neuroblastoma

Single-cell transcriptomics and epigenomics unravel the role of monocytes in neuroblastoma bone marrow metastasis

https://github.com/csbg/neuroblastoma

Keywords

bone-marrow heterogeneity metastasis microenvironment monocytes neuroblastoma scatac-seq scrna-seq single-cell

Last synced: 6 months ago · JSON representation ·

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Single-cell transcriptomics and epigenomics unravel the role of monocytes in neuroblastoma bone marrow metastasis

Basic Info

Host: GitHub
Owner: csbg
License: mit
Language: Jupyter Notebook
Default Branch: main
Homepage:
Size: 38.1 MB

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Stars: 9
Watchers: 1
Forks: 1
Open Issues: 0
Releases: 3

Topics

bone-marrow heterogeneity metastasis microenvironment monocytes neuroblastoma scatac-seq scrna-seq single-cell

Created about 5 years ago · Last pushed about 2 years ago

Metadata Files

Readme License Citation

Single-cell transcriptomics and epigenomics unravel the role of monocytes in neuroblastoma bone marrow metastasis

This code supplements the publication by Fetahu, Esser-Skala, Dnyansagar et al (2023).

Folders

(Not all of these folders appear in the git repository.)

data_generated: output files generated by the scripts in this repository
data_raw: raw input data
doc: project documentation
literature: relevant publications
metadata: additional required data
misc: miscellaneous scripts
plots: generated plots
renv: R environment data
scatac: scripts for scATAC-seq analysis
tables: exported supplementary tables and data; the subfolder source_data contains source data for figures

Download data

Create a folder data_raw that will contain raw data in the following subfolders:

adrmed:
- adrenal_medulla_Seurat.RDS: reference expression data for adrenal medullary cells; download from https://adrenal.kitz-heidelberg.de/developmentalprogramsNB_viz/ (Download data -> Download Adrenal medulla data -> Seurat object (RDS))
rna_seq: Download GSE216155_RAW.tar from GEO Series GSE216155 and extract all files.
atac_seq: Download all files from GEO Series GSE216175 (GSE216175_barcodes.tsv.gz, GSE216175_barcodes_samples.csv.gz, GSE216175_filtered_peak_bc_matrix.h5, GSE216175_matrix.mtx.gz, GSE216175_peaks.bed.gz, and GSE216175_RAW.tar). Extract all files from the tarball.
GSE137804: download the following files from GEO series GSE137804:
- GSE137804_tumor_dataset_annotation.csv.gz
- GSE137804_RAW.tar, from which the following eleven files must be extracted:
- GSM4088774_T10_gene_cell_exprs_table.xls.gz
- GSM4088776_T27_gene_cell_exprs_table.xls.gz
- GSM4088777_T34_gene_cell_exprs_table.xls.gz
- GSM4088780_T69_gene_cell_exprs_table.xls.gz
- GSM4088781_T71_gene_cell_exprs_table.xls.gz
- GSM4088782_T75_gene_cell_exprs_table.xls.gz
- GSM4088783_T92_gene_cell_exprs_table.xls.gz
- GSM4654669_T162_gene_cell_exprs_table.xls.gz
- GSM4654672_T200_gene_cell_exprs_table.xls.gz
- GSM4654673_T214_gene_cell_exprs_table.xls.gz
- GSM4654674_T230_gene_cell_exprs_table.xls.gz
snp_array: Extract the contents of snp_array.tgz provided in Zenodo repository https://doi.org/10.5281/zenodo.7707614

Optionally, obtain intermediary data: Extract the contents of R_data_generated.tgz from Zenodo repository https://doi.org/10.5281/zenodo.7707614 to folder data_generated.

scRNA-seq analysis

Main workflow

Run these R scripts in the given order to generate all files required by figures and tables.

perform_qc.R – perform QC filtering, ensure unique cell names
integrate_rna.R – integrate scRNA-seq samples with monocle, perform basic analyses and extract resulting metadata
correct_ambiance.R – remove cell-free RNA contamination
classifycelltypes.R – perform cell type classification via SingleR
assemble_metadata.R – generate one CSV and RDS file with all metadata
analyse_dge.R – analyse differential gene expression using mixed models
analyse_cnv.R – analyse copy number variations
analyse_ccc.R – analyse cell-cell communication
analyse_myeloid.R – analyse myeloid subpopulation
preparedatadong.R – prepare dataset by Dong et al for analysis
classifyasadrenal.R – classify tumor cells as adrenal medullary cell types
comparetumorspseudobulk.R – comparison of tumor samples via pseudobulk correlation

