Figure2
ERM
2023-07-21
Last updated: 2023-07-21
Checks: 7 0
Knit directory: Cardiotoxicity/
This reproducible R Markdown analysis was created with workflowr (version 1.7.0). 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 6ca2710. 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: 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/CADGWASgene_table.csv
Ignored: data/CAD_geneset.RDS
Ignored: data/CALIMA_Data/
Ignored: data/Clamp_Summary.csv
Ignored: data/Cormotif_24_k1-5_raw.RDS
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/Ep24counts.txt
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/LD50_05via.csv
Ignored: data/LDH48hoursdata.csv
Ignored: data/Mt24counts.txt
Ignored: data/NoRespDEG_final.csv
Ignored: data/RINsamplelist.txt
Ignored: data/Seonane2019supp1.txt
Ignored: data/TMMnormed_x.RDS
Ignored: data/TOP2Bi-24hoursGO_analysis.csv
Ignored: data/TR24counts.txt
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/Viabilitylistfull.csv
Ignored: data/allexpressedgenes.txt
Ignored: data/allgenes.txt
Ignored: data/allmatrix.RDS
Ignored: data/allmymatrix.RDS
Ignored: data/annotation_data_frame.RDS
Ignored: data/averageviabilitytable.RDS
Ignored: data/avgLD50.RDS
Ignored: data/avg_LD50.RDS
Ignored: data/backGL.txt
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/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/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/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/plan2plot.png
Ignored: data/raw_counts.csv
Ignored: data/response_cluster24h.csv
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/supp_normLDH48.RDS
Ignored: data/supp_pca_all_anno.RDS
Ignored: data/table3a.omar
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: Doxorubicin_vehicle_3_24.csv
Untracked: Doxtoplist.csv
Untracked: GWAS_list_of_interest.xlsx
Untracked: KEGGpathwaylist.R
Untracked: OmicNavigator_learn.R
Untracked: SigDoxtoplist.csv
Untracked: analysis/DRC_analysist.Rmd
Untracked: analysis/ciFIT.R
Untracked: analysis/enricher.Rmd
Untracked: analysis/export_to_excel.R
Untracked: analysis/untitled1.R
Untracked: code/DRC_plotfigure1.png
Untracked: code/constantcode.R
Untracked: code/cpm_boxplot.R
Untracked: code/extracting_ggplot_data.R
Untracked: code/fig1plot.png
Untracked: code/figurelegeddrc.png
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_probability_genelist.csv
Untracked: individual-legenddark2.png
Untracked: installed_old.rda
Untracked: motif_ER.txt
Untracked: motif_LR.txt
Untracked: motif_NR.txt
Untracked: motif_TI.txt
Untracked: output/DNRvenn.RDS
Untracked: output/DOXvenn.RDS
Untracked: output/EPIvenn.RDS
Untracked: output/Figures/
Untracked: output/MTXvenn.RDS
Untracked: output/Volcanoplot_10
Untracked: output/Volcanoplot_10.RDS
Untracked: output/allfinal_sup10.RDS
Untracked: output/gene_corr_fig9.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/output-old/
Untracked: output/supplementary_motif_list_GO.RDS
Untracked: output/toptablebydrug.RDS
Untracked: output/x_counts.RDS
Untracked: reneebasecode.R
Unstaged changes:
Modified: analysis/DEG-GO_analysis.Rmd
Modified: analysis/DRC_analysis.Rmd
Modified: analysis/Figure3.Rmd
Modified: analysis/Figure5.Rmd
Modified: analysis/Figure9.Rmd
Modified: analysis/Knowles2019.Rmd
Modified: analysis/Supplementary_figures.Rmd
