SCINA: Semi-Supervised Analysis of Single Cells <i>in silico</i>

Zhang, Ze; Luo, Majing; Zhong, Xue; Choi, Jin Huk; Ma, Yuanqing; Mahrt, Elena; Guo, Wenfei; Stawiski, Eric; Wang, Stacy; Modrušan, Zora; Seshagiri, Somasekar; Kapur, Payal; Wang, Xinlei; Hon, Gary C.; Brugarolas, James; Wang, Tao

doi:10.1101/559872

Cited by 44 publications

(65 citation statements)

References 39 publications

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“…Pseudo bulk analysis of GSE145926 data was performed by adding counts from the different cell subtypes and normalized using log2(CPM+1). Epithelial cells were identified using SFTPA1, SFTPB, AGER, AQP4, SFTPC, SCGB3A2, KRT5, CYP2F1, CCDC153, and TPPP3 genes using SCINA algorithm (Zhang et al, 2019). Pseudo bulk datasets were prepared by adding counts from the selected cells and normalized using log (CPM+1).…”

Section: Single Cell Rna Seq Data Analysismentioning

confidence: 99%

Adult Stem Cell-derived Complete Lung Organoid Models Emulate Lung Disease in COVID-19

Tindle

Fuller

Fonseca

et al. 2020

Preprint

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SARS-CoV-2, the virus responsible for COVID-19, causes widespread damage in the lungs in the setting of an overzealous immune response whose origin remains unclear. We present a scalable, propagable, personalized, cost-effective adult stem cell-derived human lung organoid model that is complete with both proximal and distal airway epithelia. Monolayers derived from adult lung organoids (ALOs), primary airway cells, or hiPSC-derived alveolar type-II (AT2) pneumocytes were infected with SARS-CoV-2 to create in vitro lung models of COVID-19. Infected ALO-monolayers best recapitulated the transcriptomic signatures in diverse cohorts of COVID-19 patient-derived respiratory samples. The airway (proximal) cells were critical for sustained viral infection whereas distal alveolar differentiation (AT2→AT1) was critical for mounting the overzealous host immune response in fatal disease; ALO monolayers with well-mixed proximodistal airway components recapitulated both. Findings validate a human lung model of COVID-19 which can be immediately utilized to investigate COVID-19 pathogenesis, and vet new therapies and vaccines.

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Section: Single Cell Rna Seq Data Analysismentioning

confidence: 99%

Adult Stem Cell-derived Complete Lung Organoid Models Emulate Lung Disease in COVID-19

Tindle

Fuller

Fonseca

et al. 2020

Preprint

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“…Currently, only a few semi-automated approaches for cell label predictions are available, viz. Automated Cell-type Discovery and Classification (ACDC) 17 , Semi-supervised Category Identification and Assignment (SCINA) 18 , DeepCyTOF 19 and Linear Discriminant Analysis (LDA) 10 . Both ACDC and SCINA, uses the list of pre-defined markers for a given cell type to annotate the unsupervised cell cluster(s) that expresses these signature markers.…”

Section: Introductionmentioning

confidence: 99%

CyAnno: A semi-automated approach for cell type annotation of mass cytometry datasets

Kaushik

Dunham

et al. 2020

Preprint

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For immune system monitoring in large-scale studies at the single-cell resolution using CyTOF, (semi-)automated computational methods are applied for annotating live cells of mixed cell types. Here, we show that the live cell pool can be highly enriched with undefined heterogeneous cells, i.e. ‘ungated’ cells, and that current (semi-)automated approaches ignore their modeling resulting in misclassified annotations. Therefore, we introduce ‘CyAnno’, a novel semi-automated approach for deconvoluting the unlabeled cytometry dataset based on a machine learning framework utilizing manually gated training data that allows the integrative modeling of ‘gated’ cell types and the ‘ungated’ cells. By applying this framework on several CyTOF datasets, we demonstrated that including the ‘ungated’ cells can lead to a significant increase in the prediction accuracy of the ‘gated’ cell types. CyAnno can be used to identify even a single cell type, including rare cells, with higher efficacy than current state-of-the-art semi-automated approaches.

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“…1B, C). Cell types of these 15 control atlases were assigned using Seurat (Stuart, et al, 2019) and SCINA (Zhang, et al, 2019), and the known cell type markers genes used in this process were collected from literature and PanglaoDB (Franzen, et al, 2019) (Supplementary Table S4).…”

Section: Introductionmentioning

confidence: 99%

scREAD: A single-cell RNA-Seq database for Alzheimer’s Disease

Jiang

Wang

et al. 2020

Preprint

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SummaryAlzheimer’s disease (AD) is a progressive neurodegenerative disorder of the brain and the most common form of dementia among the elderly. The single-cell RNA-sequencing (scRNA-Seq) and single-nucleus RNA-sequencing (snRNA-Seq) techniques are extremely useful for dissecting the function/dysfunction of highly heterogeneous cells in the brain at the single-cell level, and the corresponding data analyses can significantly improve our understanding of why particular cells are vulnerable in AD. We developed an integrated database named scREAD (single-cell RNA-Seq database for Alzheimer’s Disease), which is the first database dedicated to the management of all the existing scRNA-Seq and snRNA-Seq datasets from human postmortem brain tissue with AD and mouse models with AD pathology. scREAD provides comprehensive analysis results for 55 datasets from eight brain regions, including control atlas construction, cell type prediction, identification of differentially expressed genes, and identification of cell-type-specific regulons.Availability and ImplementationscREAD is a one-stop and user-friendly interface and freely available at https://bmbls.bmi.osumc.edu/scread/. The backend workflow can be downloaded from https://github.com/OSU-BMBL/scread/tree/master/script, to enable more discovery-driven analyses.Contactqin.ma@osumc.edu or hongjun.fu@osumc.eduSupplementary informationSupplementary data are available at Bioinformatics online.

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SCINA: Semi-Supervised Analysis of Single Cells in silico

Cited by 44 publications

References 39 publications

Adult Stem Cell-derived Complete Lung Organoid Models Emulate Lung Disease in COVID-19

Adult Stem Cell-derived Complete Lung Organoid Models Emulate Lung Disease in COVID-19

CyAnno: A semi-automated approach for cell type annotation of mass cytometry datasets

scREAD: A single-cell RNA-Seq database for Alzheimer’s Disease

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