Cell annotation using scRNA-seq data: A protein-protein interaction network approach
Pathway analysis is an important step in the interpretation of single cell transcriptomic data, as it provides powerful information to detect which cellular processes are active in each individual cell. We have recently developed a protein-protein interaction network-based framework to quantify plur...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2023 |
| País: | Argentina |
| Institución: | Consejo Nacional de Investigaciones Científicas y Técnicas |
| Repositorio: | CONICET Digital (CONICET) |
| Idioma: | inglés |
| OAI Identifier: | oai:ri.conicet.gov.ar:11336/221884 |
| Acceso en línea: | http://hdl.handle.net/11336/221884 |
| Access Level: | acceso abierto |
| Palabra clave: | BIOLOGICAL PROCESSES BREAST CANCER CELL ANNOTATION PROTEIN-PROTEIN INTERACTION NETWORKS SCRNA-SEQ https://purl.org/becyt/ford/1.6 https://purl.org/becyt/ford/1 https://purl.org/becyt/ford/1.2 |
| Sumario: | Pathway analysis is an important step in the interpretation of single cell transcriptomic data, as it provides powerful information to detect which cellular processes are active in each individual cell. We have recently developed a protein-protein interaction network-based framework to quantify pluripotency associated pathways from scRNA-seq data. On this occasion, we extend this approach to quantify the activity of a pathway associated with any biological process, or even any list of genes. A systems-level characterization of pathway activities across multiple cell types provides a broadly applicable tool for the analysis of pathways in both healthy and disease conditions. Dysregulated cellular functions are a hallmark of a wide spectrum of human disorders, including cancer and autoimmune diseases. Here, we illustrate our method by analyzing various biological processes in healthy and cancer breast samples. Using this approach we found that tumor breast cells, even when they form a single group in the UMAP space, keep diverse biological programs active in a differentiated manner within the cluster. • We implement a protein-protein interaction network-based approach to quantify the activity of different biological processes. • The methodology can be used for cell annotation in scRNA-seq studies and is freely available as R package. |
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