Last updated: 2025-12-19

Checks: 3 3

Knit directory: ~/Documents/DEPMAPforBRCA1P1revisions/TNBCvH1808vM231/

This reproducible R Markdown analysis was created with workflowr (version 1.7.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

Environment: empty

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

Seed: set.seed(12345)

The command set.seed(12345) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Cache: detected

The following chunks had caches available:
  • build_ranked_lists_targets_vs_tnbc
  • gse_KEGG_targets_vs_tnbc
  • gsea_go_targets_vs_tnbc_4plots
  • hallmark_enrichr_multicell
  • hallmark_gsea_targets_vs_tnbc
  • hallmark_ora_targets_vs_tnbc
  • kegg_ora_targets_vs_tnbc
  • ora_enrichGO_targets_vs_tnbc
  • reactome_gsea_targets_vs_tnbc
  • reactome_ora
  • Top100_from_precomputed_outgroupCSVs

To ensure reproducibility of the results, delete the cache directory TNBCvH1808vM231_cache and re-run the analysis. To have workflowr automatically delete the cache directory prior to building the file, set delete_cache = TRUE when running wflow_build() or wflow_publish().

File paths: absolute

Using absolute paths to the files within your workflowr project makes it difficult for you and others to run your code on a different machine. Change the absolute path(s) below to the suggested relative path(s) to make your code more reproducible.

absolute relative
C:/Users/maryamshariff/Documents/DEPMAPforBRCA1P1revisions/TNBCvH1808vM231/HCC1806 (ACH-000624) log2(TPM+1) vs mean log2(TPM+1) of 26 TNBC no H1806 models.png HCC1806 (ACH-000624) log2(TPM+1) vs mean log2(TPM+1) of 26 TNBC no H1806 models.png
C:/Users/maryamshariff/Documents/DEPMAPforBRCA1P1revisions/TNBCvH1808vM231/MDAMB231 (ACH-000768) log2(TPM+1) vs mean log2(TPM+1) of 26 TNBC no M231 models.png MDAMB231 (ACH-000768) log2(TPM+1) vs mean log2(TPM+1) of 26 TNBC no M231 models.png
~/DEPMAPforBRCA1P1revisions/TNBCvH1808vM231 .

Repository version: no version control

Tracking code development and connecting the code version to the results is critical for reproducibility. To start using Git, open the Terminal and type git init in your project directory.

This project is not being versioned with Git. To obtain the full reproducibility benefits of using workflowr, please see ?wflow_start.

For “Regulation of Antiviral and Antitumor Immunity by the BRCA1 Pseudogene in Human Cancers” we have received reviewer comment 13 as stated “The switch from MDA-MB-231 to HCC-1806 cells is justified by the complete growth inhibition observed in the former; however, a more thorough explanation of how HCC-1806 better models the biology of BRCA1P1 loss would be helpful. Differences in baseline BRCA1P1 expression, immune interactions, and antiviral pathway activation between these cell lines should be addressed.”

We planned to address this comment by checking immune interactions, and antiviral pathway activation between MDA-MB-231 and HCC-1806 cells by comparing each of these cell lines to 27 other TNBC cell lines.

DEPMAP + Top 100 Gene Lists

First, we identified the top 100 genes up-regulated in each target cell line—MDA-MB-231 and HCC1806—relative to triple-negative breast cancer (TNBC) cell lines. TNBC cell lines were selected from the DepMap database using ModelSubtypeFeatures annotations, retaining only samples classified as TNBC or basal-like TNBC; luminal TNBC samples were excluded.

Gene expression analyses were performed using publicly available DepMap expression data (DepMap Public 25Q3), comprising log-transformed transcript abundance values [log(TPM + 1)] derived from unstranded RNA-sequencing of human protein-coding genes. Expression profiles corresponding to ACH-000624 (HCC1806), ACH-000768 (MDA-MB-231), and a panel of 27 TNBC cell lines were extracted for downstream analysis. To construct the TNBC outgroup, gene expression values across the 27 TNBC cell lines were aggregated on a per-gene basis using the mean, generating a composite TNBC reference expression profile for each gene.

For each target cell line, gene-level fold changes were calculated as the difference between the log-transformed expression value in the target cell line and the corresponding value in the TNBC outgroup (Target – Outgroup). Genes were then ranked by this fold-change metric to enable pathway-level analyses. This ranking strategy is consistent with recommended pre-ranking approaches for Gene Set Enrichment Analysis (GSEA) in settings where replicate-based statistical testing is not feasible and only log-scale expression values are available. Because these analyses rely on single expression profiles per cell line and a summarized outgroup reference, the resulting enrichment patterns should be interpreted as hypothesis-generating rather than as formal differential expression results.

Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.

Top 100 up amd down in HCC1806 compared to 27 TNBC cell lines

res_hcc$top_up
res_hcc$top_down

Top 100 up amd down in MDAMB231 compared to 27 TNBC cell lines

res_m231$top_up
res_m231$top_down

I validated this ranking using DepMap visualization software. Here I mapped a couple of the top upregulated genes in both HCC1806 and MDA-MB-231 vs all other TNBC cell lines and each gene corresponded to fold change values generated by the DepMap scatterplot.

Graph 1 of HCC1806 Gene Expression Compared to 27 TNBC cell lines generated using DepMap Visualization software.

Graph 1 of HCC1806 Gene Expression Compared to 27 TNBC cell lines generated using DepMap Visualization software.

Graph 2 of MDAMB231 Gene Expression Compared to 27 TNBC cell lines generated using DepMap Visualization software.

Graph 2 of MDAMB231 Gene Expression Compared to 27 TNBC cell lines generated using DepMap Visualization software.

