Last updated: 2024-02-05

Checks: 7 0

Knit directory: Cardiotoxicity/

This reproducible R Markdown analysis was created with workflowr (version 1.7.1). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.


Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

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.

The command set.seed(20230109) 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.

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

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version e80ab0b. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:


Ignored files:
    Ignored:    .RData
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/
    Ignored:    analysis/variance_values by gene.png
    Ignored:    data/41588_2018_171_MOESM3_ESMeQTL_ST2_for paper.csv
    Ignored:    data/Arr_GWAS.txt
    Ignored:    data/Arr_geneset.RDS
    Ignored:    data/BC_cell_lines.csv
    Ignored:    data/BurridgeDOXTOX.RDS
    Ignored:    data/CADGWASgene_table.csv
    Ignored:    data/CAD_geneset.RDS
    Ignored:    data/CALIMA_Data/
    Ignored:    data/CMD04_75DRCviability.csv
    Ignored:    data/CMD04_87DRCviability.csv
    Ignored:    data/CMD05_75DRCviability.csv
    Ignored:    data/CMD05_87DRCviability.csv
    Ignored:    data/Clamp_Summary.csv
    Ignored:    data/Cormotif_24_k1-5_raw.RDS
    Ignored:    data/Counts_RNA_ERMatthews.txt
    Ignored:    data/DAgostres24.RDS
    Ignored:    data/DAtable1.csv
    Ignored:    data/DDEMresp_list.csv
    Ignored:    data/DDE_reQTL.txt
    Ignored:    data/DDEresp_list.csv
    Ignored:    data/DEG-GO/
    Ignored:    data/DEG_cormotif.RDS
    Ignored:    data/DF_Plate_Peak.csv
    Ignored:    data/DRC48hoursdata.csv
    Ignored:    data/Da24counts.txt
    Ignored:    data/Dx24counts.txt
    Ignored:    data/Dx_reQTL_specific.txt
    Ignored:    data/EPIstorelist24.RDS
    Ignored:    data/Ep24counts.txt
    Ignored:    data/FC_necela.RDS
    Ignored:    data/FC_necela_names.RDS
    Ignored:    data/Full_LD_rep.csv
    Ignored:    data/GOIsig.csv
    Ignored:    data/GOplots.R
    Ignored:    data/GTEX_setsimple.csv
    Ignored:    data/GTEX_sig24.RDS
    Ignored:    data/GTEx_gene_list.csv
    Ignored:    data/HFGWASgene_table.csv
    Ignored:    data/HF_geneset.RDS
    Ignored:    data/Heart_Left_Ventricle.v8.egenes.txt
    Ignored:    data/Heatmap_mat.RDS
    Ignored:    data/Heatmap_sig.RDS
    Ignored:    data/Hf_GWAS.txt
    Ignored:    data/K_cluster
    Ignored:    data/K_cluster_kisthree.csv
    Ignored:    data/K_cluster_kistwo.csv
    Ignored:    data/Knowles_log2cpm_real.RDS
    Ignored:    data/Knowles_variation_data.RDS
    Ignored:    data/Knowles_variation_data_conc.RDS
