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Chunk #20 — Results — Robustness and benchmarking analysis

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Dictionary learning for integrative, multimodal and scalable single-cell analysis.
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As a second quantitative benchmark with ground-truth data, we pursued a similar strategy using a recently published Paired-Tag dataset26, where individual histone modification binding profiles via scCUT&Tag were simultaneously measured with RNA transcriptomes. Since each Paired-Tag experiment was performed with biological replicates, we used one replicate as a multi-omic bridge dataset and split the other replicate into separate modalities for benchmarking. We performed cross-modality integration between scRNA-seq and scCUT&Tag for active histone marks (H3K27ac), repressive histone marks (H3K27me3), and enhancer histone marks (H3K4me1). In each case, bridge integration successfully integrated cells across modalities, and returned the highest Jaccard similarity and classification metrics between matched scRNA-seq and scCUT&Tag profiles (Fig. 3c and Supplementary Fig. 3d,e and Supplementary Table 1).