Modified: analysis/other_analysis.Rmd
Modified: analysis/run_all_analysis.Rmd
Modified: analysis/variance_scrip.Rmd
Modified: output/DNRmeSNPs.RDS
Modified: output/DNRreQTLs.RDS
Modified: output/DOXmeSNPs.RDS
Modified: output/DOXreQTLs.RDS
Modified: output/EPImeSNPs.RDS
Modified: output/EPIreQTLs.RDS
Modified: output/GOI_genelist.txt
Modified: output/MTXmeSNPs.RDS
Modified: output/MTXreQTLs.RDS
Modified: output/TNNI_LDH_RNAnormlist.txt
Modified: output/daplot.RDS
Modified: output/dxplot.RDS
Modified: output/epplot.RDS
Modified: output/mtplot.RDS
Modified: output/plan2plot.png
Modified: output/toplistall.csv
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/Figure2.Rmd) and HTML
(docs/Figure2.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 | 6ca2710 | reneeisnowhere | 2023-07-21 | small fixes |
| html | 636c62f | reneeisnowhere | 2023-07-07 | Build site. |
| Rmd | 2440452 | reneeisnowhere | 2023-07-07 | correct Trop I |
| html | b3dc281 | reneeisnowhere | 2023-07-07 | Build site. |
| Rmd | e5cbe6d | reneeisnowhere | 2023-07-07 | update exports |
| html | 32ba3b4 | reneeisnowhere | 2023-07-06 | Build site. |
| Rmd | b94a1e0 | reneeisnowhere | 2023-07-06 | adding fig 2 updates |
| html | 2379232 | reneeisnowhere | 2023-06-27 | Build site. |
| Rmd | 9719ccd | reneeisnowhere | 2023-06-27 | updated order on graphs |
| html | c382ce9 | reneeisnowhere | 2023-06-16 | Build site. |
| Rmd | ac33c8f | reneeisnowhere | 2023-06-16 | adding figure 2 |
| Rmd | 5253904 | reneeisnowhere | 2023-06-16 | adding figure 2 |
library(car)
library(tidyverse)
library(BiocGenerics)
library(data.table)
library(cowplot)
library(ggsignif)
library(RColorBrewer)
library(broom)level_order2 <- c('75','87','77','79','78','71')
drug_palexpand <- c("#41B333","#8B006D","#DF707E","#F1B72B", "#3386DD","#707031","purple3","darkgreen", "darkblue")
#named colors: dark pink,Red,yellow,blue, dark grey, green(green is always control, may need to move pal around)
calcium_data <- readRDS("data/calcium_data.RDS")
clamp_summary <- readRDS("data/clamp_summary.RDS")Figure 2
Calcium dysregulation occurs at sub-lethal concentrations of TOP2i
A. Representative line graph of individual 3
drug_pal_fact <- c("#8B006D","#DF707E","#F1B72B", "#3386DD","#707031","#41B333")
Normalization_And_Set_File <- function(file_path) {
# Read in the data from the file
CALIMA_obj <- read.csv(file_path)
# Normalize the data
ROI_cut <- CALIMA_obj[,2:ncol(CALIMA_obj)]
ROI_cut_rowmeans <- rowMeans(ROI_cut)
Intensity <- (ROI_cut_rowmeans/min(ROI_cut_rowmeans))
Final_ROI <- tibble::as_tibble(cbind(CALIMA_obj[,1], Intensity, ROI_cut))
Final_ROI$Intensity <- Final_ROI$Intensity -1
return(Final_ROI)
}
Plot_Line_df <- function(directory) {
holder <- list()
# List CSV files in the folder that is output from CALIMA
file_list <- list.files(directory, pattern = "*.csv", full.names = TRUE)
file_list <- file_list %>% as.tibble() %>%
mutate(filenames=value) %>%
separate(filenames, c(NA,NA,NA,"file"), sep="/") %>%
separate(file, c("Drug","indv"))
# Loop over all files in directory
for (i in 1:length(file_list$value)) {
normalized_data <- data.frame("indv"=file_list$indv[i], "drug"=file_list$Drug[i])
# Normalize the data from the file
norm_out <- Normalization_And_Set_File(file_list$value[i])
holder[[file_list$Drug[i]]] <- cbind(normalized_data,norm_out[,1:2])