    Ignored:    data/Knowlesvarlist.RDS
    Ignored:    data/LD50_05via.csv
    Ignored:    data/LDH48hoursdata.csv
    Ignored:    data/Mt24counts.txt
    Ignored:    data/NoRespDEG_final.csv
    Ignored:    data/RINsamplelist.txt
    Ignored:    data/RNA_seq_trial.RDS
    Ignored:    data/Schneider_GWAS.txt
    Ignored:    data/Seonane2019supp1.txt
    Ignored:    data/Sup_replicate_values.csv
    Ignored:    data/TMMnormed_x.RDS
    Ignored:    data/TOP2Bi-24hoursGO_analysis.csv
    Ignored:    data/TR24counts.txt
    Ignored:    data/TableS10.csv
    Ignored:    data/TableS11.csv
    Ignored:    data/TableS9.csv
    Ignored:    data/Top2_expression.RDS
    Ignored:    data/Top2biresp_cluster24h.csv
    Ignored:    data/Var_test_list.RDS
    Ignored:    data/Var_test_list24.RDS
    Ignored:    data/Var_test_list24alt.RDS
    Ignored:    data/Var_test_list3.RDS
    Ignored:    data/Vargenes.RDS
    Ignored:    data/Viabilitylistfull.csv
    Ignored:    data/allexpressedgenes.txt
    Ignored:    data/allfinal3hour.RDS
    Ignored:    data/allgenes.txt
    Ignored:    data/allmatrix.RDS
    Ignored:    data/allmymatrix.RDS
    Ignored:    data/annotation_data_frame.RDS
    Ignored:    data/averageviabilitytable.RDS
    Ignored:    data/averageviabilitytable.csv
    Ignored:    data/avgLD50.RDS
    Ignored:    data/avg_LD50.RDS
    Ignored:    data/avg_via_table.csv
    Ignored:    data/backGL.txt
    Ignored:    data/burr_genes.RDS
    Ignored:    data/calcium_data.RDS
    Ignored:    data/clamp_summary.RDS
    Ignored:    data/cormotif_3hk1-8.RDS
    Ignored:    data/cormotif_initalK5.RDS
    Ignored:    data/cormotif_initialK5.RDS
    Ignored:    data/cormotif_initialall.RDS
    Ignored:    data/cormotifprobs.csv
    Ignored:    data/counts24hours.RDS
    Ignored:    data/cpmcount.RDS
    Ignored:    data/cpmnorm_counts.csv
    Ignored:    data/crispr_genes.csv
    Ignored:    data/ctnnt_results.txt
    Ignored:    data/cvd_GWAS.txt
    Ignored:    data/dat_cpm.RDS
    Ignored:    data/data_outline.txt
    Ignored:    data/drug_noveh1.csv
    Ignored:    data/efit2.RDS
    Ignored:    data/efit2_final.RDS
    Ignored:    data/efit2results.RDS
    Ignored:    data/ensembl_backup.RDS
    Ignored:    data/ensgtotal.txt
    Ignored:    data/filcpm_counts.RDS
    Ignored:    data/filenameonly.txt
    Ignored:    data/filtered_cpm_counts.csv
    Ignored:    data/filtered_raw_counts.csv
    Ignored:    data/filtermatrix_x.RDS
    Ignored:    data/folder_05top/
    Ignored:    data/framefun24.RDS
    Ignored:    data/geneDoxonlyQTL.csv
    Ignored:    data/gene_corr_df.RDS
    Ignored:    data/gene_corr_frame.RDS
