ImmunogenomicLandscape-BloodCancers

Description:

Scripts related to results presented in Dufva and Pölönen et al. Immunogenomic landscape of hematological malignancies.

Note:

If you use the data, analysis, results, Please cite:

• Dufva Pölönen et al. https://doi.org/10.1016/j.ccell.2020.06.002

• Data DOI: 10.7303/syn21991014

If you use Hemap data, cite also:

• Hemap: An interactive online resource for characterizing molecular phenotypes across hematologic malignancies Petri Pölönen, Juha Mehtonen, Jake Lin, Thomas Liuksiala, Sergei Häyrynen, Susanna Teppo, Artturi Mäkinen, Ashwini Kumar, Disha Malani, Virva Pohjolainen, Kimmo Porkka, Caroline A. Heckman, Patrick May, Ville Hautamäki, Kirsi J. Granberg, Olli Lohi, Matti Nykter and Merja Heinäniemi Cancer Res April 2 2019 DOI: 10.1158/0008-5472.CAN-18-2970

If you use other publicly available data sets that were analyzed here (TCGA AML/DLBCL, Chapuy et al., Tyner et al., etc), please also cite the original research.

If you are only interested in the data sets analyzed here, check datasets_synapseID.txt for synapse accession codes.

To reproduce parts of the results:

• Get synapse credentials https://www.synapse.org

• Access synapse project syn21991014

• Download project data:

  • Input files individually (see scripts for filenames and download from https://www.synapse.org/#!Synapse:syn21991014/files/) (Recommended)
  • Programmatic access (synapse, check synapseID_Filename.txt for accession codes):

     pip install synapseclient
     synapse get synapseID
    

  • Synapse bulk (70 gb):

     pip install synapseclient
     synapse get syn21991014 -r
    

• clone the git project: git clone https://github.com/systemsgenomics/ImmunogenomicLandscape-BloodCancers.git

• Install the required R packages

• Run the analysis:

  • set working directory to the folder with the data. setwd("path/data")
  • modify GIT_HOME variable in R script (folder where the git folder is cloned). GIT_HOME typically points to common_scripts that contains various statistical and visualisation tools that were used in the analysis.

Scripts to reproduce the analysis, plots and tables

The processed and intermediate files for these scripts can be downloaded to reproduce the analysis. Inputs for these scripts have been generated using the scripts under preprocessing.

Figure 1, Figure S1:

Fig1C_AML_cytscore_flow_RNAseq_comparison.R (Fig1C)
Fig1_plots.R (Figure 1F-H)
Statistical_analysis_Cytolytic_Score_development.R (FigureS1B, G-I)

Figure 2, Figure S2 and Table S2:

Fig2_microenvironment_analysis.R (TableS2, Fig2A-B, FigS2C)
FigS2AB_microenvironment_analysis_GSEA_GSVA.R (FigS2A-B)
FigS2DE_microenvironment_validation_CLL_AML.R (FigS2D-E)

Figure 3, Figure S3 and Table S3:

Fig3A_DLBCL_cytscore_oncoprint.R (Fig3A)
Fig3BC_DLBCL_cytscore_boxplots.R (Fig3B-C)
Fig3D_TCGA_AML_cytscore_oncoprint.R (Fig3D)
Fig3_FigS3_MDSsignature.R (Fig3E, FigS3D)
Fig3_plots_scRNA.R  (Fig3F-H and K, FigS3K and O)
Statistical_analysis_DE_analysis_MDSlike.R (TableS3 tab)
Statistical_analysis_scRNA_MDSlike_analysis.R  (TableS3 tabs)
Statistical_analysis_Szabo_TCell_analysis.R (Fig3I, FigS3M)
Statistical_analysis_Yang_NKCell_analysis.R (Fig3J, FigS3N)
FigS3L.R (TableS3 tab, FigS3L)

Figure 4, Figure S4 and Table S4:

Statistical_analysis_HLAII_Score_development.R (FigS4A-B)
Fig4D_TCGA_AML_complexheatmap_CIITA.R (Fig4D)
Statistical_analysis_FIMM_AML_RRBS.R (Fig4G and H, FigS4L)
FigS4C_AML_HLAIIscore_flow_RNAseq_comparison.R (FigS4C)
FigS4D_TCGA_AML_global_hypermethylation.R (FigS4D)
FigS4EF_CIITA_methylation_validation_ERRBS.R (FigS4E-F)
FigS4K_CCLE_CIITA_methylation.R (FigS4K)
FigS4J_CIITA_methylation_validation_GSE49031.R (FigS4J)

Figure 5, Figure S5 and Table S5:

Fig5A_ligands_heatmap.R (Fig5A)
Fig5_DE_analysis_costim.R  (Fig5B, FigS5B, Table S5 tabs)
Fig5C_TCGA_AML_ligand_correlation_volcanoplot.R (Fig5C)
Fig5D_S5G_DLBCL_GSE98588_ligand_boxplots.R (Fig5D, FigS5G)
Fig5E_PDL1_IHC_boxplot.R (Fig5E)
Fig5F_VISTA_IHC_boxplot.R (Fig5F)
Fig5G_CD70_CRISPR_T cell_stimulation.R (Fig5G)
FigS5C.R (FigS5C)
FigS5DE_TCGA_ligand_methylation_AML_DLBCL_comparison.R (FigS5D-E)

Figure 6, Figure S6 and Table S6:

Fig6B_S6F_CGA_tSNEplot_hemap.R (Fig6B, FigS6F)
Fig6C_S6B_CGA_Hemap.R (Fig6C, FigS6B)
Fig6D_CCLE_CGA_heatmap.R (Fig6D)
Fig6F_CoMMpass_CGA_boxplot.R (Fig6F)
Fig6G_CoMMpass_CGA_oncoprint.R (Fig6G)
Fig6H_DLBCL_GSE98588_CGA_oncoprint.R (Fig6H)
Fig6H_FigS6H_CGA_heatmap_GSE98588.R (Fig6H, FigS6H, FigS6I)
FigS6G_CGA_GSEA_hemapMM.R (FigS6G)
FigS6A_GTEx_CGA_heatmap.R (FigS6A)
FigS6C_Hemap_CGA_dotplots.R (FigS6C)
FigS6D_TCGA_antigen_methylation.R (FigS6D)
FigS6E_CoMMpass_CGA_heatmap.R (FigS6E)
Statistical_analysis_CGA_discovery_Hemap.R

Figure 7, Figure S7 and Table S7:

Fig7_Univariate_Coxph_survival.R (TableS7)
Fig7_univariate_survival_forestplot.R (Fig7A, Fig7B-D, FigS7A-B)
Fig7_multivariable_regression_eNet_survival.R (Fig7E-F, FigS7C-G)

Statistical association analysis, TableS1-6

Statistical_analysis_correlations_BeatAML.R (TableS3-5)
Statistical_analysis_correlations_CoMMpass.R (TableS4-6)
Statistical_analysis_correlations_GSE98588_DLBCL.R (TableS3-6)
Statistical_analysis_correlations_Reddy.R (TableS3-5)
Statistical_analysis_correlations_TCGA_AML.R (TableS3-5)
Statistical_analysis_correlations_TCGA_DLBCL.R

Scripts related to data preprocessing (under folder preprocessing)

These scripts are for reference only. Raw/processed/input data would have to be downloaded and processed for these scripts. Check the publication for data accession codes.

Data Preprocessing

Preprocessing_CIBERSORT_MCPcounter.R
Preprocessing_normalize_hguarray_GSE98588.R

Generating featurematrix for each cohort

Preprocessing_Hemap_featurematrix_generation.R (TableS1)
Preprocessing_REDDY_DLBCL_featurematrix_generation.R
Preprocessing_TCGA_AML_featurematrix_generation.R
Preprocessing_TCGA_DLBCL_featurematrix_generation.R
Preprocessing_coMMpass_featurematrix_generation.R
Preprocessing_GSE98588_DLBCL_featurematrix_generation.R
Preprocessing_PanALL.R

Subtype analysis for each cohort

Preprocessing_MM_subtyping.R
Preprocessing_DLBCL_subtyping.R
Preprocessing_ALL_subtyping.R
Preprocessing_AML_subtyping.R

