AI 中文总结
研究针对稀疏计数矩阵，提出MMTB确定性二进制指纹框架，无需标签监督等。经列归一化和固定阈值映射样本，其汉明距离与L1距离有对应关系。在实验中，该框架在特定任务表现良好，适用于粗粒度分离与资源受限部署，还能减少内存。
AI 中文摘要
稀疏计数矩阵，从单细胞转录组到k-mer图谱和文档-词频，传统上通过主成分分析（PCA）降维的图聚类或连续嵌入空间中的迭代优化进行分析。我们引入了MMTB，这是一个确定性的无学习二进制表示框架，不需要标签监督、模型拟合或基于梯度的优化。按列进行最小-最大归一化，然后通过固定阈值将每个样本映射到一个温度计指纹，其汉明距离与归一化的L1距离显示出经验对应关系，在单细胞RNA-seq对上皮尔逊相关系数约为0.92。在一个有利的三细胞系混合物中，三阈值指纹在每个细胞188字节时实现了0.99的互信息（NMI）。在公平的汉明最近邻图加上莱顿读出的情况下，MMTB在这个粗略任务上接近PCA加上莱顿。在具有挑战性的组织样注释上，连续管道通常表现更好；对于PBMC Seurat，MMTB的NMI为0.39，而Scanpy为0.49，这突出表明MMTB适用于粗粒度分离和资源受限的部署，而不是细粒度亚型发现或作为连续嵌入的一般替代品。相对于密集的float32表示，MMTB指纹将内存减少了约10倍，同时提供固定宽度的可汉明索引的代码。提供了一个无标签适用性分数作为部署指南，而不是性能预测器。
英文摘要
Sparse count matrices from single-cell transcriptomes to k-mer profiles and document-term frequencies are conventionally analyzed via PCA-reduced graph clustering or iterative optimization in continuous embedding spaces. We introduce MMTB, a deterministic non-learned binary representation framework that requires no label supervision, model fitting, or gradient-based optimization. Column-wise Min-Max normalization followed by fixed cutoffs maps each sample to a thermometer fingerprint whose Hamming distances show empirical correspondence with normalized L1 distances, with Pearson correlation approximately 0.92 on single-cell RNA-seq pairs. In a favorable three-cell-line mixture, the 3-threshold fingerprint achieves NMI of 0.99 at 188 bytes per cell. Under a fair Hamming nearest-neighbor graph plus Leiden readout, MMTB approaches PCA plus Leiden on this coarse task. On challenging tissue-like annotations, continuous pipelines often lead; PBMC Seurat NMI is 0.39 for MMTB versus 0.49 for Scanpy, underscoring that MMTB is suited for coarse-grained separation and resource-constrained deployments rather than fine-grained subtype discovery or as a general replacement for continuous embeddings. Relative to dense float32 representations, MMTB fingerprints reduce memory by approximately 10-fold while providing fixed-width Hamming-indexable codes. PCA30 embeddings and sparse CSR may be smaller; we do not claim universal compression. A label-free suitability score is provided as a deployment guideline, not a performance predictor.