    Ignored:    data/gene_prob_tran3h.RDS
    Ignored:    data/gene_probabilityk5.RDS
    Ignored:    data/geneset_24.RDS
    Ignored:    data/gostresTop2bi_ER.RDS
    Ignored:    data/gostresTop2bi_LR
    Ignored:    data/gostresTop2bi_LR.RDS
    Ignored:    data/gostresTop2bi_TI.RDS
    Ignored:    data/gostrescoNR
    Ignored:    data/gtex/
    Ignored:    data/heartgenes.csv
    Ignored:    data/highly_var_genelist.RDS
    Ignored:    data/hsa_kegg_anno.RDS
    Ignored:    data/individualDRCfile.RDS
    Ignored:    data/individual_DRC48.RDS
    Ignored:    data/individual_LDH48.RDS
    Ignored:    data/indv_noveh1.csv
    Ignored:    data/kegglistDEG.RDS
    Ignored:    data/kegglistDEG24.RDS
    Ignored:    data/kegglistDEG3.RDS
    Ignored:    data/knowfig4.csv
    Ignored:    data/knowfig5.csv
    Ignored:    data/label_list.RDS
    Ignored:    data/ld50_table.csv
    Ignored:    data/mean_vardrug1.csv
    Ignored:    data/mean_varframe.csv
    Ignored:    data/mymatrix.RDS
    Ignored:    data/new_ld50avg.RDS
    Ignored:    data/nonresponse_cluster24h.csv
    Ignored:    data/norm_LDH.csv
    Ignored:    data/norm_counts.csv
    Ignored:    data/old_sets/
    Ignored:    data/organized_drugframe.csv
    Ignored:    data/pca_all_anno.csv
    Ignored:    data/plan2plot.png
    Ignored:    data/plot_intv_list.RDS
    Ignored:    data/plot_list_DRC.RDS
    Ignored:    data/qval24hr.RDS
    Ignored:    data/qval3hr.RDS
    Ignored:    data/qvalueEPItemp.RDS
    Ignored:    data/raw_counts.csv
    Ignored:    data/response_cluster24h.csv
    Ignored:    data/sampsettrz.RDS
    Ignored:    data/schneider_closest_output.RDS
    Ignored:    data/sigVDA24.txt
    Ignored:    data/sigVDA3.txt
    Ignored:    data/sigVDX24.txt
    Ignored:    data/sigVDX3.txt
    Ignored:    data/sigVEP24.txt
    Ignored:    data/sigVEP3.txt
    Ignored:    data/sigVMT24.txt
    Ignored:    data/sigVMT3.txt
    Ignored:    data/sigVTR24.txt
    Ignored:    data/sigVTR3.txt
    Ignored:    data/siglist.RDS
    Ignored:    data/siglist_final.RDS
    Ignored:    data/siglist_old.RDS
    Ignored:    data/slope_table.csv
    Ignored:    data/supp10_24hlist.RDS
    Ignored:    data/supp10_3hlist.RDS
    Ignored:    data/supp_normLDH48.RDS
    Ignored:    data/supp_pca_all_anno.RDS
    Ignored:    data/supp_viadata.csv
    Ignored:    data/table3a.omar
    Ignored:    data/test_run_sample_list.txt
    Ignored:    data/testlist.txt
    Ignored:    data/toplistall.RDS
    Ignored:    data/trtonly_24h_genes.RDS
    Ignored:    data/trtonly_3h_genes.RDS
    Ignored:    data/tvl24hour.txt
    Ignored:    data/tvl24hourw.txt
    Ignored:    data/venn_code.R
    Ignored:    data/viability.RDS