# Return the plot
}
return(holder)
}plot_77 <- Plot_Line_df("data/CALIMA_Data/77-1/")
df_77forplot <- plot_77 %>%
bind_rows() %>%
mutate(drug=factor(drug, levels = c( "DOX",
"EPI",
"DNR",
"MTX",
"TRZ",
"VEH"))) %>%
rename("Xaxis"=`CALIMA_obj[, 1]`)
Line_plot3<-
ggplot(df_77forplot, aes(x=Xaxis, y= Intensity, group=drug))+
geom_line(size=1.5,aes(color=drug))+
# stat_smooth(method = "lm")
xlab("")+
theme_bw()+
guides(color=guide_legend(override.aes = list(size=1)))+
ggtitle("Individual 3")+
scale_x_continuous(expand = c(0, 0))+
scale_color_manual(values=drug_pal_fact,name=" ")+
theme(plot.title = element_text(size=14,hjust = 0.5),
axis.title = element_text(size = 10, color = "black"),
axis.ticks = element_line(linewidth = 1),
legend.position="right",
axis.line = element_line(linewidth = 1),
axis.text = element_text(size = 12, color = "black", angle = 0),
strip.text.x = element_text(size = 12, color = "black", face = "bold"))
Line_plot3 
nlLine_plot3 <- Line_plot3+theme(legend.position = "none")B. Mean amplitude
MA_plot <- clamp_summary %>%
dplyr::select(Drug,Conc,indv,Mean_Amplitude) %>%
ggplot(.,aes(Drug,Mean_Amplitude))+
geom_boxplot(position = "identity", fill= drug_pal_fact)+
geom_point(aes(col=indv, size=2, alpha=0.5))+
guides(size = "none",alpha="none",colour = "none")+
scale_color_brewer(palette = "Dark2", name = "Individual",label=c("2","3","5"))+
geom_signif(comparisons = list(c("VEH","TRZ"),
c("VEH","MTX"),
c( "VEH","DNR"),
c("VEH","EPI"),
c( "VEH","DOX")),
test = "t.test",
map_signif_level = c("***"=0.001, "**"=0.01, "*"=0.05,"ns"=1),
step_increase = 0.08,
textsize = 4)+
ylab( "a.u.")+
xlab(" ")+
ggtitle("Mean amplitude")+
theme_classic()+
theme(plot.title = element_text(siz=12,hjust = 0.5),
axis.title = element_text(size = 12, color = "black"),
axis.ticks = element_line(linewidth = 1.0),
axis.line = element_line(linewidth = 1.0),
axis.text = element_text(size = 12, color = "black", angle = 0),
strip.text.x = element_text(size = 12, color = "black", face = "bold"))
MA_plot
C. Rising slope
RS_plot <- clamp_summary %>%
dplyr::select(Drug,Conc,indv,Rise_Slope) %>%
ggplot(.,aes(Drug,Rise_Slope))+
geom_boxplot(position = "identity", fill= drug_pal_fact)+
geom_boxplot(position = "identity", fill= drug_pal_fact)+
geom_point(aes(col=indv, size=2, alpha=0.5))+
guides(size = "none",alpha="none",colour = "none")+
scale_color_brewer(palette = "Dark2", name = "Individual",label=c("2","3","5"))+
geom_signif(comparisons = list(c("VEH","TRZ"),
c("VEH","MTX"),
c( "VEH","DNR"),
c("VEH","EPI"),
c( "VEH","DOX")),
test = "t.test",
map_signif_level = c("***"=0.001, "**"=0.01, "*"=0.05,"ns"=1),
step_increase = 0.08,
textsize = 4)+
ylab("a.u./ sec")+
xlab(" ")+
theme_classic()+
ggtitle("Rising slope")+
theme_classic()+
theme(plot.title = element_text(siz=12,hjust = 0.5),
axis.title = element_text(size = 12, color = "black"),
axis.ticks = element_line(linewidth = 1.0),
axis.line = element_line(linewidth = 1.0),
axis.text = element_text(size = 12, color = "black", angle = 0),
strip.text.x = element_text(size = 12, color = "black", face = "bold"))
RS_plot
D. Decay slope
Decay_plot <- clamp_summary %>%
dplyr::select(Drug,Conc,indv,Decay_Slope) %>%
ggplot(.,aes(Drug,Decay_Slope))+
geom_boxplot(position = "identity", fill= drug_pal_fact)+
