HCC_SPH

This is the code for HCC spatially punctuated heterogeneity distribution.

Directory structure

The code for HCC spatially punctuated heterogeneity distribution (HCC_SPH) is organized into different directories and scripts.

The directory structure is as follows:

• Data :  This directory contains example data that is required for regenerating plots.
• DNA : This directory contains the analysis scripts that are based on DNA-related data.
• RNA : This directory contains the analysis scripts that are based on RNA-related data.
• Figure :  This directory contains the scripts that are related to reproducing the main figures.

Description of Scripts and How to Use

DNA folder

1. DNA/bwa.sh & DNA/mutect2.sh

   • Description: These scripts are pipelines for calling mutations. bwa.sh takes fastq files as input and generates bam output, which can be used as input for mutect2.sh. mutect2.sh is the script for calling somatic mutations and it takes the output from bwa.sh to generate somatic vcf files.
   • How to use: First, change the input path and output path within the script files. Then, run sh bwa.sh $sample and sh bwa.sh $name $normal $tumor. Please refer to the documentation on the official website of mutect2 for more information.

2. DNA/oncogtator.sh

   • Description: This script is used for annotating vcf files. It takes vcf files as input and generates maf files.
   • How to use: Change the input path of vcf files, and run sh oncotator.sh. See the GitHub page about oncotator for more details.

3. DNA/sequenza.sh and DNA/sequenza.R

   • Description: These scripts are used for calling somatic copy number variants. They take bam files as input, which can be generated by bwa.sh.
   • How to use: First, change the input path and output path within the script files. Then, run sh sequenza.sh $sample. See the tutorial about sequenza for more information.

4. DNA/gistic2.sh

   • Description: This script is used for calling significantly somatic copy number variants. It takes segment files from sequenza as input.
   • How to use: Merge the segment files from sequenza and provide them as input. See the description on the website about GISTIC2 for more details.

5. DNA/signature.py

   • Description: This script is used for analyzing mutational signatures using SigProfiler. It takes somatic mutational vcf files as input.
   • How to use: Change the parameters of the input path to your own path. See the GitHub page of SigProfilerExtractor for more information.

6. DNA/pyclone.sh

   • Description: These scripts are used for running the PyClone pipeline. They take SNVs and CNVs information as input. These scripts require affiliation files in the DNA/script folder.
   • How to use: Provide the correct path to the related files and run sh DNA/pyclone.sh. Get more information from the pyclone GitHub page.

7. DNA/citup.sh

   • Description: These scripts are used for running the citup pipeline. They take PyClone outputs as inputs. These scripts require affiliation files in the DNA/script folder.
   • How to use: Provide the correct path to the related files and run sh DNA/citup.sh. Get more information from the citup GitHub page.

RNA folder

1. RNA/STAR.sh

   • Description: This script is for aligning high-throughput RNA-seq data. It takes fastq data as input.
   • How to use: Change the folder link to the input files and run sh RNA/STAR.sh $sample. Get more information from the STAR GitHub page.

2. RNA/RSEM.sh

   • Description: This script is for estimating gene and isoform expression levels from RNA-Seq data. It takes bam files from STAR as input.
   • How to use: Change the folder link to the input files and run sh RNA/RSEM.sh $sample. Get more information from the RSEM GitHub page.

Figure folder

• Description: This folder contains R scripts to generate plots used in the article. Some example data can be found in the Data directory. If you have all the related files, you can reproduce the results by running these R scripts like Rscript Figure1.R.

Dependencies

R session info:

> sessionInfo()
R version 4.3.2 (2023-10-31 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 11 x64 (build 22621)

Matrix products: default


locale:
[1] LC_COLLATE=English_United States.utf8  LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8 LC_NUMERIC=C            
[5] LC_TIME=English_United States.utf8  

time zone: Asia/Shanghai
tzcode source: internal

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

other attached packages:
 [1] ggfortify_0.4.16            apTreeshape_1.5-0.1         GSVA_1.50.0  
 [4] fgsea_1.28.0                data.table_1.14.10          DESeq2_1.42.0  
 [7] SummarizedExperiment_1.32.0 MatrixGenerics_1.14.0       matrixStats_1.2.0  
[10] GenomicRanges_1.54.1        GenomeInfoDb_1.38.2         colorRamps_2.3.1   
[13] dendextend_1.17.1           scales_1.3.0                ggsci_3.0.0  
[16] msigdbr_7.5.1               org.Hs.eg.db_3.18.0         AnnotationDbi_1.64.1   
[19] IRanges_2.36.0              S4Vectors_0.40.2            Biobase_2.62.0   
[22] BiocGenerics_0.48.1         clusterProfiler_4.10.0      circlize_0.4.15  
[25] ConsensusClusterPlus_1.66.0 survminer_0.4.9             survival_3.5-7   
[28] RColorBrewer_1.1-3          ComplexHeatmap_2.18.0       ape_5.7-1    
[31] ggpubr_0.6.0                lubridate_1.9.3             forcats_1.0.0  
[34] stringr_1.5.1               dplyr_1.1.4                 purrr_1.0.2  
[37] readr_2.1.4                 tidyr_1.3.0                 tibble_3.2.1   
[40] ggplot2_3.4.4               tidyverse_2.0.0  

