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. <strong>R scripts</strong> Script to run Machine learning models predicting group specific marker genes: CML_Find_Markers_Zenodo.R<br> Script to reproduce the majority of Main and Supplementary Figures shown in the manuscript: CML_Paper_Figures_Zenodo.R<br> 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 <strong>RDS files</strong> <em>General scRNA-seq Seurat objects:</em> 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 <em>SCENIC files:</em> 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 <em>BCR-ABL1 inference:</em> 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 <em>NK sub-clustering and filtering:</em> 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 <em>txt and csv files:</em> 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