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ERM
2023-11-07
Last updated: 2023-11-07
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Knit directory: Cardiotoxicity/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 441f82b | reneeisnowhere | 2023-11-07 | adding more code |
| html | cae46a9 | reneeisnowhere | 2023-11-07 | Build site. |
| html | bd0342c | reneeisnowhere | 2023-11-07 | Build site. |
| Rmd | 453ebe6 | reneeisnowhere | 2023-11-07 | adding code |
| Rmd | d6ecce9 | reneeisnowhere | 2023-11-07 | adding code |
| html | ae9124e | reneeisnowhere | 2023-10-30 | Build site. |
| Rmd | 74c2dc1 | reneeisnowhere | 2023-10-30 | updated |
| Rmd | d970e84 | reneeisnowhere | 2023-10-30 | adding more analysis |
library(tidyverse)
library(ggsignif)
library(cowplot)
library(ggpubr)
library(scales)
library(sjmisc)
library(kableExtra)
library(broom)
library(ComplexHeatmap)
library(ggVennDiagram)
library(biomaRt)
library(limma)
library(edgeR)
library(RColorBrewer)palette_colors_mine <- colorRampPalette(colors = c("green","white","purple","red" ))(90)
scales::show_col(palette_colors_mine)
| Version | Author | Date |
|---|---|---|
| bd0342c | reneeisnowhere | 2023-11-07 |
Here I will attempt to recreate my correlation analysis on the knowles data using their troponin and RNAseq log2cpm.
### genes I want to know about
interest_genes <- read.csv("output/GOI_genelist.txt", row.names = 1)
trop_knowles <- read.csv("output/trop_knowles_fun.csv", row.names = 1)
Knowles_log2cpm <- read.csv("output/Knowles_log2cpm.csv", row.names = 1)
trop0.625 <- trop_knowles %>%
filter(dosage <1)
store <- Knowles_log2cpm %>%
dplyr::select( 'ESGN',ends_with(c('0.625', '0'))) %>%
dplyr::filter(ESGN %in% interest_genes$ensembl_gene_id) %>%
pivot_longer(cols=!ESGN, names_to = "ind", values_to = "counts") %>%
separate(ind,into=c("cell_line","dosage"), sep = ":") %>%
mutate(dosage = as.numeric(dosage)) %>%
full_join(., trop0.625, by=c("cell_line", "dosage")) %>%
group_by(cell_line) %>%
full_join(., interest_genes, by = c("ESGN" = "ensembl_gene_id"))
store %>%
filter(ESGN==interest_genes[1,2])# A tibble: 1 × 8
# Groups: cell_line [1]
ESGN cell_line dosage counts dbgap troponin entrezgene_id hgnc_symbol
<chr> <chr> <dbl> <dbl> <chr> <dbl> <int> <chr>
1 ENSG00000283… <NA> NA NA <NA> NA 1933 EEF1B2 ###new graph stuff
for (gene in interest_genes$ensembl_gene_id){
gene_plot <- store %>%
dplyr::filter(ESGN == gene) %>%
ggplot(., aes(x=troponin, y=counts))+
geom_point(aes(col=cell_line))+
geom_smooth(method="lm")+
facet_wrap(hgnc_symbol~dosage, scales="free")+
theme_classic()+
xlab("troponin I expression") +
ylab("Gene counts in log2 cpm") +
ggtitle(expression(paste("Correlation between counts and troponin I Knowles")))+
scale_color_manual(values = palette_colors_mine, aesthetics = c("color", "fill"))+
ggpubr:: stat_cor(method="spearman",
# cor.coef.name="rho",
aes(label = paste(..r.label.., ..p.label.., sep = "~`,\n`~")),
color = "red",
label.x.npc = 0.01,
label.y.npc=0.01,
size = 3)+
theme(plot.title = element_text(size = rel(1.5), hjust = 0.5,face = "bold"),
axis.title = element_text(size = 15, color = "black"),
axis.ticks = element_line(size = 1.5),
axis.text = element_text(size = 8, color = "black", angle = 20),
