JonathanShor/DoubletDetection: doubletdetection v3.0

Gayoso, Adam; Shor, Jonathan

doi:10.5281/zenodo.4359992

Cited by 3 publications

(1 citation statement)

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“…Heterotypic doublets detected by demuxlet were removed, and additional homotypic doublets (i.e. two cells from the same individual co-encapsulated) were removed using doubletdetector ( 23 ).…”

Section: Methodsmentioning

confidence: 99%

Multi-Modal Single-Cell Sequencing Identifies Cellular Immunophenotypes Associated With Juvenile Dermatomyositis Disease Activity

et al. 2022

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Juvenile dermatomyositis (JDM) is a rare autoimmune condition with insufficient biomarkers and treatments, in part, due to incomplete knowledge of the cell types mediating disease. We investigated immunophenotypes and cell-specific genes associated with disease activity using multiplexed RNA and protein single-cell sequencing applied to PBMCs from 4 treatment-naïve JDM (TN-JDM) subjects at baseline, 2, 4, and 6 months post-treatment and 4 subjects with inactive disease on treatment. Analysis of 55,564 cells revealed separate clustering of TN-JDM cells within monocyte, NK, CD8+ effector T and naïve B populations. The proportion of CD16+ monocytes was reduced in TN-JDM, and naïve B cells and CD4+ Tregs were expanded. Cell-type differential gene expression analysis and hierarchical clustering identified a pan-cell-type IFN gene signature over-expressed in TN-JDM in all cell types and correlated with disease activity most strongly in cytotoxic cell types. TN-JDM CD16+ monocytes expressed the highest IFN gene score and were highly skewed toward an inflammatory and antigen-presenting phenotype at both the transcriptomic and proteomic levels. A transitional B cell population with a distinct transcriptomic signature was expanded in TN-JDM and characterized by higher CD24 and CD5 proteins and less CD39, an immunoregulatory protein. This data provides new insights into JDM immune dysregulation at cellular resolution and serves as a novel resource for myositis investigators.

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Section: Methodsmentioning

confidence: 99%

Multi-Modal Single-Cell Sequencing Identifies Cellular Immunophenotypes Associated With Juvenile Dermatomyositis Disease Activity

et al. 2022

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Building the mega single-cell transcriptome ocular meta-atlas

et al. 2021

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Background: The development of highly scalable single-cell transcriptome technology has resulted in the creation of thousands of datasets, >30 in the retina alone. Analyzing the transcriptomes between different projects is highly desirable because this would allow for better assessment of which biological effects are consistent across independent studies. However it is difficult to compare and contrast data across different projects because there are substantial batch effects from computational processing, single-cell technology utilized, and the natural biological variation. While many single-cell transcriptome-specific batch correction methods purport to remove the technical noise, it is difficult to ascertain which method functions best. Results: We developed a lightweight R package (scPOP, single-cell Pick Optimal Parameters) that brings in batch integration methods and uses a simple heuristic to balance batch merging and cell type/cluster purity. We use this package along with a Snakefile-based workflow system to demonstrate how to optimally merge 766,615 cells from 33 retina datsets and 3 species to create a massive ocular single-cell transcriptome meta-atlas. Conclusions: This provides a model for how to efficiently create meta-atlases for tissues and cells of interest.

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Building the Mega Single Cell Transcriptome Ocular Meta-Atlas

Swamy

Fufa

Hufnagel

et al. 2021

Preprint

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The development of highly scalable single cell transcriptome technology has resulted in the creation of thousands of datasets, over 30 in the retina alone. Analyzing the transcriptomes between different projects is highly desirable as this would allow for better assessment of which biological effects are consistent across independent studies. However it is difficult to compare and contrast data across different projects as there are substantial batch effects from computational processing, single cell technology utilized, and the natural biological variation. While many single cell transcriptome specific batch correction methods purport to remove the technical noise it is difficult to ascertain which method functions works best. We developed a lightweight R package (scPOP) that brings in batch integration methods and uses a simple heuristic to balance batch merging and celltype/cluster purity. We use this package along with a Snakefile based workflow system to demonstrate how to optimally merge 766,615 cells from 34 retina datsets and three species to create a massive ocular single cell transcriptome meta-atlas. This provides a model how to efficiently create meta-atlases for tissues and cells of interest.

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JonathanShor/DoubletDetection: doubletdetection v3.0

Cited by 3 publications

References 0 publications

Multi-Modal Single-Cell Sequencing Identifies Cellular Immunophenotypes Associated With Juvenile Dermatomyositis Disease Activity

Multi-Modal Single-Cell Sequencing Identifies Cellular Immunophenotypes Associated With Juvenile Dermatomyositis Disease Activity

Building the mega single-cell transcriptome ocular meta-atlas

Building the Mega Single Cell Transcriptome Ocular Meta-Atlas

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