One-Way Matching of Datasets with Low Rank Signals

Chen, Shuxiao; Jiang, Sizun; Ma, Zongming; Nolan, Garry P.; Zhu, Bokai

doi:10.48550/arxiv.2204.13858

Cited by 5 publications

(4 citation statements)

References 33 publications

(44 reference statements)

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“…For instance, a model for correlated randomly growing graphs was studied in [35], graph matching for correlated stochastic block model was studied in [36] and graph matching for correlated random geometric graphs was studied in [40]. In a very recent work [9], a related matching problem (albeit somewhat different from graph matching) was studied and it seems the method developed therein enjoyed direct and successful applications to single-cell problems.…”

Section: Background and Related Resultsmentioning

confidence: 99%

Matching recovery threshold for correlated random graphs

Ding¹,

Du²

2022

Preprint

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For two correlated graphs which are independently sub-sampled from a common Erdős-Rényi graph G(n, p), we wish to recover their latent vertex matching from the observation of these two graphs without labels. When p = n −α+o(1) for α ∈ (0, 1], we establish a sharp information-theoretic threshold for whether it is possible to correctly match a positive fraction of vertices. Our result sharpens a constant factor in a recent work by Wu, Xu and Yu.

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Section: Background and Related Resultsmentioning

confidence: 99%

Matching recovery threshold for correlated random graphs

Ding¹,

Du²

2022

Preprint

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“…As the columns in and in have correspondences, we can compute an n y × n z distance matrix D ° where measures the distance between the i -th row in and the j -th row in after projecting to respective leading singular subspaces. We obtain an initial matching as the solution to the linear assign-ment problem (33, 60): …”

Section: Methodsmentioning

confidence: 99%

Integration of spatial and single-cell data across modalities with weak linkage

Chen

Zhu

Huang

et al. 2023

Preprint

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Single-cell sequencing methods have enabled the profiling of multiple types of molecular readouts at cellular resolution, and recent developments in spatial barcoding, in situ hybridization, and in situ sequencing allow such molecular readouts to retain their spatial context. Since no technology can provide complete characterization across all layers of biological modalities within the same cell, there is pervasive need for computational cross-modal integration (also called diagonal integration) of single-cell and spatial omics data. For current methods, the feasibility of cross-modal integration relies on the existence of highly correlated, a priori ''linked'' features. When such linked features are few or uninformative, a scenario that we call ''weak linkage'', existing methods fail. We developed MaxFuse, a cross-modal data integration method that, through iterative co-embedding, data smoothing, and cell matching, leverages all information in each modality to obtain high-quality integration. MaxFuse is modality-agnostic and, through comprehensive benchmarks on single-cell and spatial ground-truth multiome datasets, demonstrates high robustness and accuracy in the weak linkage scenario. A prototypical example of weak linkage is the integration of spatial proteomic data with single-cell sequencing data. On two example analyses of this type, we demonstrate how MaxFuse enables the spatial consolidation of proteomic, transcriptomic and epigenomic information at single-cell resolution on the same tissue section.

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“…m and in Z∘ m have correspondences, we can compute an n y × n z distance matrix D ∘ where D ∘ ij measures the distance between the i-th row in Ỹ ∘ m and the j-th row in Z∘ m after projecting to respective leading singular subspaces. We obtain an initial matching Π∘ as the solution to the linear assignment problem 32,64 :…”

Section: Initial Matching Via Linear Assignment As the Columns In ỹ ∘mentioning

confidence: 99%

Integration of spatial and single-cell data across modalities with weakly linked features

Chen,

Zhu,

Huang

et al. 2023

Nat Biotechnol

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Although single-cell and spatial sequencing methods enable simultaneous measurement of more than one biological modality, no technology can capture all modalities within the same cell. For current data integration methods, the feasibility of cross-modal integration relies on the existence of highly correlated, a priori ‘linked’ features. We describe matching X-modality via fuzzy smoothed embedding (MaxFuse), a cross-modal data integration method that, through iterative coembedding, data smoothing and cell matching, uses all information in each modality to obtain high-quality integration even when features are weakly linked. MaxFuse is modality-agnostic and demonstrates high robustness and accuracy in the weak linkage scenario, achieving 20~70% relative improvement over existing methods under key evaluation metrics on benchmarking datasets. A prototypical example of weak linkage is the integration of spatial proteomic data with single-cell sequencing data. On two example analyses of this type, MaxFuse enabled the spatial consolidation of proteomic, transcriptomic and epigenomic information at single-cell resolution on the same tissue section.

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One-Way Matching of Datasets with Low Rank Signals

Cited by 5 publications

References 33 publications

Matching recovery threshold for correlated random graphs

Matching recovery threshold for correlated random graphs

Integration of spatial and single-cell data across modalities with weak linkage

Integration of spatial and single-cell data across modalities with weakly linked features

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