Untracked files:
    Untracked:  .RDataTmp
    Untracked:  .RDataTmp1
    Untracked:  .RDataTmp2
    Untracked:  .RDataTmp3
    Untracked:  3hr all.pdf
    Untracked:  Code_files_list.csv
    Untracked:  Data_files_list.csv
    Untracked:  Doxorubicin_vehicle_3_24.csv
    Untracked:  Doxtoplist.csv
    Untracked:  EPIqvalue_analysis.Rmd
    Untracked:  Final.sup.pdf
    Untracked:  GWAS_list_of_interest.xlsx
    Untracked:  KEGGpathwaylist.R
    Untracked:  NA
    Untracked:  OmicNavigator_learn.R
    Untracked:  SNP_egenes_allfiles.RDS
    Untracked:  SNP_frame_pdf
    Untracked:  SNP_frame_pdf.pdf
    Untracked:  SigDoxtoplist.csv
    Untracked:  analysis/DRC_viability_check.Rmd
    Untracked:  analysis/Knowles2018.Rmd
    Untracked:  analysis/New_code_dec-23.R
    Untracked:  analysis/cellcycle_kegg_genes.R
    Untracked:  analysis/ciFIT.R
    Untracked:  analysis/export_to_excel.R
    Untracked:  analysis/featureCountsPLAY.R
    Untracked:  cleanupfiles_script.R
    Untracked:  code/biomart_gene_names.R
    Untracked:  code/constantcode.R
    Untracked:  code/corMotifcustom.R
    Untracked:  code/cpm_boxplot.R
    Untracked:  code/extracting_ggplot_data.R
    Untracked:  code/movingfilesto_ppl.R
    Untracked:  code/pearson_extract_func.R
    Untracked:  code/pearson_tox_extract.R
    Untracked:  code/plot1C.fun.R
    Untracked:  code/spearman_extract_func.R
    Untracked:  code/venndiagramcolor_control.R
    Untracked:  cormotif_p.post.list_4.csv
    Untracked:  figS1024h.pdf
    Untracked:  final.pdf
    Untracked:  individual-legenddark2.png
    Untracked:  installed_old.rda
    Untracked:  listoftranscripts
    Untracked:  motif_ER.txt
    Untracked:  motif_LR.txt
    Untracked:  motif_NR.txt
    Untracked:  motif_TI.txt
    Untracked:  output/ABHD8_dif_values.RDS
    Untracked:  output/C3orf18_dif_values.RDS
    Untracked:  output/Cardiotox_dif_values.RDS
    Untracked:  output/DNR_DEGlist.csv
    Untracked:  output/DNRvenn.RDS
    Untracked:  output/DOX_DEGlist.csv
    Untracked:  output/DOX_de_goi.csv
    Untracked:  output/DOXvenn.RDS
    Untracked:  output/EEF1B2_dif_values.RDS
    Untracked:  output/EEIG1_dif_values.RDS
    Untracked:  output/EPI_DEGlist.csv
    Untracked:  output/EPIvenn.RDS
    Untracked:  output/ESGN_rds.RDS
    Untracked:  output/FC_necela.RDS
    Untracked:  output/FC_necela_names.RDS
    Untracked:  output/FRS2_dif_values.RDS
    Untracked:  output/Figures/
    Untracked:  output/GTEXv8_gene_median_tpm.RDS
    Untracked:  output/GTEXv8_gene_tpm_heart_left_ventricle.RDS
    Untracked:  output/HDDC2_dif_values.RDS
    Untracked:  output/HER2_gene.RDS
    Untracked:  output/KEGGcellcyclegenes.RDS
