🤖 AI Summary
This work addresses the high computational and memory costs of traditional methods—such as PCA or iterative optimization—for analyzing sparse count data from single-cell transcriptomics, k-mer spectra, and document term frequencies. The authors propose MMTB, a novel framework that deterministically maps samples to binary “thermometer” fingerprints via column-wise Min-Max normalization and a fixed threshold, eliminating the need for training, labels, or gradient-based optimization. To the best of our knowledge, this is the first method to achieve learning-free, deterministic binary encoding that preserves high correlation with L1 distance while enabling efficient Hamming-distance-based indexing. Evaluated on a mixture of three cell lines, MMTB achieves an NMI of 0.99 using only 188 bytes per cell—approximately a 10× reduction in memory compared to float32 embeddings—making it well-suited for resource-constrained, coarse-grained clustering tasks.
📝 Abstract
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.