的首次运行输出不在本站发布。
What remains uncertain尚不确定之处
Labels are signature-score calls, not ground truth; E2 shows structure beyond the signature genes but is partly circular because the same labels train and score the classifier. Independent genotype or lineage-tracing validation was not available.标签是特征基因评分的判定而非金标准；E2 显示特征基因之外也存在结构，但训练与评估使用同一套标签，部分循环。未能使用独立的基因型或谱系示踪验证。 VC0059
Signatures are an unverified third-party transcription; the official supplementary table was not used.特征基因为未经核验的第三方转录，未使用官方补充表。
Malignant cells were not identified by copy-number inference; the crude marker filter removed 51% of 10X cells and 20% of Smart-seq2 cells, and residual non-malignant astrocyte/oligodendrocyte-lineage cells may inflate AC/OPC/MES calls. The platform difference in MES-like fraction may reflect technical effects (e.g. stress response, ambient RNA) rather than biology; this was not tested.未通过拷贝数推断识别恶性细胞；粗略标志基因过滤去除了 51% 的 10X 细胞与 20% 的 Smart-seq2 细胞，残留的非恶性星形/少突谱系细胞可能抬高 AC/OPC/MES 判定。两平台 MES 样比例差异可能来自技术因素（如应激反应、环境 RNA）而非生物学，未检验。
Only 28 tumors, 6 shared across platforms, mostly from one centre; results from GBmap (an independent 26-dataset atlas) or other cohorts were not tested here. Spatial and therapy-associated states were not assessed.仅 28 例肿瘤，其中 6 例两平台共有，且主要来自同一中心；未在 GBmap（独立的 26 数据集图谱）或其他队列中检验，也未评估空间及治疗相关状态。 VC0060 VC0069
This analysis does not test any perturbation or therapy-response model, and does not evaluate whether state transitions under treatment can be predicted.本分析未检验任何扰动或治疗反应模型，也未评估治疗下状态转变是否可预测。 VC0070 VC0092 · preprint · 预印本
For research and education only. Not medical advice and not a recommendation of any diagnosis, treatment or product. Patients should consult their own physicians.仅供科研与教育参考，不构成医疗建议，也不构成对任何诊疗或产品的推荐。患者请咨询主治医生。