Data from: Reference transcriptomics of porcine peripheral immune cells created through bulk and single-cell RNA sequencing

<p>This dataset contains files reconstructing single-cell data presented in 'Reference transcriptomics of porcine peripheral immune cells created through bulk and single-cell RNA sequencing' by Herrera-Uribe & Wiarda et al. 2021. Samples of peripheral blood mononuclear cells (PBMCs) were collected from seven pigs and processed for single-cell RNA sequencing (scRNA-seq) in order to provide a reference annotation of porcine immune cell transcriptomics at enhanced, single-cell resolution. Analysis of single-cell data allowed identification of 36 cell clusters that were further classified into 13 cell types, including monocytes, dendritic cells, B cells, antibody-secreting cells, numerous populations of T cells, NK cells, and erythrocytes. Files may be used to reconstruct the data as presented in the manuscript, allowing for individual query by other users. Scripts for original data analysis are available at <a href="https://github.com/USDA-FSEPRU/PorcinePBMCs_bulkRNAseq_scRNAseq">https://github.com/USDA-FSEPRU/PorcinePBMCs_bulkRNAseq_scRNAseq</a>. Raw data are available at <a href="https://www.ebi.ac.uk/ena/browser/view/PRJEB43826">https://www.ebi.ac.uk/ena/browser/view/PRJEB43826</a>.</p>
<p>Funding for this dataset was also provided by NRSP8: National Animal Genome Research Program (<a href="https://www.nimss.org/projects/view/mrp/outline/18464">https://www.nimss.org/projects/view/mrp/outline/18464</a>).  </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: Herrera-Uribe & Wiarda et al. PBMCs - All Cells 10X Format.</p> <p>File Name: PBMC7_AllCells.zip</p><p>Resource Description: Zipped folder containing PBMC counts matrix, gene names, and cell IDs. Files are as follows:</p>
<ul>
<li>matrix of gene counts* (matrix.mtx.gx) </li>
<li>gene names (features.tsv.gz) </li>
<li>cell IDs (barcodes.tsv.gz)</li>
</ul>
<p>*The ‘raw’ count matrix is actually gene counts obtained following ambient RNA removal. During ambient RNA removal, we specified to calculate non-integer count estimations, so most gene counts are actually non-integer values in this matrix but should still be treated as raw/unnormalized data that requires further normalization/transformation.</p>
<p>Data can be read into R using the function Read10X().</p></li><br><li><p>Resource Title: Herrera-Uribe & Wiarda et al. PBMCs - All Cells Metadata.</p> <p>File Name: PBMC7_AllCells_meta.csv</p><p>Resource Description: .csv file containing metadata for cells included in the final dataset. Metadata columns include:</p>
<ul>
<li>nCount_RNA = the number of transcripts detected in a cell</li>
<li>nFeature_RNA = the number of genes detected in a cell</li>
<li>Loupe = cell barcodes; correspond to the cell IDs found in the .h5Seurat and 10X formatted objects for all cells</li>
<li>prcntMito = percent mitochondrial reads in a cell</li>
<li>Scrublet = doublet probability score assigned to a cell</li>
<li>seurat_clusters = cluster ID assigned to a cell</li>
<li>PaperIDs = sample ID for a cell</li>
<li>celltypes = cell type ID assigned to a cell</li><br><li><p>Resource Title: Herrera-Uribe & Wiarda et al. PBMCs - All Cells PCA Coordinates.</p> <p>File Name: PBMC7_AllCells_PCAcoord.csv</p><p>Resource Description: .csv file containing first 100 PCA coordinates for cells. </p></li><br><li><p>Resource Title: Herrera-Uribe & Wiarda et al. PBMCs - All Cells t-SNE Coordinates.</p> <p>File Name: PBMC7_AllCells_tSNEcoord.csv</p><p>Resource Description: .csv file containing t-SNE coordinates for all cells.</p></li><br><li><p>Resource Title: Herrera-Uribe & Wiarda et al. PBMCs - All Cells UMAP Coordinates.</p> <p>File Name: PBMC7_AllCells_UMAPcoord.csv</p><p>Resource Description: .csv file containing UMAP coordinates for all cells.</p></li><br><li><p>Resource Title: Herrera-Uribe & Wiarda et al. PBMCs - CD4 T Cells t-SNE Coordinates.</p> <p>File Name: PBMC7_CD4only_tSNEcoord.csv</p><p>Resource Description: .csv file containing t-SNE coordinates for only CD4 T cells (clusters 0, 3, 4, 28). A dataset of only CD4 T cells can be re-created from the PBMC7_AllCells.h5Seurat, and t-SNE coordinates used in publication can be re-assigned using this .csv file.</p></li><br><li><p>Resource Title: Herrera-Uribe & Wiarda et al. PBMCs - CD4 T Cells UMAP Coordinates.</p> <p>File Name: PBMC7_CD4only_UMAPcoord.csv</p><p>Resource Description: .csv file containing UMAP coordinates for only CD4 T cells (clusters 0, 3, 4, 28). A dataset of only CD4 T cells can be re-created from the PBMC7_AllCells.h5Seurat, and UMAP coordinates used in publication can be re-assigned using this .csv file.</p></li><br><li><p>Resource Title: Herrera-Uribe & Wiarda et al. PBMCs - Gamma Delta T Cells UMAP Coordinates.</p> <p>File Name: PBMC7_GDonly_UMAPcoord.csv</p><p>Resource Description: .csv file containing UMAP coordinates for only gamma delta T cells (clusters 6, 21, 24, 31). A dataset of only gamma delta T cells can be re-created from the PBMC7_AllCells.h5Seurat, and UMAP coordinates used in publication can be re-assigned using this .csv file.</p></li><br><li><p>Resource Title: Herrera-Uribe & Wiarda et al. PBMCs - Gamma Delta T Cells t-SNE Coordinates.</p> <p>File Name: PBMC7_GDonly_tSNEcoord.csv</p><p>Resource Description: .csv file containing t-SNE coordinates for only gamma delta T cells (clusters 6, 21, 24, 31). A dataset of only gamma delta T cells can be re-created from the PBMC7_AllCells.h5Seurat, and t-SNE coordinates used in publication can be re-assigned using this .csv file.</p></li><br><li><p>Resource Title: Herrera-Uribe & Wiarda et al. PBMCs - Gene Annotation Information.</p> <p>File Name: UnfilteredGeneInfo.txt</p><p>Resource Description: .txt file containing gene nomenclature information used to assign gene names in the dataset. 'Name' column corresponds to the name assigned to a feature in the dataset.</p></li><br><li><p>Resource Title: Herrera-Uribe & Wiarda et al. PBMCs - All Cells H5Seurat.</p> <p>File Name: PBMC7.tar</p><p>Resource Description: .h5Seurat object of all cells in PBMC dataset. File needs to be untarred, then read into R using function LoadH5Seurat().</p></li></ul><p></p></li>
</ul>

Tags: ars, data-gov, faang, immune-cells, np108, pbmc, pig, porcine, porcine-leukocytes, reference-transcriptomes, rna-seq, rna-sequencing, scrna-seq, single-cell, single-cell-rna-sequencing, transcriptome
Organization: Department of Agriculture