Functional Class Scoring Methods: GSEA using clusterprofiler and downstream GO, KEGG, Hallmark, and Reactome Pathway Analysis

Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.

Gene Ontology using GSEA with gseGO and clusterProfiler

Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.

KEGG Pathway Analysis using GSEA with gseKEGG and clusterProfiler

Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.

Hallmark Pathway Analysis using GSEA with msigdbr

Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.

Reactome Pathway Analysis using GSEA with gsePathway and clusterProfiler

Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.

Over-Representation Analysis: clusterProfiler ORA for GO, KEGG, Hallmark and Reactome

ORA using enrichGO clusterProfilier function for GO

Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.

ORA using enrichKEGG clusterProfilier function for Pathway Analysis

Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.

ORA with MSigDB HALLMARK (clusterProfiler::enricher + msigdbr)

Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.

Using ReactomePA::enrichPathway + clusterProfiler/enrichplot viz

Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.

Validating ORA Results from clusterProfiler in Enrichr: Hallmark

ORA MsigDB Hallmark ORA from Enrichr (multi-cell line)

Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details. 


sessionInfo()
R version 4.5.1 (2025-06-13 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 26100)

Matrix products: default
  LAPACK version 3.12.1

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

time zone: America/Chicago
tzcode source: internal

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

other attached packages:
 [1] lubridate_1.9.4        purrr_1.1.0            tidyr_1.3.1           
 [4] tidyverse_2.0.0        tibble_3.3.0           stringr_1.5.2         
 [7] RColorBrewer_1.1-3     readxl_1.4.5           readr_2.1.5           
[10] ReactomePA_1.52.0      pheatmap_1.0.13        org.Hs.eg.db_3.21.0   
[13] AnnotationDbi_1.70.0   IRanges_2.42.0         S4Vectors_0.46.0      
[16] Biobase_2.68.0         BiocGenerics_0.54.0    generics_0.1.4        
[19] openxlsx_4.2.8         msigdbr_25.1.1         ggupset_0.4.1         
[22] ggplot2_4.0.0          forcats_1.0.1          fgsea_1.34.2          
[25] enrichplot_1.28.4      dplyr_1.1.4            DOSE_4.2.0            
[28] clusterProfiler_4.16.0

loaded via a namespace (and not attached):
  [1] rstudioapi_0.17.1       jsonlite_2.0.0          magrittr_2.0.3         
  [4] ggtangle_0.0.9          farver_2.1.2            rmarkdown_2.30         
  [7] fs_1.6.6                vctrs_0.6.5             memoise_2.0.1          
 [10] ggtree_3.16.3           htmltools_0.5.8.1       curl_7.0.0             
 [13] cellranger_1.1.0        gridGraphics_0.5-1      sass_0.4.10            
 [16] bslib_0.9.0             plyr_1.8.9              cachem_1.1.0           
 [19] igraph_2.2.1            lifecycle_1.0.4         pkgconfig_2.0.3        
 [22] Matrix_1.7-3            R6_2.6.1                fastmap_1.2.0          
 [25] gson_0.1.0              GenomeInfoDbData_1.2.14 digest_0.6.37          
 [28] aplot_0.2.9             patchwork_1.3.2         rprojroot_2.1.1        
 [31] RSQLite_2.4.3           timechange_0.3.0        httr_1.4.7             
 [34] polyclip_1.10-7         compiler_4.5.1          bit64_4.6.0-1          
 [37] withr_3.0.2             graphite_1.54.0         S7_0.2.0               
 [40] BiocParallel_1.42.1     viridis_0.6.5           DBI_1.2.3              
 [43] ggforce_0.5.0           R.utils_2.13.0          MASS_7.3-65            
 [46] rappdirs_0.3.3          tools_4.5.1             otel_0.2.0             
 [49] ape_5.8-1               zip_2.3.3               httpuv_1.6.16          
 [52] R.oo_1.27.1             glue_1.8.0              nlme_3.1-168           
 [55] GOSemSim_2.34.0         promises_1.5.0          grid_4.5.1             
 [58] reshape2_1.4.4          gtable_0.3.6            tzdb_0.5.0             
 [61] R.methodsS3_1.8.2       hms_1.1.4               data.table_1.17.8      
 [64] tidygraph_1.3.1         XVector_0.48.0          ggrepel_0.9.6          
 [67] pillar_1.11.1           yulab.utils_0.2.2       babelgene_22.9         
 [70] later_1.4.4             splines_4.5.1           tweenr_2.0.3           
 [73] treeio_1.32.0           lattice_0.22-7          bit_4.6.0              
 [76] tidyselect_1.2.1        GO.db_3.21.0            Biostrings_2.76.0      
 [79] reactome.db_1.92.0      knitr_1.50              git2r_0.36.2           
 [82] gridExtra_2.3           xfun_0.53               graphlayouts_1.2.2     
 [85] stringi_1.8.7           UCSC.utils_1.4.0        workflowr_1.7.2        
 [88] lazyeval_0.2.2          ggfun_0.2.0             yaml_2.3.10            
 [91] evaluate_1.0.5          codetools_0.2-20        ggraph_2.2.2           
 [94] qvalue_2.40.0           graph_1.86.0            ggplotify_0.1.3        
 [97] cli_3.6.5               jquerylib_0.1.4         Rcpp_1.1.0             
[100] GenomeInfoDb_1.44.3     png_0.1-8               parallel_4.5.1         
[103] assertthat_0.2.1        blob_1.2.4              viridisLite_0.4.2      
[106] tidytree_0.4.6          scales_1.4.0            crayon_1.5.3           
[109] rlang_1.1.6             cowplot_1.2.0           fastmatch_1.1-6        
[112] KEGGREST_1.48.1