This dataset contains transcriptome profiles of malignant cells generated via single-cell RNA sequencing. The cells were collected from primary tumors in 10 HNSCC patients and matching lymph node (LN) metastases from 5 of these patients. Puram et al (2017) found that each of these 10 patients clearly mapped to a molecular subtype of HNSCC, whose signatures were previously defined by analysis of bulk expression data of 279 TCGA HNSCC tumors.
The data are normalized, log-transformed counts for 17,113 genes in 2,176 cells: \(y_{ij} = \log_2(1 + TPM_{ij}/10)\), where \(TPM_{ij}\) is the transcript-per-million (TPM) value for gene \(j\) in cell \(i\). The counts are stored as a 2,176 x 17,113 sparse matrix.
These data are used in the vignette to illustrate how gbcd can be used to analyze single-cell RNA-seq data derived from multiple tumor samples.
hnscc is a list with the following elements:
2,176 x 17,113 sparse matrix of normalized, log-transformed counts, with rows corresponding to cells and columns corresponding to genes.
Data frame containing information about the cells, including patient identity, primary/metastatic status and molecular subtype of the tumor sample.
Color used in plots to indicate the different tumor samples.
Color used in plots to indicate the different molecular subtypes.
S.V. Puram et al (2017). Single-cell transcriptomic analysis of primary and metastatic tumor ecosystems in head and neck cancer Cell 171, 1611-1624. doi:10.1016/j.cell.2017.10.044