DOI: 10.5281/zenodo.7337398
title: Single-cell Atlas Reveals Diagnostic Features Predicting Progressive Drug Resistance in Chronic Myeloid Leukemia

description: 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 <strong>Revision</strong> 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.