CML manuscript

There is a newer version of the record available.

Published August 6, 2021 | Version 1
Dataset Restricted

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

Creators

  • 1. Duke-NUS Medical School
  • 2. Genome Institute of Singapore
  • 3. Singapore General Hospital
  • 4. National Cancer Centre, Advanced Cell Therapy and Research Institute
  • 5. SingHealth Duke-NUS Academic Medical Centre
  • 6. Singapore General Hospital, National Cancer Centre, Advanced Cell Therapy and Research Institute
  • 7. SingHealth Duke-NUS Academic Medical Centre, KK Women's and Children's Hospital
  • 8. Duke-NUS Medical School, Singapore General Hospital

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.

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

Files

Restricted

The record is publicly accessible, but files are restricted to users with access.

Request access

If you would like to request access to these files, please fill out the form below.

You need to satisfy these conditions in order for this request to be accepted:

The primary purpose of the Genome Institute of Singapore (GIS) granting access to the Data to the Recipient and its Registered Users is to facilitate academic research conducted by the Recipient and to provide a means for the Recipient to validate information relevant to its academic, non-commercial research. GIS anticipates that the Data will be used by others. Persons who use the Data must acknowledge GIS using the following wording “This study makes use of data generated by the Genome Institute of Singapore” and cite the relevant primary GIS publication from which the Data was taken. All users of the Data must take note that GIS bears no responsibility for the further analysis or interpretation of the Data.

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