Single-cell Atlas Reveals Diagnostic Features Predicting Progressive Drug Resistance in Chronic Myeloid Leukemia

This archive contains data of scRNAseq and CyTOF in form of Seurat objects, txt and csv files as well as R scripts for data analysis and Figure generation.

A summary of the content is provided in the following.

R scripts

Script to run Machine learning models predicting group specific marker genes: CML_Find_Markers_Zenodo.R
Script to reproduce the majority of Main and Supplementary Figures shown in the manuscript: CML_Paper_Figures_Zenodo.R
Script to run inferCNV analysis: inferCNV_Zenodo.R                                                                                                                              Script to plot NATMI analysis results:NATMI_CvsA_FC0.32_Updown_Column_plot_Zenodo.R                                                              Script to conduct sub-clustering and filtering of NK cells NK_Marker_Detection_Zenodo.R

Helper scripts for plotting and DEG calculation:ComputePairWiseDE_v2.R, Seurat_DE_Heatmap_RCA_Style.R

RDS files

General scRNA-seq Seurat objects:

scRNA-seq seurat object after QC, and cell type annotation used for most analysis in the manuscript: DUKE_DataSet_Doublets_Removed_Relabeled.RDS

scRNA-seq including findings e.g. from NK analysis used in the shiny app: DUKE_final_for_Shiny_App.rds

Neighborhood enrichment score computed for group A across all HSPCs: Enrichment_score_global_groupA.RDS 

UMAP coordinates used in the article: Layout_2D_nNeighbours_25_Metric_cosine_TCU_removed.RDS

SCENIC files:

Regulon set used in SCENIC: 2.6_regulons_asGeneSet.Rds

AUC values computed for regulons: 3.4_regulonAUC.Rds

MetaData used in SCENIC cellInfo.Rds

Group specific regulons for LCS: groupSpecificRegulonsBCRAblP.RDS

Patient specific regulons for LSC: patientSpecificRegulonsBCRAblP.RDS

Patient specificity score for LSC: PatientSpecificRegulonSpecificityScoreBCRAblP.RDS

Regulon specificty score for LSC: RegulonSpecificityScoreBCRAblP.RDS

BCR-ABL1 inference:

HSC with inferred BCR-ABL1 label: HSCs_CML_with_BCR-Abl_label.RDS

UMAP for HSC with inferred BCR-ABL1 label: HSCs_CML_with_BCR-Abl_label_UMAP.RDS

HSPCs with BCR-ABL1 module scores: HSPC_metacluster_74K_with_modscore_27thmay.RDS

NK sub-clustering and filtering:

NK object with module scores: NK_8617cells_with_modscore_1stjune.RDS

Feature genes for NK cells computed with DubStepR: NK_Cells_DubStepR

NK cells Seurat object excluding contaminating T and B cells: NK_cells_T_B_17_removed.RDS

NK Seurat object including neighbourhood enrichment score calculations: NK_seurat_object_with_enrichment_labels_V2.RDS

txt and csv  files:

Proportions per cluster calculated from CyTOF: CyTOF_Proportions.txt

Correlation between scRNAseq and CyTOF cell type abundance: scRNAseq_Cor_Cytof.txt

Correlation between manual gating and FlowSOM clustering: Manual_vs_FlowSOM.txt

GSEA results:

HSPC, HSC and LSC results: FINAL_GSEA_DATA_For_GGPLOT.txt

NK: NK_For_Plotting.txt

TFRC and HLA expression: TFRC_and_HLA_Values.txt

NATMI result files:

UP-regulated_mean.csv

DOWN-regulated_mean.csv

Gene position file used in inferCNV: inferCNV_gene_positions_hg38.txt

Module scores for NK subclusters per cell: NK_Supplementary_Module_Scores.csv

Compressed folders:

All CyTOF raw data files: CyTOF_Data_raw.zip

Results of the patient-based classifier: PatientwiseClassifier.zip

Results of the single-cell based classifier: SingleCellClassifierResults.zip

For general new data analysis approaches, we recommend the readers to use the Seruat object stored in DUKE_final_for_Shiny_App.rds or to use the shiny app(http://scdbm.ddnetbio.com/) and perform further analysis from there.

RAW data is available at EGA upon request using Study ID: EGAS00001005509

Revision

The for_CML_manuscript_revision.tar.gz folder contains scripts and data for the paper revision including 1) Detection of the BCR-ABL fusion with long read sequencing; 2) Identification of BCR-ABL junction reads with scRNAseq; 3) Detection of expressed mutations using scRNAseq.