Plotting functions

Run these R scripts in arbitrary order to generate publication figures and tables:

plotfigure1_S1.R – includes Tables S2, S4 and Data S1
plotfigure2_S2.R
plotfigure3_S3.R – includes Data S2
plotfigure4S4S7b.R – includes Data S3 and S4
plotfigureS5_S7c.R – includes Data S5 and S6
plotfiguresrevision.R – plots for the reply to reviewers

Other scripts

common_functions.R – functions used throughout the project
plot_dependencies.R – plot the dependency graph
styling.R – functions for generating publication-quality figures and tables

scATAC-seq analysis

All required scripts are in subfolder scatac.

scATAC-seq workflow

R0_scopen.R – quality control, normalization, clustering
R1_annotation.R – annotation of clusters formed and markers used
R2footprintingcelltype.R – cell-type specific footprinting
R3nebularun.R – identify differentially accessible OCRs in patient groups
R4nebularesults.R – analyze nebula results
R5nebulamicroenvironment.R – enrichment in microenvironment cells
R6nebulanbcells.R – enrichment in NB cells
R7createbedrunhomer.R – create cell specific bed files and motif analysis
R8homerresults.R – analyze motif analysis results

scATAC-seq scRNA-seq integration workflow

For data integration we used scGLUE (Graph Linked Unified Embedding) model for unpaired single-cell multi-omics data integration (https://scglue.readthedocs.io/en/latest/). We followed the detailed tutorial at https://scglue.readthedocs.io/en/latest/tutorials.html. Before the tutorial we needed to convert the objects in anndata format from SingleCellExperiment and Seurat for scRNA-seq and scATAC-seq respectively. There are many tools available to do this and we are sharing our approach for format conversion, namely monocletoanndata.R and Seurattoanndata.R.

The following Jupyter notebooks follow the notebooks of the scGLUE integration pipeline.

G1nbgluepreprocessingmyeloid.ipynb – preprocess scRNA-seq and scATAC-seq anndata objects
G2gluemodel_myeloid.ipynb – create glue model
G3regulatoryinference_myeloid.ipynb
G4regulatorynetworkplotsmyeloid.ipynb

Finally, Figures.R generates publication figures.

Owner

Name: Computational Systems Biology Group
Login: csbg
Kind: organization
Location: Salzburg, AT

Website: www.plus.ac.at/fortelny
Repositories: 1
Profile: https://github.com/csbg

The Fortelny Lab at the University of Salzburg

Citation (CITATION.cff)