strip.text.x = element_text(size = 12, color = "black", face = "italic"))
print(gene_plot)
}
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| bd0342c | reneeisnowhere | 2023-11-07 |
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| bd0342c | reneeisnowhere | 2023-11-07 |
RNA-seq trial analysis
Analysis of expressed genes
RNA_seq_trial<- readRDS("data/RNA_seq_trial.RDS")
all_cpmcount <- read_table("data/Counts_RNA_ERMatthews.txt")
cpm_count_main <- readRDS("data/cpmcount.RDS") %>% rownames_to_column(var = "ENTREZID")
colnames(cpm_count_main) <- colnames(all_cpmcount)
test_run_sample_list <- read.csv("data/test_run_sample_list.txt", row.names = 1)
colnames(RNA_seq_trial) <- c("ENTREZID",test_run_sample_list$Sample_ID)
lcpm_trial <- RNA_seq_trial %>%
column_to_rownames("ENTREZID") %>%
cpm(., log=TRUE) %>%
as.data.frame() #%>%
row_means <- rowMeans(lcpm_trial)
x_trial <- lcpm_trial[row_means > 0,]
dim(x_trial)[1] 13277 4list_genes_trial <- rownames(x_trial)
ggVennDiagram::ggVennDiagram(list(list_genes_trial, cpm_count_main$ENTREZID),
category.names = c("Trialgenes","Maingenes"),
show_intersect = TRUE,
set_color = "black",
label = "count",
label_percent_digit = 1,
label_size = 4,
label_alpha = 0,
label_color = "black",
edge_lty = "solid", set_size = 4.5)#+Correlation of counts files
lcpm_trial_full <- RNA_seq_trial %>%
column_to_rownames("ENTREZID") %>%
cpm(., log=TRUE) %>%
as.data.frame() %>%
rownames_to_column(var = "ENTREZID")
lcpm_trial_full %>%
column_to_rownames(var="ENTREZID") %>%
cor(.) %>%
Heatmap(.,layer_fun = function(j, i, x, y, width, height, fill) {
grid.text(sprintf("%.3f", pindex(., i, j)), x, y,
gp = gpar(fontsize = 10))})
| Version | Author | Date |
|---|---|---|
| ae9124e | reneeisnowhere | 2023-10-30 |
lcpm_main <- all_cpmcount %>%
column_to_rownames("ENTREZID") %>%
cpm(., log=TRUE) %>%
as.data.frame() %>%
rownames_to_column(var = "ENTREZID") %>%
dplyr::select(ENTREZID, all_of(starts_with("DOX"))) %>%
dplyr::select(ENTREZID, all_of(ends_with("3h")))
combined_data <- lcpm_main %>%
full_join(., lcpm_trial_full, by= "ENTREZID") %>%
column_to_rownames("ENTREZID") %>%
cor(.,)
Heatmap(combined_data,column_title = "Full gene list",
layer_fun = function(j, i, x, y, width, height, fill) {
grid.text(sprintf("%.3f", pindex(combined_data, i, j)), x, y,
gp = gpar(fontsize = 10))})
| Version | Author | Date |
|---|---|---|
| ae9124e | reneeisnowhere | 2023-10-30 |
only79_ind <- lcpm_main %>%
full_join(., lcpm_trial_full, by= "ENTREZID") %>%
dplyr::select(ENTREZID,'3hr_0.5',"DOX.4.3h") %>%
column_to_rownames("ENTREZID") %>%
cor(.,)
Heatmap(only79_ind,column_title = "Full gene list",
layer_fun = function(j, i, x, y, width, height, fill) {
grid.text(sprintf("%.3f", pindex(only79_ind, i, j)), x, y,
gp = gpar(fontsize = 10))})
| Version | Author | Date |
|---|---|---|
| ae9124e | reneeisnowhere | 2023-10-30 |
lcpm_main_veh <- all_cpmcount %>%
column_to_rownames("ENTREZID") %>%
cpm(., log=TRUE) %>%
as.data.frame() %>%
rownames_to_column(var = "ENTREZID") %>%
dplyr::select(ENTREZID, all_of(c(starts_with("DOX"),starts_with("VEH")))) %>%
dplyr::select(ENTREZID, all_of(ends_with("3h")))
combined_data_veh<- lcpm_main_veh %>%
