https://github.com/bhklab/radiogx-analysis
https://github.com/bhklab/radiogx-analysis
Last synced: 4 months ago
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- Host: GitHub
- URL: https://github.com/bhklab/radiogx-analysis
- Owner: bhklab
- Created: 2017-10-25T12:37:57.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2019-06-25T17:17:47.000Z (almost 6 years ago)
- Last Synced: 2024-12-30T03:20:55.574Z (5 months ago)
- Language: R
- Size: 25.7 MB
- Stars: 1
- Watchers: 6
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# RadioGx Analysis
Scripts to reproduce figures.
Manuscript: Modeling cellular response in large-scale radiogenomic databases to advance precision radiotherapy
Venkata SK. Manem, Meghan Lambie, Petr Smirnov, Victor Kofia, Mark Freeman, Marianne Koritzinsky, Mohamed E. Abazeed, Benjamin Haibe-Kains, Scott V. Bratman
Packages:
https://cran.r-project.org/web/packages/RadioGx/index.html
https://cran.r-project.org/web/packages/CoreGx/index.html
Figure2-Code.R: Fitting of dose-response data to the LQ model and concordance of radiation response across asays. (A) LQ model fit using RadioGx on the SNU-245 cholangiocarcinoma cell line (red) and SK-ES-1 Ewing sarcoma cell line (blue). The LQ model describes the fraction of cells predicted to survive (y-axis) a uniform radiation dose (x-axis) and is characterized by and components for each cell line. For SNU-245 and SK-ES-1, =0.14 ( Gy-1), (Gy-2)=0 and =0.45 (Gy-1),=0.02 (Gy-2), respectively. Solid curves indicate the model fit and points denote experimental data (Yard et al., 2016). (B) Histogram of AUC values calculated using the computeAUC function in RadioGx. (C) Correlation (Pearson R with standard deviation) of radioresponse results produced by the 9-day viability assay and the standard clonogenic assay according to the following indicators: SF2, SF4, SF6, SF8, and AUC. Primary data were obtained from Yard et al.(Yard et al., 2016).
Figure3-Code.R: Concordance of SF2 and AUC. (A) Correlation between the radioresponse indicators, SF2 and AUC, across 498 cell lines. (B) Pearson correlation (with standard deviation) between SF2 and AUC across 498 cell lines based on tertiles. (C) Venn diagram illustrating the transcriptional pathways associated with radioresponse using SF2 or AUC as the response indicator. (D) False discovery rate (FDR) for each transcriptional pathway from (C) illustrating greater levels of statistical significance among pathways specific to AUC.
Figure4-Code.R: Distinct biological underpinnings of alpha/beta derived from the LQ model. (A) Histogram of alpha/beta (Gy) values obtained from the LQ model across all cell lines. (B) Pearson correlations (with standard deviation) between AUC and the alpha and beta components of the LQ model. (C) Transcriptional pathways that are significantly associated with AUC, alpha, and/or beta.
Figure5-Code.R: Examining somatic mutations in positive control gene set for impact on radiation response. (A) Boxplots (Tukey) displaying the radiation response of cell lines harboring mutations in at least one of the 14 positive control genes. Wilcoxon-U p-values were determined by comparing ‘VEP High’ and ‘WT’ groups. (B) Thick Forest plot showing the cohen’s d effect size of the impact of each individual mutation as well as the summary (aggregate of all 14 genes) with 95% confidence interval. Height of the error bars is proportional to the weight of the impact, which is influenced by cell line number within the ‘VEP High’ mutation group.
Figure6-Code.R: Integrative analysis of radiobiological model with transcriptomic data and prediction of radioresponse under hypoxia. (A) Hypothetical illustration of cancer cell surviving fraction according to dose and oxygen partial pressure, as modeled using RadioGx. Solid curves are modeled using Equation (3) (Methods). The computed AUC values are 2.41, 2.71, 2.97 for normoxia (160 mmHg), hypoxic condition with 10 mmHg, and hypoxic condition with 5 mmHg, respectively. (B) Changes in AUC by tissue type (with minimum of 15 cell lines within RadioGx), ranked based on median AUC under normoxic conditions. Hypoxic condition: 5 mmHg. (C) Difference in ranks between the oxic and hypoxic conditions across all genes using a univariate modeling approach.
Figure7-Code.R: Tissue specificity of molecular determinants of radioresponse. (A) The tumour types (columns) represented by a minimum of 15 cell lines were considered for analysis. A total of 281 pathways are depicted (rows) and are annotated by function. Colours designate pathways significantly associated with AUC (FDR < 5%). (B) Heterogeneity of alpha/beta ratios across various tumour types.
Figure8-Code.R: Identification of drugs and pharmacological classes with cytotoxic effects on cancer cell lines that correlate with radioresponse. Pharmacological enrichment analysis using radiation AUC as the radioresponse indicator. Pharmacological classes with statistically significant associations with radioresponse in cancer cell lines are indicated.
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> sessionInfo()
R version 3.5.0 (2018-04-23)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows >= 8 x64 (build 9200)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.1252 LC_CTYPE=English_United States.1252 LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C LC_TIME=English_United States.1252
attached base packages:
[1] grid stats graphics grDevices utils datasets methods base
other attached packages:
[1] ggplot2_3.0.0 gplots_3.0.1 RColorBrewer_1.1-2 reshape_0.8.7 bindrcpp_0.2.2
[6] dplyr_0.7.6 VennDiagram_1.6.20 futile.logger_1.4.3 piano_1.20.0 readxl_1.1.0
[11] RadioGx_0.0.0.0.9000 CoreGx_0.0.001 caTools_1.17.1.1 gdata_2.18.0
loaded via a namespace (and not attached):
[1] Rcpp_0.12.18 lattice_0.20-35 relations_0.6-8 gtools_3.8.1 assertthat_0.2.0
[6] digest_0.6.16 slam_0.1-43 lsa_0.73.1 R6_2.2.2 cellranger_1.1.0
[11] plyr_1.8.4 futile.options_1.0.1 pillar_1.3.0 rlang_0.2.2 lazyeval_0.2.1
[16] rstudioapi_0.7 data.table_1.11.4 Matrix_1.2-14 labeling_0.3 sets_1.0-18
[21] BiocParallel_1.14.2 stringr_1.3.1 igraph_1.2.2 munsell_0.5.0 fgsea_1.6.0
[26] compiler_3.5.0 pkgconfig_2.0.2 BiocGenerics_0.26.0 marray_1.58.0 tidyselect_0.2.4
[31] tibble_1.4.2 gridExtra_2.3 crayon_1.3.4 withr_2.1.2 bitops_1.0-6
[36] SnowballC_0.5.1 gtable_0.2.0 magrittr_1.5 formatR_1.5 scales_1.0.0
[41] KernSmooth_2.23-15 stringi_1.1.7 reshape2_1.4.3 limma_3.36.3 fastmatch_1.1-0
[46] lambda.r_1.2.3 tools_3.5.0 Biobase_2.40.0 glue_1.3.0 purrr_0.2.5
[51] parallel_3.5.0 yaml_2.2.0 colorspace_1.3-2 cluster_2.0.7-1 bindr_0.1.1