    Untracked:  output/Knowles_S13.csv
    Untracked:  output/Knowles_log2cpm.csv
    Untracked:  output/Knowles_supp13.csv
    Untracked:  output/LD50tox_table.RDS
    Untracked:  output/MTX_DEGlist.csv
    Untracked:  output/MTXvenn.RDS
    Untracked:  output/PEX16_dif_values.RDS
    Untracked:  output/RASIP1_dif_values.RDS
    Untracked:  output/RMI1_dif_values.RDS
    Untracked:  output/RSID_QTL_list_full.txt
    Untracked:  output/SETA_analysis_reyes.RDS
    Untracked:  output/SGWAS_top50_order.csv
    Untracked:  output/SLC27A1_dif_values.RDS
    Untracked:  output/SLC28A3_dif_values.RDS
    Untracked:  output/SNP_egenes_allfiles.RDS
    Untracked:  output/SNP_list_ID.RDS
    Untracked:  output/SNP_list_full.txt
    Untracked:  output/SNP_supp.RDS
    Untracked:  output/TGFBR3L_dif_values.RDS
    Untracked:  output/TNS2_dif_values.RDS
    Untracked:  output/TOP_50SNPreffile.csv
    Untracked:  output/TRZ_DEGlist.csv
    Untracked:  output/TableS8.csv
    Untracked:  output/Volcanoplot_10
    Untracked:  output/Volcanoplot_10.RDS
    Untracked:  output/ZNF740_dif_values.RDS
    Untracked:  output/allfinal_sup10.RDS
    Untracked:  output/counts_v8_heart_left_ventricle_gct.RDS
    Untracked:  output/crisprfoldchange.RDS
    Untracked:  output/endocytosisgenes.csv
    Untracked:  output/expre7k.csv
    Untracked:  output/expressed_egenes_by_RSID.csv
    Untracked:  output/gene_corr_fig9.RDS
    Untracked:  output/genes.RDS
    Untracked:  output/legend_b.RDS
    Untracked:  output/motif_ERrep.RDS
    Untracked:  output/motif_LRrep.RDS
    Untracked:  output/motif_NRrep.RDS
    Untracked:  output/motif_TI_rep.RDS
    Untracked:  output/near_genes_SNP1.RDS
    Untracked:  output/necela_list_test.RDS
    Untracked:  output/necela_val_genes.RDS
    Untracked:  output/output-old/
    Untracked:  output/rank24genes.csv
    Untracked:  output/rank3genes.csv
    Untracked:  output/sequencinginformationforsupp.csv
    Untracked:  output/sequencinginformationforsupp.prn
    Untracked:  output/sigVDA24.txt
    Untracked:  output/sigVDA3.txt
    Untracked:  output/sigVDX24.txt
    Untracked:  output/sigVDX3.txt
    Untracked:  output/sigVEP24.txt
    Untracked:  output/sigVEP3.txt
    Untracked:  output/sigVMT24.txt
    Untracked:  output/sigVMT3.txt
    Untracked:  output/sigVTR24.txt
    Untracked:  output/sigVTR3.txt
    Untracked:  output/supplementary_motif_list_GO.RDS
    Untracked:  output/test_biomart_run.RDS
    Untracked:  output/toptablebydrug.RDS
    Untracked:  output/trop_knowles_fun.csv
    Untracked:  output/tvl24hour.txt
    Untracked:  output/x_counts.RDS
    Untracked:  reneebasecode.R