full_join(., lcpm_trial_full, by= "ENTREZID") %>%
column_to_rownames("ENTREZID") %>%
cor(.,)
Heatmap(combined_data_veh, column_title = "all genes in list, no filtering",
layer_fun = function(j, i, x, y, width, height, fill) {
grid.text(sprintf("%.3f", pindex(combined_data_veh, i, j)), x, y,
gp = gpar(fontsize = 8))})
| Version | Author | Date |
|---|---|---|
| ae9124e | reneeisnowhere | 2023-10-30 |
lcpm_trial_filter_main <- lcpm_trial_full %>%
filter(ENTREZID %in% cpm_count_main$ENTREZID)
lcpm_trial_filter_main %>%
column_to_rownames(var="ENTREZID") %>%
cor(.) %>%
Heatmap(.,column_title = "Using 14,084 expressed genes from Main data",
layer_fun = function(j, i, x, y, width, height, fill) {
grid.text(sprintf("%.3f", pindex(., i, j)), x, y,
gp = gpar(fontsize = 8))})
| Version | Author | Date |
|---|---|---|
| ae9124e | reneeisnowhere | 2023-10-30 |
lcpm_trial_filter <- lcpm_trial_full %>%
filter(ENTREZID %in% list_genes_trial)
lcpm_trial_filter %>%
column_to_rownames(var="ENTREZID") %>%
cor(.) %>%
Heatmap(.,column_title = "Using 13277 expressed genes",
layer_fun = function(j, i, x, y, width, height, fill) {
grid.text(sprintf("%.3f", pindex(., i, j)), x, y,
gp = gpar(fontsize = 8))})
| Version | Author | Date |
|---|---|---|
| ae9124e | reneeisnowhere | 2023-10-30 |
lcpm_main_filter_trial <- lcpm_main_veh %>%
filter(ENTREZID %in% list_genes_trial)
lcpm_trial_filter %>%
full_join(., lcpm_main_filter_trial, by = "ENTREZID") %>%
column_to_rownames(var="ENTREZID") %>%
cor(.) %>%
Heatmap(.,column_title = "Using 13277 expressed genes",
layer_fun = function(j, i, x, y, width, height, fill) {
grid.text(sprintf("%.3f", pindex(., i, j)), x, y,
gp = gpar(fontsize = 8))})
| Version | Author | Date |
|---|---|---|
| ae9124e | reneeisnowhere | 2023-10-30 |
lcpm_trial_filter_main %>%
left_join(., lcpm_main, by = "ENTREZID") %>%
column_to_rownames(var="ENTREZID") %>%
dplyr::select(DOX.4.3h,starts_with(("3hr")))%>%
cor(.) %>%
Heatmap(.,column_title = "Using 14084 expressed genes, just 79-1",
layer_fun = function(j, i, x, y, width, height, fill) {
grid.text(sprintf("%.3f", pindex(., i, j)), x, y,
gp = gpar(fontsize = 8))})
| Version | Author | Date |
|---|---|---|
| bd0342c | reneeisnowhere | 2023-11-07 |
hr3_indv4 <- lcpm_trial_filter_main %>%
left_join(., lcpm_main, by = "ENTREZID") %>%
column_to_rownames(var="ENTREZID") %>%
dplyr::select(DOX.4.3h,`3hr_0.5`,`3hr_0.0`)%>%
cor(.) %>%
Heatmap(.,column_title = "Using 14084 expressed genes, just 79-1",
layer_fun = function(j, i, x, y, width, height, fill) {
grid.text(sprintf("%.3f", pindex(., i, j)), x, y,
gp = gpar(fontsize = 8))})
plot(hr3_indv4)
| Version | Author | Date |
|---|---|---|
| bd0342c | reneeisnowhere | 2023-11-07 |
barplots
GOI_genelist <- read.csv("output/GOI_genelist.txt")
cpm_boxplot_trial <-function(lcpm_trial, GOI, ylab) {
##GOI needs to be ENTREZID
df_plot <- lcpm_trial %>%
dplyr::filter(rownames(.)== GOI) %>%
pivot_longer(everything(),
names_to = "treatment",
values_to = "counts") %>%
separate(treatment, c("time","conc"), sep= "_") %>%
mutate(conc = factor(conc,levels=c('0.0','0.1','0.5','1.0'), labels = c ("NT", "0.1 uM", "0.5 uM", "1.0 uM")))
plota <- ggplot2::ggplot(df_plot, aes(x=conc, y= counts))+
geom_col(position="identity")+