Methylation data processing

Preprocessing_TCGA_AML_add_meth_probes.R
Preprocessing_TCGA_meth_data_genelist.R
Preprocessing_FIMM_AML_RRBS.R
Preprocessing_AML_RRBS_meth_de_analysis.R

scRNA data preprocessing for statistical analysis

Preprocessing_scRNA_CLL_GSE111014.R
Preprocessing_scRNA_FIMM_AML.R
Preprocessing_scRNA_Galen_AML.R
Preprocessing_scRNA_HCA.R
Preprocessing_scRNA_PB_Citeseq.R
Preprocessing_scRNA_Szabo_Tcells_dataprocessing.R
Preprocessing_scRNA_Yang_NK_dataprocessing.R
Preprocessing_scRNA_integrate_Tcells_NKcells.R

SessionInfo (R)

R version 3.6.0 (2019-04-26)
Platform: x86_64-redhat-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)

Matrix products: default
BLAS/LAPACK: /usr/lib64/R/lib/libRblas.so

locale:
 [1] LC_CTYPE=C                 LC_NUMERIC=C
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C
 [9] LC_ADDRESS=C               LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C

attached base packages:
 [1] grid      parallel  stats4    stats     graphics  grDevices utils
 [8] datasets  methods   base

other attached packages:
 [1] tidyr_1.0.0
 [2] dplyr_0.8.3
 [3] cowplot_1.0.0
 [4] tibble_2.1.3
 [5] stringr_1.4.0
 [6] survminer_0.4.6
 [7] survcomp_1.34.0
 [8] prodlim_2018.04.18
 [9] Seurat_3.1.1.9021
[10] RnBeads_2.2.0
[11] plyr_1.8.4
[12] methylumi_2.30.0
[13] minfi_1.30.0
[14] bumphunter_1.26.0
[15] locfit_1.5-9.1
[16] iterators_1.0.12
[17] foreach_1.4.7
[18] Biostrings_2.52.0
[19] XVector_0.24.0
[20] SummarizedExperiment_1.14.1
[21] DelayedArray_0.10.0
[22] BiocParallel_1.18.1
[23] FDb.InfiniumMethylation.hg19_2.2.0
[24] org.Hs.eg.db_3.8.2
[25] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
[26] GenomicFeatures_1.36.4
[27] reshape2_1.4.3
[28] scales_1.0.0
[29] illuminaio_0.26.0
[30] matrixStats_0.54.0
[31] gridExtra_2.3
[32] gplots_3.0.1.1
[33] fields_10.0
[34] maps_3.3.0
[35] spam_2.4-0
[36] dotCall64_1.0-0
[37] ff_2.2-14
[38] bit_1.1-14
[39] cluster_2.1.0
[40] MASS_7.3-51.4
[41] GenomicRanges_1.36.0
[42] GenomeInfoDb_1.20.0
[43] RColorBrewer_1.1-2
[44] openxlsx_4.1.4
[45] multipanelfigure_2.0.2
[46] mclust_5.4.5
[47] Matrix_1.2-17
[48] Hmisc_4.2-0
[49] Formula_1.2-3
[50] survival_2.44-1.1
[51] lattice_0.20-38
[52] GSVA_1.32.0
[53] ggridges_0.5.1
[54] ggrastr_0.1.7
[55] ggpubr_0.2.3
[56] future_1.14.0
[57] forestplot_1.9
[58] checkmate_1.9.4
[59] magrittr_1.5
[60] EnhancedVolcano_1.3.5
[61] ggrepel_0.8.1
[62] ggplot2_3.2.1
[63] edgeR_3.26.8
[64] limma_3.40.6
[65] data.table_1.12.4
[66] ComplexHeatmap_2.0.0
[67] circlize_0.4.7
[68] caTools_1.17.1.2
[69] AnnotationDbi_1.46.1
[70] IRanges_2.18.2
[71] S4Vectors_0.22.0
[72] Biobase_2.44.0
[73] BiocGenerics_0.30.0
[74] methylSig_0.1
[75] CePa_0.7.0
[76] viridis_0.5.1