This dataset contains the processed query dataset from the HeOrganAtlas dataset for Marrow tissue. It has been preprocessed to include log-normalized counts, specific metadata columns, annotations based on SingleR cell type scoring, and PCA, t-SNE, and UMAP results.
query_dataAn object of class SingleCellExperiment with 392 rows and 503 columns.
The HeOrganAtlas dataset, available through the scRNAseq package.
This dataset underwent the following steps:
Loads the HeOrganAtlas dataset specifically for Marrow tissue from the scRNAseq package.
Divides the loaded dataset into a query dataset used for downstream analysis.
Performs log normalization on the query dataset using the function logNormCounts from the scuttle package.
Selects specific columns (percent_mito, expert_annotation) from the cell metadata for downstream analysis.
Selects highly variable genes (HVGs) using the function getTopHVGs from the scran package on the query dataset.
Computes AUC gene set scores using the function AUCell_calcAUC from the AUCell package based on a CD4 T cell
signature containing 12 known CD4 T cell marker genes (IL7R, CCR7, SELL, LEF1, TCF7, LTB, KLF2, IL32, CD2, CD3D, CD3E, CD3G)
and adds these scores to the query dataset as gene_set_scores.
Intersects the highly variable genes between the query and reference datasets to obtain common genes for analysis.
Performs Principal Component Analysis (PCA) on the query dataset using the function runPCA from the scater
package.
Performs t-Distributed Stochastic Neighbor Embedding (t-SNE) on the query dataset using the function runTSNE from the
scater package.
Performs Uniform Manifold Approximation and Projection (UMAP) on the query dataset using the function runUMAP from the
scater package.
Adds SingleR annotations (SingleR_annotation) and annotation scores (annotation_scores) to the query dataset
using the function SingleR from the SingleR package.
He, et al. (2020). HeOrganAtlas: a comprehensive human organ atlas based on single-cell RNA sequencing.
Use data("query_data") to load and access the resulting query dataset and the
data("reference_data") for comparison with the reference dataset.
# Load and explore the query dataset
data("query_data")