pbmc single cell RNA-seq matrix processed
| Main Authors: | Buchet, Samuel, Carbone, Francesco, Magnin, Morgan, Ménager, Mickaël, Roux, Olivier |
|---|---|
| Format: | info dataset Journal |
| Bahasa: | eng |
| Terbitan: |
, 2021
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| Subjects: | |
| Online Access: |
https://zenodo.org/record/4728943 |
Daftar Isi:
- Single cell RNA-sequencing dataset of peripheral blood mononuclear cells (pbmc: T, B, NK and monocytes) extracted from two healthy donors. Cells labeled as C26 come from a 30 years old female and cells labeled as C27 come from a 53 years old male. Cells have been isolated from blood using ficoll. Samples were sequenced using standard 3' v3 chemistry protocols by 10x genomics. Cellranger v4.0.0 was used for the processing, and reads were aligned to the ensembl GRCg38 human genome (GRCg38_r98-ensembl_Sept2019). QC metrics were calculated on the count matrix generated by cellranger (filtered_feature_bc_matrix). Cells with less than 3 genes per cells, less than 500 reads per cell and more than 20% of mithocondrial genes were discarded. The processing steps was performed with the R package Seurat (https://satijalab.org/seurat/), including sample integration, data normalisation and scaling, dimensional reduction, and clustering. SCTransform method was adopted for the normalisation and scaling steps. The clustered cells were manually annotated using known cell type markers. Files content: - normalized_dataset.csv: normalized gene counts (single cell matrix) - cell_types.csv: cell types identified from annotated cell clusters - cell_types_macro.csv: cell macro types - UMAP_coordinates.csv: 2d cell coordinates computed with UMAP algorithm in Seurat