Single-cell landscape of innate and acquired drug resistance in acute myeloid leukemia: scRNA-seq and CyTOF processed datasets

This data was generated as part of the Tumor Profiler study. If you use it in your research, please cite:

Wegmann, R., Bonilla, X., Casanova, R. et al. Single-cell landscape of innate and acquired drug resistance in acute myeloid leukemia. Nat Commun 15, 9402 (2024). https://doi.org/10.1038/s41467-024-53535-4

Derived data - scRNA-seq

This is an R data set (.RDS) containing a SingleCellExperiment object with the following slots:

Assays:

counts: raw counts

colData: Cell-level metadata

 barcodes: The cell barcode

 fractionMT: Fraction mitochondrial genes per cell

 n_umi: Total number of UMIs per cell

 n_gene: Total number of genes per cell

 log_umi: log10 total number of UMIs per cell

 g2m_score: Cell cycle phase score for G2M

s_score: Cell cycle phase score for S

cycle_phase: predicted cell cycle phase

celltype_major_full_ct_name: Major cell type full name

celltype_major: Major cell type short name

celltype_final_full_ct_name: Cell subtype full name

celltype_final: Cell subtype short name             

sample_id  

rowData: Gene-level metadata

gene_ids

gene_names

Derived data - CyTOF

This is an R data set (.RDS) containing a SingleCellExperiment object with the following slots:

Assays:

counts_raw: signal intensity based on CyTOF dual counts

exprs_raw: arcsinh transformed raw counts (cofactor 5)

counts: batch corrected raw counts (linear scaling based on a quantile)

exprs: arcsin transformed counts (cofactor 5)

scaled: 0-1 normalized exprs (clipped to the 99.95th percentile)

colData (cell metadata)

bc_id: barcode of the sample during staining  

run: CyTOF experiment batch, named after the first sample of the batch

type: Sample type (blood or bone marrow)

sample_id: TuPro sample ID

pred_id: Predicted cell type [char]

pred_n: Predicted cell type [integer]

rowData (marker metadata)

channel_name: Name and isotopic mass of the metal ion corresponding to this marker

marker_name: Protein name

channel_group, channel_group_integer: Biological processes the channel identifies, e.g. specific cell type, signalling, cell death

tsne_channel: Logical - use this channel for dimensionality reduction?

channel_order: Define the order of channels for plotting

cluster_channel: Logical - use this channel for clustering?