geom_point(aes(col=indv, size=2, alpha=0.5))+
guides(size = "none",alpha="none",colour = "none")+
scale_color_brewer(palette = "Dark2", name = "Individual",label=c("2","3","5"))+
geom_signif(comparisons = list(c("VEH","TRZ"),
c("VEH","MTX"),
c( "VEH","DNR"),
c("VEH","EPI"),
c( "VEH","DOX")),
test = "t.test",
map_signif_level = c("***"=0.001, "**"=0.01, "*"=0.05,"ns"=1),
step_increase = 0.08,
textsize = 4)+
ylab("a.u.")+
xlab(" ")+
theme_classic()+
ggtitle("Decay slope")+
theme_classic()+
theme(plot.title = element_text(siz=12,hjust = 0.5),
axis.title = element_text(size = 12, color = "black"),
axis.ticks = element_line(linewidth = 1.0),
axis.line = element_line(linewidth = 1.0),
axis.text = element_text(size = 12, color = "black", angle = 0),
strip.text.x = element_text(size = 12, color = "black", face = "bold"))
Decay_plot
E. Full-width at half-max
FWHM_plot <- clamp_summary %>%
dplyr::select(Drug,Conc,indv,FWHM) %>%
ggplot(.,aes(Drug,FWHM))+
geom_boxplot(position = "identity", fill= drug_pal_fact)+
geom_point(aes(col=indv, size=2, alpha=0.5))+
guides(size = "none",alpha="none",colour = "none")+
scale_color_brewer(palette = "Dark2", name = "Individual",
label=c("2","3","5"))+
geom_signif(comparisons = list(c("VEH","TRZ"),
c("VEH","MTX"),
c( "VEH","DNR"),
c("VEH","EPI"),
c( "VEH","DOX")),
test = "t.test",
map_signif_level = c("***"=0.001, "**"=0.01, "*"=0.05,"ns"=1),
step_increase = 0.08,
textsize = 4)+
ylab("a.u.")+
xlab(" ")+
theme_classic()+
ggtitle("Full-width at half-max")+
theme_classic()+
theme(plot.title = element_text(siz=12,hjust = 0.5),
axis.title = element_text(size = 12, color = "black"),
axis.ticks = element_line(linewidth = 1.0),
axis.line = element_line(linewidth = 1.0),
axis.text = element_text(size = 12, color = "black", angle = 0),
strip.text.x = element_text(size = 12, color = "black", face = "bold"))
FWHM_plot
F. Contraction rate
BR_plot <- calcium_data %>%
dplyr::select(Drug,Conc,indv,Rate) %>%
mutate(indv=substr(indv,1,2)) %>%
mutate(indv=factor(indv, levels = level_order2)) %>%
mutate(contrl= 0.383) %>%
mutate(norm_rate=Rate/contrl) %>%
filter(Conc==0| Conc==0.5) %>%
ggplot(., aes(x=Drug, y=Rate))+
geom_boxplot(position = "identity", fill= drug_pal_fact)+
geom_point(aes(col=indv, size=2, alpha=0.5))+
guides(size = "none",alpha="none",colour = guide_legend(override.aes = list(alpha= 0.5)))+
# guides(size = "none",alpha="none",colour = "none")+
scale_color_brewer(palette = "Dark2", name = "Individual",label=c("2","3","5"))+
geom_signif(comparisons = list(c("VEH","TRZ"),
c("VEH","MTX"),
c( "VEH","DNR"),
c("VEH","EPI"),
c( "VEH","DOX")),
test = "t.test",
map_signif_level = c("***"=0.001, "**"=0.01, "*"=0.05,"ns"=1),
step_increase = 0.08,
textsize = 4)+
ylab("avg. beats/sec")+
xlab(" ")+
ggtitle("Contraction rate")+
theme_classic()+
theme(plot.title = element_text(siz=12,hjust = 0.5),
axis.title = element_text(size = 12, color = "black"),
axis.ticks = element_line(linewidth = 1.0),
axis.line = element_line(linewidth = 1.0),
axis.text = element_text(size = 12, color = "black", angle = 0),
strip.text.x = element_text(size = 12 ,color = "black", face = "bold"))
BR_plot
BR_plot2 <- BR_plot+theme(legend.position = "none")
legend <- get_legend(BR_plot)G. PCA of Calcium data
k_means <- read.csv("data/K_cluster_kisthree.csv")