Unstaged changes:
    Modified:   analysis/DRC_analysis.Rmd
    Modified:   analysis/GOI_plots.Rmd
    Modified:   analysis/GTEx_genes.Rmd
    Deleted:    analysis/Knowles2019.Rmd
    Modified:   analysis/Var_genes.Rmd
    Modified:   analysis/after_comments.Rmd
    Modified:   analysis/variance_scrip.Rmd
    Modified:   output/daplot.RDS
    Modified:   output/dxplot.RDS
    Modified:   output/epplot.RDS
    Modified:   output/mtplot.RDS
    Modified:   output/plan2plot.png
    Modified:   output/trplot.RDS
    Modified:   output/veplot.RDS

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.


These are the previous versions of the repository in which changes were made to the R Markdown (analysis/Figure1.Rmd) and HTML (docs/Figure1.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File Version Author Date Message
Rmd e80ab0b reneeisnowhere 2024-02-05 wflow_publish("analysis/Figure1.Rmd")
html 9ede6bc reneeisnowhere 2024-02-05 Build site.
Rmd 57987b9 reneeisnowhere 2024-02-05 updates to figure
html 1c92d4c reneeisnowhere 2024-02-05 Build site.
Rmd 2920976 reneeisnowhere 2024-02-05 adding first panel
html 691cb4b reneeisnowhere 2023-09-27 Build site.
Rmd 584ccb1 reneeisnowhere 2023-09-27 updates to links and cleanup code
html 1f75fdb reneeisnowhere 2023-09-27 Build site.
Rmd 90e0eb2 reneeisnowhere 2023-09-27 adding new links
html 9eb4774 reneeisnowhere 2023-09-27 Build site.
Rmd e59dc7e reneeisnowhere 2023-09-27 adding new links
html 4bb0b80 reneeisnowhere 2023-07-28 Build site.
Rmd 974fcde reneeisnowhere 2023-07-28 new figures update
html 513ca48 reneeisnowhere 2023-07-26 Build site.
Rmd 7a6a0df reneeisnowhere 2023-07-26 updates in figures
html 05fe9b1 reneeisnowhere 2023-07-19 Build site.
Rmd f3ce7e9 reneeisnowhere 2023-07-19 updated LD50 plot
html e3fe073 reneeisnowhere 2023-07-18 Build site.
Rmd eff7550 reneeisnowhere 2023-07-18 update to MTX
html 9dd118a reneeisnowhere 2023-07-14 Build site.
Rmd bb3a004 reneeisnowhere 2023-07-14 updated figure arrangement and plots
html ed18a48 reneeisnowhere 2023-07-07 Build site.
Rmd 5930353 reneeisnowhere 2023-07-07 code and plot updates done Friday before Michelle notes
html 433a442 reneeisnowhere 2023-06-21 Build site.
Rmd 5d643ee reneeisnowhere 2023-06-21 update figure with error bars
html 6328422 reneeisnowhere 2023-06-16 Build site.
Rmd 0aceb9a reneeisnowhere 2023-06-16 adding code and graphs
html bd0e45f reneeisnowhere 2023-06-15 Build site.
Rmd f8f511a reneeisnowhere 2023-06-15 updates and simplifications of code
html f8f511a reneeisnowhere 2023-06-15 updates and simplifications of code
html 908b616 reneeisnowhere 2023-06-13 Build site.
Rmd 44ae8bb reneeisnowhere 2023-06-13 picture check
html f2d0e90 reneeisnowhere 2023-06-13 Build site.
Rmd 71b3ce2 reneeisnowhere 2023-06-13 picture check
html d175bfb reneeisnowhere 2023-06-13 Build site.
Rmd 0c83ba8 reneeisnowhere 2023-06-13 picture check
html 044fc66 reneeisnowhere 2023-06-12 Build site.
Rmd 1ebf470 reneeisnowhere 2023-06-12 adding figure 1

library(car)
library(tidyverse)
library(tinytex)
library(BiocGenerics)
library(data.table)
library(drc)
library(cowplot)
library(ggsignif)
library(RColorBrewer)
library(broom)
library(ComplexHeatmap)
library(png)

Figure 1

Top2i drugs affect cardiomyocyte viability in a dose-dependent manner.

A. Project overview: Image not given here. Please look at our paper.

knitr::include_graphics("docs/assests/Figure1-A.png")

B. Dose-response curves from 48 hour drug exposure.

daplot <- readRDS("output/daplot.RDS")  
dxplot <- readRDS("output/dxplot.RDS")
epplot<- readRDS("output/epplot.RDS")
mtplot<- readRDS("output/mtplot.RDS")
trplot<- readRDS("output/trplot.RDS")
veplot<- readRDS("output/veplot.RDS")
veplot <- veplot+xlab(NULL)
trplot <- trplot+xlab(NULL)
legend_b <- readRDS("output/legend_b.RDS")
plan2 <-  cowplot::plot_grid(daplot,dxplot,epplot,mtplot, trplot,veplot, legend_b,ncol =7, rel_widths = c(1,1,1,1,1,1,.5))
print(plan2)

Version Author Date
513ca48 reneeisnowhere 2023-07-26
05fe9b1 reneeisnowhere 2023-07-19
e3fe073 reneeisnowhere 2023-07-18
9dd118a reneeisnowhere 2023-07-14
6328422 reneeisnowhere 2023-06-16

Lines are a 4 point log-logistic regression of the mean from two biological replicates for each individual at 8 concentrations for each condition, except(trastuzmab)TRZ, which is 7 concentrations.

