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  • 1
    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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  • 2
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    Online Resource
    MO442ACUTE ADVERSE EFFECTS OF LOW POTASSIUM ON HEART AND KIDNEY*
    Oxford University Press (OUP) ; 2021
    In:  Nephrology Dialysis Transplantation Vol. 36, No. Supplement_1 ( 2021-05-29)
    Details
    In: Nephrology Dialysis Transplantation, Oxford University Press (OUP), Vol. 36, No. Supplement_1 ( 2021-05-29)
    Abstract: Chronic hypokalemia causes kidney fibrosis with cystic lesions and arterial hypertension. In contrast, potassium-rich diet lowers blood pressure. The acute effects of hypo- and hyperkalemia on heart and kidney are not well understood. Method Wild-type mice were fed with low (LK), normal (NK) and high (HK) potassium diet for 4 and 20 days. Kidneys were examined for site of acute injury, inflammation and fibrosis. Blood analysis of electrolytes and kidney parameters were analyzed. Echocardiography and ECG were used to assess heart function. Further, KCNJ10 knockout mice were used to investigate kidney damage in a genetically induced hypokalemia model. Results Proximal tubule injury as detected by KIM-1+ staining and yH2AX+ DNA-damage was observed after 4 and 20 days of LK diet. Injury was associated with strong Ki-67+ proliferation of proximal tubule cells. No injury was detected in mice on NK and HK diet. After 20 days of LK diet, F4/80+ inflammation and aSMA+ extracellular matrix accumulation, typical for fibrosis, were observed. LK mice developed polyurie, volume depletion, loss of body weight and high BUN. Lower cardiac output and signs of myocardial stress was seen in echocardiography and ECG. Consistent with WT mice on LK diet, KCNJ10 knockout mice developed same pattern of kidney injury. Nine months after deletion of KCNJ10, cysts were observed in the proximal tubule in outer medzulla. Conclusion Acute hypokalemia causes kidney injury and myocardial stress. Cystic lesions originate from late proximal tubule. Hypokalemia should be corrected rapidly to stop progression into kidney fibrosis.
    Type of Medium: Online Resource
    ISSN: 0931-0509 , 1460-2385
    URL: Article
    DOI: 10.1093/ndt/gfab090.004
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2021
    detail.hit.zdb_id: 1465709-0
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  • 3
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    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
    SSG: 12
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