Predicting bacterial infection outcomes using single cell RNA-sequencing analysis of human immune cells
Complex interactions between different host immune cell types can determine the outcome of pathogen infections. Here, Avraham and colleagues present a deconvolution algorithm that uses single-cell RNA and bulk RNA sequencing measurements of pathogen-infected cells to predict disease risk outcomes.
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| Autores principales: | , , , , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Nature Portfolio
2019
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/4934fef129ce45bf965afdfe39cd13bc |
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| Sumario: | Complex interactions between different host immune cell types can determine the outcome of pathogen infections. Here, Avraham and colleagues present a deconvolution algorithm that uses single-cell RNA and bulk RNA sequencing measurements of pathogen-infected cells to predict disease risk outcomes. |
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