You can find the link to initial DRC analysis at thislink

C & D. LD50 and EC50

drug_palc <- c("#8B006D","#DF707E","#F1B72B", "#3386DD","#707031","#41B333")
BC_cell_lines <- read.csv("data/BC_cell_lines.csv",row.names = 1)
LD50_table <- readRDS("data/new_ld50avg.RDS")



graphLD50 <- LD50_table %>%
  mutate(Treatment = factor(Treatment,
                            levels = c('DOX', 'EPI', 'DNR', 'MTX', 'TRZ', 'VEH'))) %>%
  ggplot(., (aes(x = Treatment, y = log10(Estimate)))) +
  geom_boxplot(position = "identity", aes(fill = Treatment)) +
  geom_point(aes(
    color = indv,
    size = 5,
    alpha = 0.5
  )) +
  ggtitle(expression("Experimentally-derived LD"[50] * "s\n from treated cardiomyocytes")) +
  xlab("") +
  geom_signif(
    comparisons = list(
      c("DNR", "MTX"),
      c("DOX", "EPI"),
      c ("DOX", "DNR"),
      c("DOX", "MTX")
    ),
    test = "t.test",
    map_signif_level = TRUE,
    textsize = 4,
    step_increase = 0.15
  ) +
  ylab(bquote('Log'[10] ~ 'LD'[50] ~ 'in ' * mu * M)) +
  scale_color_brewer(palette = "Dark2",
                     name = "Individual") +
  ylim(-2, 3) +
  
  scale_fill_manual(values = drug_palc, name = "Treatment") +
  theme_bw() +
  guides(alpha = "none", size = "none") +
  theme(
    plot.title = element_text(hjust = 0.5, size = 12),
    axis.title = element_text(size = 15, color = "black"),
    axis.ticks = element_line(linewidth = 1.5),
    axis.line = element_line(linewidth = 1.5),
    axis.text.x = element_text(size = 0, color = "white"),
    # axis.text = element_text(size = 12, color = "black", angle = 0),
    strip.text.x = element_text(
      size = 15,
      color = "black",
      face = "bold"
    )
  )   

You can find the links to DRC and BCC analysis at thislink.

graphBC <-
  BC_cell_lines %>%
  mutate(Cell_line = factor(Cell_line)) %>%
  pivot_longer(.,
               col = !Cell_line,
               names_to = 'drug',
               values_to = 'LD50') %>%
  mutate(
    drug = case_match(
      drug,
      "Daunorubicin" ~ "DNR",
      "Doxorubicin" ~ "DOX",
      "Epirubicin" ~ "EPI",
      "Mitoxantrone" ~ "MTX",
      "Trastuzumab" ~ "TRZ",
      "Vehicle" ~ "VEH",
      .default = drug
    )
  ) %>%
  mutate(drug = factor(drug,
                       levels = c('DOX', 'EPI', 'DNR', 'MTX', 'TRZ', 'VEH'))) %>%
  ggplot(., (aes(x = (drug), y = log10(LD50)))) +
  geom_boxplot(position = "identity", aes(fill = drug)) +
  geom_point(aes(
    color = Cell_line,
    size = 5,
    alpha = 0.5
  )) +
  ggtitle(expression("Breast cancer cell line reported  ED"[50] * "s")) +
  xlab("") +
  
  geom_signif(
    comparisons = list(c("DOX", "EPI"),
                       c ("DOX", "DNR"),
                       c("DOX", "MTX")),
    test = "t.test",
    map_signif_level = TRUE,
    textsize = 4,
    step_increase = 0.15
  ) +
  ylab(bquote('Log'[10] ~ 'LD'[50] ~ 'in ' * mu * M)) +
  scale_color_brewer(palette = "Spectral",
                     name = "Cell lines") +
  scale_fill_manual(values = drug_palc) +
  ylim(-2, 3) +
  theme_bw() +
  guides(alpha = "none", size = "none", fill = "none") +
  theme(
    plot.title = element_text(hjust = 0.5, size = 12),
    axis.title = element_text(size = 15, color = "black"),
    axis.ticks = element_line(linewidth = 1.5),
    axis.line = element_line(linewidth = 1.5),
    axis.text.x = element_text(size = 0, color = "white"),
    # axis.text = element_text(size = 12, color = "black", angle = 0),
    strip.text.x = element_text(
      size = 15,
      color = "black",
      face = "bold"
    )
  )
# graphBC
plan50ld <-
  cowplot::plot_grid(graphLD50,
                     NULL,
                     graphBC,
                     rel_widths = c(1, .2, 1),
                     nrow = 1)
print(plan50ld)