loaded via a namespace (and not attached):
  [1] cubature_2.1.0              splines_4.3.2               bitops_1.0-7   
  [4] ggplotify_0.1.2             polyclip_1.10-6             graph_1.80.0   
  [7] XML_3.99-0.16.1             lifecycle_1.0.4             rstatix_0.7.2  
 [10] doParallel_1.0.17           vroom_1.6.5                 lattice_0.21-9   
 [13] MASS_7.3-60                 backports_1.4.1             magrittr_2.0.3   
 [16] pbapply_1.7-2               cowplot_1.1.2               DBI_1.1.3    
 [19] abind_1.4-5                 zlibbioc_1.48.0             ggraph_2.1.0   
 [22] RCurl_1.98-1.13             yulab.utils_0.1.1           tweenr_2.0.2   
 [25] GenomeInfoDbData_1.2.11     KMsurv_0.1-5                enrichplot_1.22.0  
 [28] ggrepel_0.9.4               irlba_2.3.5.1               tidytree_0.4.6   
 [31] MatrixModels_0.5-3          annotate_1.80.0             DelayedMatrixStats_1.24.0  
 [34] codetools_0.2-19            DelayedArray_0.28.0         DOSE_3.28.2  
 [37] ggforce_0.4.1               tidyselect_1.2.0            shape_1.4.6  
 [40] aplot_0.2.2                 farver_2.1.1                ScaledMatrix_1.10.0  
 [43] viridis_0.6.4               jsonlite_1.8.8              GetoptLong_1.0.5   
 [46] tidygraph_1.2.3             iterators_1.0.14            foreach_1.5.2  
 [49] ggnewscale_0.4.9            tools_4.3.2                 treeio_1.26.0  
 [52] Rcpp_1.0.11                 glue_1.6.2                  gridExtra_2.3  
 [55] SparseArray_1.2.2           mgcv_1.9-0                  xfun_0.41    
 [58] qvalue_2.34.0               HDF5Array_1.30.0            withr_2.5.2  
 [61] fastmap_1.1.1               rhdf5filters_1.14.1         fansi_1.0.6  
 [64] SparseM_1.81                rsvd_1.0.5                  digest_0.6.33  
 [67] timechange_0.2.0            R6_2.5.1                    gridGraphics_0.5-1   
 [70] colorspace_2.1-0            GO.db_3.18.0                RSQLite_2.3.4  
 [73] utf8_1.2.4                  generics_0.1.3              graphlayouts_1.0.2   
 [76] httr_1.4.7                  S4Arrays_1.2.0              scatterpie_0.2.1   
 [79] pkgconfig_2.0.3             gtable_0.3.4                blob_1.2.4   
 [82] SingleCellExperiment_1.24.0 XVector_0.42.0              survMisc_0.5.6   
 [85] shadowtext_0.1.2            carData_3.0-5               GSEABase_1.64.0  
 [88] clue_0.3-65                 png_0.1-8                   ggfun_0.1.3  
 [91] knitr_1.45                  km.ci_0.5-6                 rstudioapi_0.15.0  
 [94] tzdb_0.4.0                  reshape2_1.4.4              rjson_0.2.21   
 [97] coda_0.19-4                 nlme_3.1-163                rhdf5_2.46.1   
[100] cachem_1.0.8                zoo_1.8-12                  GlobalOptions_0.1.2  
[103] parallel_4.3.2              HDO.db_0.99.1               pillar_1.9.0   
[106] vctrs_0.6.5                 BiocSingular_1.18.0         car_3.1-2    
[109] beachmat_2.18.0             xtable_1.8-4                cluster_2.1.4  
[112] cli_3.6.2                   locfit_1.5-9.8              compiler_4.3.2   
[115] rlang_1.1.2                 crayon_1.5.2                ggsignif_0.6.4   
[118] labeling_0.4.3              plyr_1.8.9                  fs_1.6.3     
[121] stringi_1.8.3               viridisLite_0.4.2           BiocParallel_1.36.0  
[124] babelgene_22.9              munsell_0.5.0               Biostrings_2.70.1  
[127] lazyeval_0.2.2              quantreg_5.97               GOSemSim_2.28.0  
[130] Matrix_1.6-4                hms_1.1.3                   patchwork_1.1.3  
[133] sparseMatrixStats_1.14.0    bit64_4.0.5                 Rhdf5lib_1.24.1  
[136] KEGGREST_1.42.0             igraph_1.6.0                broom_1.0.5  
[139] memoise_2.0.1               ggtree_3.10.0               fastmatch_1.1-4  
[142] bit_4.0.5                   gson_0.1.0 

Data source

The raw exome and RNA sequencing data have been deposited in the Genome Sequence Archive (GSA) hosted at National Genomics Data Center HRA002112, processed data can be found at source data file from the article.