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ERM
2023-10-30
Last updated: 2023-10-30
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Knit directory: Cardiotoxicity/
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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/Knowels_trop_analysis.Rmd)
and HTML (docs/Knowels_trop_analysis.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
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| 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)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))})
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))})
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))})
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))})
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))})
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))})
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))})
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] edgeR_3.42.4 limma_3.56.2 biomaRt_2.56.1
[4] ggVennDiagram_1.2.3 ComplexHeatmap_2.16.0 broom_1.0.5
[7] kableExtra_1.3.4 sjmisc_2.8.9 scales_1.2.1
[10] ggpubr_0.6.0 cowplot_1.1.1 ggsignif_0.6.4
[13] lubridate_1.9.3 forcats_1.0.0 stringr_1.5.0
[16] dplyr_1.1.3 purrr_1.0.2 readr_2.1.4
[19] tidyr_1.3.0 tibble_3.2.1 ggplot2_3.4.4
[22] tidyverse_2.0.0 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] RColorBrewer_1.1-3 rstudioapi_0.15.0 jsonlite_1.8.7
[4] shape_1.4.6 magrittr_2.0.3 magick_2.8.1
[7] farver_2.1.1 rmarkdown_2.25 GlobalOptions_0.1.2
[10] fs_1.6.3 zlibbioc_1.46.0 vctrs_0.6.4
[13] memoise_2.0.1 RCurl_1.98-1.12 rstatix_0.7.2
[16] webshot_0.5.5 htmltools_0.5.6.1 progress_1.2.2
[19] curl_5.1.0 sass_0.4.7 KernSmooth_2.23-22
[22] bslib_0.5.1 htmlwidgets_1.6.2 plotly_4.10.3
[25] cachem_1.0.8 whisker_0.4.1 lifecycle_1.0.3
[28] iterators_1.0.14 pkgconfig_2.0.3 sjlabelled_1.2.0
[31] R6_2.5.1 fastmap_1.1.1 GenomeInfoDbData_1.2.10
[34] clue_0.3-65 digest_0.6.33 colorspace_2.1-0
[37] AnnotationDbi_1.62.2 S4Vectors_0.38.2 ps_1.7.5
[40] rprojroot_2.0.3 crosstalk_1.2.0 RSQLite_2.3.2
[43] labeling_0.4.3 filelock_1.0.2 fansi_1.0.5
[46] timechange_0.2.0 httr_1.4.7 abind_1.4-5
[49] compiler_4.3.1 proxy_0.4-27 bit64_4.0.5
[52] withr_2.5.1 doParallel_1.0.17 backports_1.4.1
[55] carData_3.0-5 DBI_1.1.3 rappdirs_0.3.3
[58] classInt_0.4-10 rjson_0.2.21 units_0.8-4
[61] tools_4.3.1 httpuv_1.6.11 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_1.2.3 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_1.44 git2r_0.32.0 IRanges_2.34.1
[94] svglite_2.1.2 stats4_4.3.1 xfun_0.40
[97] Biobase_2.60.0 matrixStats_1.0.0 stringi_1.7.12
[100] lazyeval_0.2.2 yaml_2.3.7 evaluate_0.22
[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_3.99-0.14
[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_1.1.1 KEGGREST_1.40.1
[127] rvest_1.0.3