theme_bw()+
ylab(ylab)+
xlab("")+
ggtitle(paste(GOI))+
theme(
# strip.background = element_rect(fill = "white",linetype=1, linewidth = 0.5),
plot.title = element_text(size=12,hjust = 0.5,face="bold"),
axis.title = element_text(size = 10, color = "black"),
axis.ticks = element_line(linewidth = 1.0),
panel.background = element_rect(colour = "black", size=1),
# axis.text.x = element_blank(),
strip.text.x = element_text(margin = margin(2,0,2,0, "pt"),face = "bold"))
print(plota)
}
for (g in seq(1:11)){
datafilter <- GOI_genelist
a <- GOI_genelist[g,3]
# b <- datafilter[g,1]
cpm_boxplot_trial(lcpm_trial,GOI=datafilter[g,1],
ylab =bquote(~italic(.(a))~log[2]~"cpm "))
} 
expression of trial RNA seq data
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] RColorBrewer_1.1-3 edgeR_3.42.4 limma_3.56.2
[4] biomaRt_2.56.1 ggVennDiagram_1.2.3 ComplexHeatmap_2.16.0
[7] broom_1.0.5 kableExtra_1.3.4 sjmisc_2.8.9
[10] scales_1.2.1 ggpubr_0.6.0 cowplot_1.1.1
[13] ggsignif_0.6.4 lubridate_1.9.3 forcats_1.0.0
[16] stringr_1.5.0 dplyr_1.1.3 purrr_1.0.2
[19] readr_2.1.4 tidyr_1.3.0 tibble_3.2.1
[22] ggplot2_3.4.4 tidyverse_2.0.0 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] rstudioapi_0.15.0 jsonlite_1.8.7 shape_1.4.6
[4] magrittr_2.0.3 magick_2.8.1 farver_2.1.1
[7] rmarkdown_2.25 GlobalOptions_0.1.2 fs_1.6.3
[10] zlibbioc_1.46.0 vctrs_0.6.4 memoise_2.0.1
[13] RCurl_1.98-1.13 rstatix_0.7.2 webshot_0.5.5
[16] htmltools_0.5.7 progress_1.2.2 curl_5.1.0
[19] sass_0.4.7 KernSmooth_2.23-22 bslib_0.5.1
[22] htmlwidgets_1.6.2 plotly_4.10.3 cachem_1.0.8
[25] whisker_0.4.1 lifecycle_1.0.3 iterators_1.0.14
[28] pkgconfig_2.0.3 sjlabelled_1.2.0 R6_2.5.1
[31] fastmap_1.1.1 GenomeInfoDbData_1.2.10 clue_0.3-65
[34] digest_0.6.33 colorspace_2.1-0 AnnotationDbi_1.62.2
[37] S4Vectors_0.38.2 ps_1.7.5 rprojroot_2.0.4
[40] crosstalk_1.2.0 RSQLite_2.3.3 labeling_0.4.3
[43] filelock_1.0.2 fansi_1.0.5 timechange_0.2.0
[46] httr_1.4.7 abind_1.4-5 compiler_4.3.1
[49] proxy_0.4-27 bit64_4.0.5 withr_2.5.2
[52] doParallel_1.0.17 backports_1.4.1 carData_3.0-5
[55] DBI_1.1.3 highr_0.10 rappdirs_0.3.3
[58] classInt_0.4-10 rjson_0.2.21 units_0.8-4
[61] tools_4.3.1 httpuv_1.6.12 glue_1.6.2
[64] callr_3.7.3 promises_1.2.1 sf_1.0-14
[67] getPass_0.2-2 cluster_2.1.4 generics_0.1.3
[70] gtable_0.3.4 tzdb_0.4.0 class_7.3-22
[73] data.table_1.14.8 hms_1.1.3 xml2_1.3.5
[76] car_3.1-2 utf8_1.2.4 XVector_0.40.0
[79] BiocGenerics_0.46.0 foreach_1.5.2 pillar_1.9.0
[82] yulab.utils_0.1.0 later_1.3.1 circlize_0.4.15
[85] lattice_0.22-5 BiocFileCache_2.8.0 bit_4.0.5
[88] tidyselect_1.2.0 locfit_1.5-9.8 Biostrings_2.68.1
[91] knitr_1.45 git2r_0.32.0 IRanges_2.34.1
[94] svglite_2.1.2 stats4_4.3.1 xfun_0.41
[97] Biobase_2.60.0 matrixStats_1.0.0 stringi_1.7.12
[100] lazyeval_0.2.2 yaml_2.3.7 evaluate_0.23
[103] codetools_0.2-19 RVenn_1.1.0 cli_3.6.1
[106] systemfonts_1.0.5 munsell_0.5.0 processx_3.8.2
[109] jquerylib_0.1.4 Rcpp_1.0.11 GenomeInfoDb_1.36.4
[112] dbplyr_2.4.0 png_0.1-8 XML_3.99-0.15
[115] parallel_4.3.1 ellipsis_0.3.2 blob_1.2.4
[118] prettyunits_1.2.0 bitops_1.0-7 viridisLite_0.4.2
[121] e1071_1.7-13 insight_0.19.6 crayon_1.5.2
[124] GetoptLong_1.0.5 rlang_1.1.2 KEGGREST_1.40.1
[127] rvest_1.0.3