Version Author Date
9eb4774 reneeisnowhere 2023-09-27
513ca48 reneeisnowhere 2023-07-26
9dd118a reneeisnowhere 2023-07-14
433a442 reneeisnowhere 2023-06-21
6328422 reneeisnowhere 2023-06-16

You can find the code to EC50 breast cancer cell lines at this link.


sessionInfo()
R version 4.3.1 (2023-06-16 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19045)

Matrix products: default


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] grid      stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] png_0.1-8             ComplexHeatmap_2.16.0 broom_1.0.5          
 [4] RColorBrewer_1.1-3    ggsignif_0.6.4        cowplot_1.1.1        
 [7] drc_3.0-1             MASS_7.3-60           data.table_1.14.8    
[10] BiocGenerics_0.46.0   tinytex_0.48          lubridate_1.9.3      
[13] forcats_1.0.0         stringr_1.5.0         dplyr_1.1.3          
[16] purrr_1.0.2           readr_2.1.4           tidyr_1.3.0          
[19] tibble_3.2.1          ggplot2_3.4.4         tidyverse_2.0.0      
[22] car_3.1-2             carData_3.0-5         workflowr_1.7.1      

loaded via a namespace (and not attached):
 [1] sandwich_3.0-2      rlang_1.1.2         magrittr_2.0.3     
 [4] clue_0.3-65         GetoptLong_1.0.5    git2r_0.32.0       
 [7] multcomp_1.4-25     matrixStats_1.1.0   compiler_4.3.1     
[10] getPass_0.2-2       mgcv_1.9-0          callr_3.7.3        
[13] vctrs_0.6.4         pkgconfig_2.0.3     shape_1.4.6        
[16] crayon_1.5.2        fastmap_1.1.1       backports_1.4.1    
[19] labeling_0.4.3      utf8_1.2.4          promises_1.2.1     
[22] rmarkdown_2.25      tzdb_0.4.0          ps_1.7.5           
[25] xfun_0.41           cachem_1.0.8        jsonlite_1.8.7     
[28] highr_0.10          later_1.3.1         parallel_4.3.1     
[31] cluster_2.1.4       R6_2.5.1            bslib_0.5.1        
[34] stringi_1.7.12      jquerylib_0.1.4     Rcpp_1.0.11        
[37] iterators_1.0.14    knitr_1.45          zoo_1.8-12         
[40] IRanges_2.34.1      httpuv_1.6.12       Matrix_1.6-2       
[43] splines_4.3.1       timechange_0.2.0    tidyselect_1.2.0   
[46] rstudioapi_0.15.0   abind_1.4-5         yaml_2.3.7         
[49] doParallel_1.0.17   codetools_0.2-19    processx_3.8.2     
[52] lattice_0.22-5      withr_2.5.2         evaluate_0.23      
[55] survival_3.5-7      circlize_0.4.15     pillar_1.9.0       
[58] whisker_0.4.1       foreach_1.5.2       stats4_4.3.1       
[61] generics_0.1.3      rprojroot_2.0.4     hms_1.1.3          
[64] S4Vectors_0.38.2    munsell_0.5.0       scales_1.2.1       
[67] gtools_3.9.4        glue_1.6.2          tools_4.3.1        
[70] fs_1.6.3            mvtnorm_1.2-3       plotrix_3.8-3      
[73] colorspace_2.1-0    nlme_3.1-163        cli_3.6.1          
[76] fansi_1.0.5         gtable_0.3.4        sass_0.4.7         
[79] digest_0.6.33       TH.data_1.1-2       farver_2.1.1       
[82] rjson_0.2.21        htmltools_0.5.7     lifecycle_1.0.4    
[85] httr_1.4.7          GlobalOptions_0.1.2