cff-version: 1.2.0
message: If you use this software, please cite both the article from preferred-citation and the software itself.
title: scRNA-seq and scATAC-seq analysis scripts
authors:
- family-names: Esser-Skala
  given-names: Wolfgang
  orcid: https://orcid.org/0000-0002-7350-4045
- family-names: Dnyansagar
  given-names: Rohit
  orcid: https://orcid.org/0000-0002-2699-0087
- family-names: Sindelar
  given-names: Samuel
- family-names: Lazic
  given-names: Daria
  orcid: https://orcid.org/0000-0002-8793-6885
- family-names: Fortelny
  given-names: Nikolaus
  orcid: https://orcid.org/0000-0003-4025-9968
version: 1.2.0
type: software
license: MIT
doi: 10.5281/zenodo.7867892
repository-code: https://github.com/csbg/neuroblastoma
preferred-citation:
  type: article
  authors:
    - family-names: Fetahu
      given-names: Irfete S.
      affiliation: St. Anna Children's Cancer Research Institute, Vienna, Austria
      orcid: https://orcid.org/0000-0002-0468-7458
      email: irfete.fetahu@ccri.at
    - family-names: Esser-Skala
      given-names: Wolfgang
      affiliation: Department of Biosciences and Medical Biology, University of Salzburg, Salzburg, Austria
      orcid: https://orcid.org/0000-0002-7350-4045
    - family-names: Dnyansagar
      given-names: Rohit
      affiliation: Department of Biosciences and Medical Biology, University of Salzburg, Salzburg, Austria
      orcid: https://orcid.org/0000-0002-2699-0087
    - family-names: Sindelar
      given-names: Samuel
      affiliation: Department of Biosciences and Medical Biology, University of Salzburg, Salzburg, Austria
    - family-names: Rifatbegovic
      given-names: Fikret
      affiliation: St. Anna Children's Cancer Research Institute, Vienna, Austria
      orcid: https://orcid.org/0000-0002-9956-2382
    - family-names: Bileck
      given-names: Andrea
      affiliation: Department of Analytical Chemistry, Faculty of Chemistry, University of Vienna, Vienna, Austria; and Joint Metabolomics Facility, University and Medical University of Vienna, Vienna, Austria
      orcid: https://orcid.org/0000-0002-7053-8856
    - family-names: Skos
      given-names: Lukas
      affiliation: Department of Analytical Chemistry, Faculty of Chemistry, University of Vienna, Vienna, Austria
      orcid: https://orcid.org/0000-0003-3983-1736
    - family-names: Bozsaky
      given-names: Eva
      affiliation: St. Anna Children's Cancer Research Institute, Vienna, Austria
    - family-names: Lazic
      given-names: Daria
      affiliation: St. Anna Children's Cancer Research Institute, Vienna, Austria
      orcid: https://orcid.org/0000-0002-8793-6885
    - family-names: Shaw
      given-names: Lisa
      affiliation: Department of Dermatology, Medical University of Vienna, Vienna, Austria
    - family-names: Tötzl
      given-names: Marcus
      affiliation: St. Anna Children's Cancer Research Institute, Vienna, Austria
    - family-names: Tarlungeanu
      given-names: Dora
      affiliation: St. Anna Children's Cancer Research Institute, Vienna, Austria
    - family-names: Bernkopf
      given-names: Marie
      affiliation: St. Anna Children's Cancer Research Institute, Vienna, Austria
    - family-names: Rados
      given-names: Magdalena
      affiliation: St. Anna Children's Cancer Research Institute, Vienna, Austria
    - family-names: Weninger
      given-names: Wolfgang
      affiliation: Department of Dermatology, Medical University of Vienna, Vienna, Austria
    - family-names: Tomazou
      given-names: Eleni M.
      affiliation: St. Anna Children's Cancer Research Institute, Vienna, Austria
      orcid: https://orcid.org/0000-0002-7497-4567
    - family-names: Bock
      given-names: Christoph
      affiliation: CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria; and Center for Medical Statistics, Informatics and Intelligent Systems, Institute of Artificial Intelligence, Medical University of Vienna, Vienna, Austria
      orcid: https://orcid.org/0000-0001-6091-3088
    - family-names: Gerner
      given-names: Christopher
      affiliation: Department of Analytical Chemistry, Faculty of Chemistry, University of Vienna, Vienna, Austria; and Joint Metabolomics Facility, University and Medical University of Vienna, Vienna, Austria
      orcid: https://orcid.org/0000-0003-4964-0642
    - family-names: Ladenstein
      given-names: Ruth
      affiliation: St. Anna Children's Cancer Research Institute, Vienna, Austria
      orcid: https://orcid.org/0000-0001-6548-3873
    - family-names: Farlik
      given-names: Matthias
      affiliation: Department of Dermatology, Medical University of Vienna, Vienna, Austria
      orcid: https://orcid.org/0000-0003-0698-2992
    - family-names: Fortelny
      given-names: Nikolaus
      affiliation: Department of Biosciences and Medical Biology, University of Salzburg, Salzburg, Austria
      orcid: https://orcid.org/0000-0003-4025-9968
      email: nikolaus.fortelny@plus.ac.at
    - family-names: Taschner-Mandl
      given-names: Sabine
      affiliation: St. Anna Children's Cancer Research Institute, Vienna, Austria
      orcid: https://orcid.org/0000-0002-1439-5301
      email: sabine.taschner@ccri.at
  title: Single-cell transcriptomics and epigenomics unravel the role of monocytes in neuroblastoma bone marrow metastasis
  journal: Nature Communications
  volume: 14
  pages: 3620 
  year: 2023
  doi: 10.1038/s41467-023-39210-0
  license: CC-BY-4.0
  pmcid: PMC10293285

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neuroblastoma

Science Score: 75.0%

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Basic Info

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Metadata Files

README.md

Single-cell transcriptomics and epigenomics unravel the role of monocytes in neuroblastoma bone marrow metastasis

Folders

Download data

scRNA-seq analysis

Main workflow

Plotting functions

Other scripts

scATAC-seq analysis

scATAC-seq workflow

scATAC-seq scRNA-seq integration workflow

Owner

Citation (CITATION.cff)

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