A feasibility study for improving the resolution of malignant and immune populations in precision cancer genomic medicine patients through the integration of bulk and single-cell multiomics

Description of scripts, in the order of the workflow

1. processing_cellRangerDownstream.R: Performs QC and data processing on CellRanger outputs to generate Seurat objects containing both snRNA-seq and snATAC-seq information.
2. processing_snvCNVScoring.R: Analyses related to CNVs and SNVs
   1. For all samples:
      1. Infer CNVs from snRNA-seq
      2. Calculate SNV scores from snATAC-seq using SNVs detected from matched bulk WGS
      3. Compare SNV scores to a randomly generated background. Run compareNormaltoNull.R afterwards to visualize score distributions and calculate effect sizes.
   2. For longitudinal biopsies:
      1. Calculate SNV scores using SNVs detected from bulk WGS of the later biopsy
      2. Compare changes in the proportion of malignant cells with a given CNV
3. multiBiop_batchMixing.R:
4. analyses_targetComparisons.R: Comparisons of expression prevalence for genes associated with therapeutic efficacy between malignant cells and normal cells matched by cell type of origin and biopsy site

Figure generation

figures_code.R uses the following as inputs, which are not uploaded into this repo for patient privacy or file size reasons:

• patient_char_mat_clean: Record of patient data, including cancer type and biopsy site
• allSamples_merged: Seurat object containing all cells from all patients; output of the workflow above
• cnvComp_propCells: Proportion of malignant cells with a given CNV in longitudinal biopsies; output of processing_snvCNVScoring.R
• multiBiopCNV_list: List of Seurat objects containing cells from longitudinal biopsies
• tx_history: History of treatments received by patients

Analyses not shown in the manuscript

• correlationCalculation.R: Correlates VAFs derived from bulk WGS and snATAC-seq across SNVs
• snvCoverage.R: Assesses how SNVs detected in snATAC-seq are distributed across peaks and genomic regions

Session info

R version 4.2.2 (2022-10-31) Platform: x86_64-pc-linux-gnu (64-bit) Running under: CentOS Linux 7 (Core)

locale: [1] C

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

other attached packages: [1] aricode_1.0.3
[2] annotatr_1.24.0
[3] org.Hs.eg.db_3.16.0
[4] paletteer_1.6.0
[5] circlize_0.4.15
[6] ggsankey_0.0.99999
[7] ggalluvial_0.12.5
[8] qs2_0.1.7
[9] httpgd_2.0.4
[10] rstatix_0.7.2
[11] emmeans_1.11.2
[12] glmmTMB_1.1.11
[13] slingshot_2.6.0
[14] TrajectoryUtils_1.6.0
[15] princurve_2.1.6
[16] viridis_0.6.3
[17] viridisLite_0.4.2
[18] CellChat_2.1.2
[19] bigmemory_4.6.4
[20] igraph_2.0.3
[21] SeuratExtend_1.1.0
[22] SeuratExtendData_0.2.1
[23] SeuratWrappers_0.3.2
[24] ggrepel_0.9.3
[25] RColorBrewer_1.1-3
[26] ggsci_3.0.0
[27] ggpubr_0.6.0
[28] harmony_1.2.4
[29] Rcpp_1.0.11
[30] infercnv_1.3.3
[31] scDblFinder_1.12.0
[32] BSgenome.Hsapiens.UCSC.hg38_1.4.5
[33] BSgenome_1.66.3
[34] rtracklayer_1.58.0
[35] Biostrings_2.66.0
[36] XVector_0.38.0
[37] TFBSTools_1.36.0
[38] JASPAR2020_0.99.10
[39] motifmatchr_1.20.0
[40] biomaRt_2.54.1
[41] TxDb.Hsapiens.UCSC.hg38.knownGene_3.16.0 [42] pheatmap_1.0.12
[43] EnsDb.Hsapiens.v86_2.99.0
[44] ensembldb_2.22.0
[45] AnnotationFilter_1.22.0
[46] GenomicFeatures_1.50.4
[47] AnnotationDbi_1.60.2
[48] scran_1.26.2
[49] scRNAseq_2.12.0
[50] scater_1.26.1
[51] scuttle_1.8.4
[52] SingleCellExperiment_1.20.1
[53] SummarizedExperiment_1.28.0
[54] Biobase_2.58.0
[55] GenomicRanges_1.50.2
[56] GenomeInfoDb_1.34.9
[57] IRanges_2.32.0
[58] S4Vectors_0.36.2
[59] BiocGenerics_0.44.0
[60] MatrixGenerics_1.10.0
[61] matrixStats_1.0.0
[62] Signac_1.14.0
[63] Seurat_5.2.1
[64] SeuratObject_5.0.2
[65] sp_2.0-0
[66] Matrix_1.6-4
[67] data.table_1.14.8
[68] lubridate_1.9.2
[69] forcats_1.0.0
[70] stringr_1.5.0
[71] dplyr_1.1.2
[72] purrr_1.0.1
[73] readr_2.1.4
[74] tidyr_1.3.0
[75] tibble_3.2.1
[76] ggplot2_4.0.2
[77] tidyverse_2.0.0

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[3] lattice_0.20-45 vctrs_0.6.2
[5] mgcv_1.8-41 blob_1.2.4
[7] survival_3.4-0 nloptr_2.0.3
[9] spatstat.data_3.0-1 later_1.3.1
[11] DBI_1.1.3 R.utils_2.12.2
[13] rappdirs_0.3.3 uwot_0.1.16
[15] dqrng_0.3.0 zlibbioc_1.44.0
[17] htmlwidgets_1.6.2 mvtnorm_1.2-2
[19] GlobalOptions_0.1.2 future_1.33.0
[21] parallel_4.2.2 irlba_2.3.5.1
[23] KernSmooth_2.23-20 promises_1.2.0.1
[25] DelayedArray_0.24.0 limma_3.54.2
[27] RcppParallel_5.1.7 RSpectra_0.16-1
[29] fastmatch_1.1-3 digest_0.6.33
[31] png_0.1-8 rjags_4-14
[33] bluster_1.8.0 sctransform_0.4.1
[35] cowplot_1.2.0.9000 pkgconfig_2.0.3
[37] GO.db_3.16.0 gridBase_0.4-7
[39] spatstat.random_3.1-4 DelayedMatrixStats_1.20.0
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[129] remotes_2.4.2 unigd_0.1.3
[131] argparse_2.2.2 spatstat.utils_3.1-3
[133] htmltools_0.5.5 BiocFileCache_2.6.1
[135] yaml_2.3.7 NMF_0.26
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[139] interactiveDisplayBase_1.36.0 XML_3.99-0.13
[141] withr_2.5.0 fitdistrplus_1.1-11
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[149] ProtGenerics_1.30.0 progressr_0.18.0
[151] rsvd_1.0.5 ScaledMatrix_1.6.0
[153] memoise_2.0.1 tzdb_0.4.0
[155] curl_5.0.1 fansi_1.0.4
[157] fastDummies_1.7.3 tensor_1.5
[159] edgeR_3.40.2 regioneR_1.30.0
[161] cachem_1.0.8 deldir_1.0-9
[163] metapod_1.6.0 rjson_0.2.21
[165] clue_0.3-64 tools_4.2.2
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[175] httr_1.4.6 boot_1.3-28
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