Abstract

‘deplink’ compares the genetic/epigenetic features between cancer cell lines with highest and lowest dependencies of a gene set (signature).

All-in-one analysis

For example, deplink compares the genetic/epigenetic features between cancer cell lines with highest and lowest dependencies of “9-1-1” complex members:

The results will be output to a local directory (default: root directory) under a folder in name of the designated “signature.name” (“9-1-1” in this case).

Several cutoffs are set by default as below and can be changed by will. Please see the help page for more details (?deplink).

The comparison covers the following features:

  • Genomic/epigenetic features
    • Genetic dependency
    • Gene expression
    • Chromatin modification
  • Genome instability
    • Genetic mutation
    • COSMIC signature
    • Tumor mutation burden (TMB)
    • Copy number variation (CNV)
    • Microsatellite instability (MSI)
  • Drug sensitivity
    • Drug sensitivity from GDSC data set
    • Drug sensitivity from PRISM data set
  • Immune infiltration
    • Immune signature gene (ISG)
  • Stemness
    • mRNA stemness index (mRNAsi)
    • Epithelial–mesenchymal transition (EMT)
  • Misc.
    • Cancer type

Individual analysis

deplink can compare individual genetic/epigenetic feature between cancer cell lines with highest and lowest dependencies of a gene set (signature). For example of “9-1-1” complex members:

Cancer type component

‘cancertypeHigh’ displays the cancer type component of cell lines with high dependencies of a gene set (signature).

‘cancertypeLow’ displays the cancer type component of cell lines with low dependencies of a gene set (signature).

‘cancertypeLandscape’ displays the landscape of cancer type component of cell lines with different dependencies of a gene set (signature).

Genetic dependency

‘dependency’ compares the genetic dependency between cancer cell lines with highest and lowest dependencies of a gene set (signature).

Gene expression

‘expressions’ compares the gene expression between cancer cell lines with highest and lowest dependencies of a gene set (signature).

Chromatin modification

‘chromatinModification’ compares the chromatin modification abundance between cancer cell lines with highest and lowest dependencies of a gene set (signature).

Genetic mutation

‘mutations’ compares the genetic mutations between cancer cell lines with highest and lowest dependencies of a gene set (signature).

COSMIC signature

‘cosmic’ compares the COSMIC signatures between cell lines with highest and lowest dependencies of a gene set (signature).

Tumor mutation burden (TMB)

‘tmb’ compares the tumor mutation burden (TMB) between cancer cell lines with highest and lowest dependencies of a gene set (signature).

Copy number variation (CNV)

‘cnv’ compares the copy number variation (CNV) between cancer cell lines with highest and lowest dependencies of a gene set (signature).

Microsatellite instability (MSI)

‘msi’ compares the microsatellite instability (MSI) between cancer cell lines with highest and lowest dependencies of a gene set (signature).

Drug sensitivity

‘drugGDSC’ compares the drug sensitivity (GDSC dataset) between cancer cell lines with highest and lowest dependencies of a gene set (signature).

‘drugPRISM’ compares the drug sensitivity (PRISM dataset) between cancer cell lines with highest and lowest dependencies of a gene set (signature).

Immune signature gene (ISG)

‘isg’ compares the immune signature gene (ISG) between cancer cell lines with highest and lowest dependencies of a gene set (signature).

mRNA stemness index (mRNAsi)

‘mrnasi’ compares the mRNA stemness index (mRNAsi) between cancer cell lines with highest and lowest dependencies of a gene set (signature).

Epithelial–mesenchymal transition (EMT)

‘emt’ compares the epithelial–mesenchymal transition (EMT) between cancer cell lines with highest and lowest dependencies of a gene set (signature).

Citation

If you use deplink in published research, please cite the most appropriate paper(s) from this list:

  1. X Chen, J McGuire, F Zhu, X Xu, Y Li, D Karagiannis, R Dalla-Favera, A Ciccia, J Amengual & C Lu (2020). Harnessing genetic dependency correlation network to reveal chromatin vulnerability in cancer. In preparation.

Session Information

Here is the output of sessionInfo() on the system on which this document was compiled:

## R version 3.5.0 (2018-04-23)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 17134)
## 
## Matrix products: default
## 
## locale:
## [1] LC_COLLATE=English_United States.1252 
## [2] LC_CTYPE=English_United States.1252   
## [3] LC_MONETARY=English_United States.1252
## [4] LC_NUMERIC=C                          
## [5] LC_TIME=English_United States.1252    
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] deplink_0.99.0    wesanderson_0.3.6 purrr_0.3.4       ggrepel_0.8.2    
## [5] ggpubr_0.2.5      magrittr_1.5      ggplot2_3.3.0     data.table_1.12.8
## [9] cowplot_1.0.0    
## 
## loaded via a namespace (and not attached):
##  [1] Rcpp_0.12.18     pillar_1.3.0     compiler_3.5.0   bindr_0.1.1     
##  [5] prettydoc_0.4.0  tools_3.5.0      digest_0.6.18    evaluate_0.14   
##  [9] tibble_1.4.2     gtable_0.2.0     pkgconfig_2.0.2  rlang_0.4.5     
## [13] yaml_2.2.0       xfun_0.17        bindrcpp_0.2.2   withr_2.1.2     
## [17] dplyr_0.7.6      stringr_1.3.1    knitr_1.23       grid_3.5.0      
## [21] tidyselect_0.2.5 glue_1.3.0       R6_2.2.2         rmarkdown_2.3   
## [25] scales_1.0.0     htmltools_0.3.6  assertthat_0.2.0 colorspace_1.3-2
## [29] ggsignif_0.4.0   labeling_0.3     stringi_1.2.4    munsell_0.5.0   
## [33] crayon_1.3.4