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  • 1
    Online Resource
    Online Resource
    Probabilistic pathway-based multimodal factor analysis
    Oxford University Press (OUP) ; 2024
    In:  Bioinformatics Vol. 40, No. Supplement_1 ( 2024-06-28), p. i189-i198
    Details
    In: Bioinformatics, Oxford University Press (OUP), Vol. 40, No. Supplement_1 ( 2024-06-28), p. i189-i198
    Abstract: Multimodal profiling strategies promise to produce more informative insights into biomedical cohorts via the integration of the information each modality contributes. To perform this integration, however, the development of novel analytical strategies is needed. Multimodal profiling strategies often come at the expense of lower sample numbers, which can challenge methods to uncover shared signals across a cohort. Thus, factor analysis approaches are commonly used for the analysis of high-dimensional data in molecular biology, however, they typically do not yield representations that are directly interpretable, whereas many research questions often center around the analysis of pathways associated with specific observations. Results We develop PathFA, a novel approach for multimodal factor analysis over the space of pathways. PathFA produces integrative and interpretable views across multimodal profiling technologies, which allow for the derivation of concrete hypotheses. PathFA combines a pathway-learning approach with integrative multimodal capability under a Bayesian procedure that is efficient, hyper-parameter free, and able to automatically infer observation noise from the data. We demonstrate strong performance on small sample sizes within our simulation framework and on matched proteomics and transcriptomics profiles from real tumor samples taken from the Swiss Tumor Profiler consortium. On a subcohort of melanoma patients, PathFA recovers pathway activity that has been independently associated with poor outcome. We further demonstrate the ability of this approach to identify pathways associated with the presence of specific cell-types as well as tumor heterogeneity. Our results show that we capture known biology, making it well suited for analyzing multimodal sample cohorts. Availability and implementation The tool is implemented in python and available at https://github.com/ratschlab/path-fa
    Type of Medium: Online Resource
    ISSN: 1367-4803 , 1367-4811
    URL: Article
    DOI: 10.1093/bioinformatics/btae216
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2024
    detail.hit.zdb_id: 1468345-3
    detail.hit.zdb_id: 1422668-6
    SSG: 12
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  • 2
    Online Resource
    Online Resource
    scROSHI: robust supervised hierarchical identification of single cells
    Oxford University Press (OUP) ; 2023
    In:  NAR Genomics and Bioinformatics Vol. 5, No. 2 ( 2023-03-29)
    Details
    In: NAR Genomics and Bioinformatics, Oxford University Press (OUP), Vol. 5, No. 2 ( 2023-03-29)
    Abstract: Identifying cell types based on expression profiles is a pillar of single cell analysis. Existing machine-learning methods identify predictive features from annotated training data, which are often not available in early-stage studies. This can lead to overfitting and inferior performance when applied to new data. To address these challenges we present scROSHI, which utilizes previously obtained cell type-specific gene lists and does not require training or the existence of annotated data. By respecting the hierarchical nature of cell type relationships and assigning cells consecutively to more specialized identities, excellent prediction performance is achieved. In a benchmark based on publicly available PBMC data sets, scROSHI outperforms competing methods when training data are limited or the diversity between experiments is large.
    Type of Medium: Online Resource
    ISSN: 2631-9268
    URL: Article
    DOI: 10.1093/nargab/lqad058
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2023
    detail.hit.zdb_id: 3009998-5
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  • 3
    Online Resource
    Online Resource
    SCIM: universal single-cell matching with unpaired feature sets
    Oxford University Press (OUP) ; 2020
    In:  Bioinformatics Vol. 36, No. Supplement_2 ( 2020-12-30), p. i919-i927
    Details
    In: Bioinformatics, Oxford University Press (OUP), Vol. 36, No. Supplement_2 ( 2020-12-30), p. i919-i927
    Abstract: Recent technological advances have led to an increase in the production and availability of single-cell data. The ability to integrate a set of multi-technology measurements would allow the identification of biologically or clinically meaningful observations through the unification of the perspectives afforded by each technology. In most cases, however, profiling technologies consume the used cells and thus pairwise correspondences between datasets are lost. Due to the sheer size single-cell datasets can acquire, scalable algorithms that are able to universally match single-cell measurements carried out in one cell to its corresponding sibling in another technology are needed. Results We propose Single-Cell data Integration via Matching (SCIM), a scalable approach to recover such correspondences in two or more technologies. SCIM assumes that cells share a common (low-dimensional) underlying structure and that the underlying cell distribution is approximately constant across technologies. It constructs a technology-invariant latent space using an autoencoder framework with an adversarial objective. Multi-modal datasets are integrated by pairing cells across technologies using a bipartite matching scheme that operates on the low-dimensional latent representations. We evaluate SCIM on a simulated cellular branching process and show that the cell-to-cell matches derived by SCIM reflect the same pseudotime on the simulated dataset. Moreover, we apply our method to two real-world scenarios, a melanoma tumor sample and a human bone marrow sample, where we pair cells from a scRNA dataset to their sibling cells in a CyTOF dataset achieving 90% and 78% cell-matching accuracy for each one of the samples, respectively. Availability and implementation https://github.com/ratschlab/scim. Supplementary information Supplementary data are available at Bioinformatics online.
    Type of Medium: Online Resource
    ISSN: 1367-4803 , 1367-4811
    URL: Article
    DOI: 10.1093/bioinformatics/btaa843
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2020
    detail.hit.zdb_id: 1468345-3
    detail.hit.zdb_id: 1422668-6
    SSG: 12
    Location Call Number Limitation Availability
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