PCA_calciumplot <- k_means %>% mutate(Drug =case_match(Drug_Name,
"Dau_0.5"~"DNR",
"Dau_0.5.1" ~"DNR",
"Dau_0.5.2" ~"DNR",
"Dox_0.5"~"DOX",
"Dox_0.5.1" ~"DOX",
"Dox_0.5.2" ~"DOX",
"Epi_0.5"~"EPI",
"Epi_0.5.1"~"EPI",
"Epi_0.5.2"~"EPI",
"Mito_0.5"~"MTX",
"Mito_0.5.1"~"MTX",
"Mito_0.5.2"~"MTX",
"Tras_0.5"~"TRZ",
"Tras_0.5.1"~"TRZ",
"Tras_0.5.2"~"TRZ",
"Control.1"~"VEH",
"Control.2"~"VEH",
"Control"~"VEH",.default = Drug_Name)) %>%
mutate(Class= case_match(Drug,"DOX"~"TOP2i","DNR"~"TOP2i","EPI" ~"TOP2i","MTX" ~"TOP2i", "TRZ"~"not-TOP2i","VEH"~"not-TOP2i",.default = Drug)) %>%
mutate(Drug=factor(Drug, levels = c( "DOX",
"EPI",
"DNR",
"MTX",
"TRZ",
"VEH"))) %>%
ggplot(., aes(x=PC1, y=PC2, col= Drug,shape = factor(Class)))+
geom_point(size = 8)+
scale_shape_manual(values=c(19, 17,15), name= "Class")+
scale_color_manual(values=drug_pal_fact)+
ggtitle(expression("PCA of Ca"^"2+"~"data"))+
theme_bw()+
labs(x = "PC 1 (54 %)",y = "PC 2 (34%)")+
theme(plot.title=element_text(size= 14,hjust = 0.5),
axis.title = element_text(size = 10, color = "black"),
axis.ticks = element_line(size = 1.5),
axis.text = element_text(size = 12, color = "black", angle = 0),
strip.text.x = element_text(size = 15, color = "black", face = "bold"))
PCA_calciumplot 
legend2 <- get_legend(PCA_calciumplot)
PCA2 <- PCA_calciumplot+theme(legend.position = "none")
ggsave("output/Figures/calciumPCA.eps")library(cowplot)
# align all plots
plots <- align_plots(MA_plot, RS_plot,Decay_plot,FWHM_plot,BR_plot2,align='v',axis='l')
##make bottom row
bottom_row <- plot_grid(plots[[1]],plots[[2]],plots[[3]], plots[[4]],plots[[5]],legend,nrow=1, rel_widths = c(1,1,1,1,1,0.2))
# put together
top_row <- plot_grid(nlLine_plot3,legend2,PCA2, rel_widths=c(1,.2,1), nrow=1)
# middle_row <- plot_grid(legend, nrow=1, scale=4)
test <- plot_grid(top_row, bottom_row, ncol=1, rel_heights = c(2,1.1), rel_widths=c(1,1))
test
ggsave("output/Figures/Calciumgroup.eps",width = 8, height =16, units = "in")
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] stats graphics grDevices utils datasets methods base
other attached packages:
[1] broom_1.0.5 RColorBrewer_1.1-3 ggsignif_0.6.4
[4] cowplot_1.1.1 data.table_1.14.8 BiocGenerics_0.46.0
[7] lubridate_1.9.2 forcats_1.0.0 stringr_1.5.0
[10] dplyr_1.1.2 purrr_1.0.1 readr_2.1.4
[13] tidyr_1.3.0 tibble_3.2.1 ggplot2_3.4.2
[16] tidyverse_2.0.0 car_3.1-2 carData_3.0-5
[19] workflowr_1.7.0
loaded via a namespace (and not attached):
[1] gtable_0.3.3 xfun_0.39 bslib_0.5.0 processx_3.8.1
[5] callr_3.7.3 tzdb_0.4.0 vctrs_0.6.3 tools_4.3.1
[9] ps_1.7.5 generics_0.1.3 fansi_1.0.4 highr_0.10
[13] pkgconfig_2.0.3 lifecycle_1.0.3 farver_2.1.1 compiler_4.3.1
[17] git2r_0.32.0 textshaping_0.3.6 munsell_0.5.0 getPass_0.2-2
[21] httpuv_1.6.11 htmltools_0.5.5 sass_0.4.6 yaml_2.3.7
[25] later_1.3.1 pillar_1.9.0 jquerylib_0.1.4 whisker_0.4.1
[29] cachem_1.0.8 abind_1.4-5 tidyselect_1.2.0 digest_0.6.33
[33] stringi_1.7.12 labeling_0.4.2 rprojroot_2.0.3 fastmap_1.1.1
[37] grid_4.3.1 colorspace_2.1-0 cli_3.6.1 magrittr_2.0.3
[41] utf8_1.2.3 withr_2.5.0 scales_1.2.1 promises_1.2.0.1
[45] backports_1.4.1 timechange_0.2.0 rmarkdown_2.23 httr_1.4.6
[49] ragg_1.2.5 hms_1.1.3 evaluate_0.21 knitr_1.43
[53] rlang_1.1.1 Rcpp_1.0.11 glue_1.6.2 rstudioapi_0.15.0
[57] jsonlite_1.8.7 R6_2.5.1 systemfonts_1.0.4 fs_1.6.2