GO analysis
ERM
2023-01-23
Last updated: 2023-01-23
Checks: 7 0
Knit directory: Cardiotoxicity/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | dc890ec | reneeisnowhere | 2023-01-23 | Updating the source dir |
| Rmd | a78eab0 | reneeisnowhere | 2023-01-23 | updated Go analysis |
| Rmd | d9edc06 | reneeisnowhere | 2023-01-20 | try a little update later |
| Rmd | b7eac76 | reneeisnowhere | 2023-01-20 | updating GO |
| html | 6c21cf8 | reneeisnowhere | 2023-01-20 | Build site. |
| Rmd | 443a9f3 | reneeisnowhere | 2023-01-20 | adding in GO analysis |
GO Analysis
I have created several files from the RNA analysis that contain the significant genes(determined by adj.P.val < 0.1) from each Time and Condition. The names of the files are in the following format: ‘sigV’+Drug(2 letters)+time.
example: ‘sigVDA3.txt’ means this file contains the significant DE genes from the Daunorubicin 3 hour compared to Vehicle Control 3 hour analysis
library(gprofiler2)
library(tidyverse)
library(readr)
library(BiocGenerics)
library(gridExtra)The analysis is based on all genes that passed the rowMeans>0 from the previous page[] (https://reneeisnowhere.github.io/Cardiotoxicity/RNAseqrun_1_analysis.html)
a note of working dir for me, remember that rmd files render from its working dir, therefore relative files need to do the /.. to get to the right relative dir.
I first looked at the data setting ‘measure_underrepresentation = TRUE’ in this interactive plot with all genes from the sigDA3 dataset
then I looked at the same data with ‘measure_underrepresentation =
FALSE’
Analysis of Up versus Down
I then separated the VDA3 file by log2 Fold Change to see how the gene sets are enriched.
p2_down <- gostplot(gp_down, capped = FALSE, interactive = TRUE)
p2_down #+ ggtitle("Duanorubicin down regulated gene enrichment at 3 hours")Graphing specific gene expression
apple <- c('CDKN1A',"BBC3", "MDM2", "BCL2", "BAX", "GPX1", 'MFN2')#,"HAS3",'CYBA','TOP2B', 'TP53', 'ABCC1', 'ABCC5', 'RRAGD', 'DUSP13', 'NDUFAF1', 'TDP2', 'TXNIP','BRCA1', 'CTCF','RAD21','RYR2')
##find the index number for each gene
indices <- match(apple, x$genes$SYMBOL)
###subset the matrix
entreset <- x$genes$ENTREZID[indices]
gnames <- cbind(entreset,apple)
colnames(gnames) <- c("ENTREZID","SYMBOL" )
## Now to investigate specific gene expression and analyse similar vs difference of genes up and down between drug "classes" (anthracycline vs non-anthracycline).
The nextstep is to make plots of all the genes you want to see. I am stopping here, because I would like to add
for (gn in indices){
print(Da24counts %>% filter(Tags == gn) %>% ggplot(aes(x=as.factor(Samples), y=Freq))+
geom_boxplot(aes(fill = as.factor(Samples)))+
theme_cowplot(font_size =12,)+
ggtitle(paste0(gnames$gnames," expression in Daunorubicin at 24 hours"))+
labs(y = "Log2(cpm)",x= "", fill = "Treatment"))
}
`{r}
plot_function <- function(index, df) {
for (i in index) {
data <- df %>% filter(ENTREZID == i)
if (!nrow(data)) next
print(data %>% ggplot(aes(x=as.factor(Samples), y=Counts))+
geom_boxplot(aes(fill = as.factor(Samples)))+
theme_cowplot(font_size =12,)+
ggtitle(paste0(i," expression "))+
labs(y = "Log2(cpm)",x= "", fill = "Treatment"))
}
}
apple <- as.data.frame(sigVDA24$ENTREZID)
plot_function(apple,Dx3counts)+theme(axis.text.x = element_text(size= 8,angle = 90))+scale_x_discrete(labels=ENTREZID)
sessionInfo()R version 4.2.2 (2022-10-31 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19044)
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
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] gridExtra_2.3 BiocGenerics_0.42.0 forcats_0.5.2
[4] stringr_1.5.0 dplyr_1.0.10 purrr_1.0.1
[7] readr_2.1.3 tidyr_1.2.1 tibble_3.1.8
[10] ggplot2_3.4.0 tidyverse_1.3.2 gprofiler2_0.2.1
[13] workflowr_1.7.0
loaded via a namespace (and not attached):
[1] bitops_1.0-7 fs_1.5.2 lubridate_1.9.0
[4] bit64_4.0.5 httr_1.4.4 rprojroot_2.0.3
[7] tools_4.2.2 backports_1.4.1 bslib_0.4.2
[10] utf8_1.2.2 R6_2.5.1 DBI_1.1.3
[13] lazyeval_0.2.2 colorspace_2.0-3 withr_2.5.0
[16] tidyselect_1.2.0 processx_3.8.0 bit_4.0.5
[19] compiler_4.2.2 git2r_0.30.1 cli_3.6.0
[22] rvest_1.0.3 xml2_1.3.3 plotly_4.10.1
[25] labeling_0.4.2 sass_0.4.4 scales_1.2.1
[28] callr_3.7.3 digest_0.6.31 rmarkdown_2.20
[31] pkgconfig_2.0.3 htmltools_0.5.4 highr_0.10
[34] dbplyr_2.3.0 fastmap_1.1.0 htmlwidgets_1.6.1
[37] rlang_1.0.6 readxl_1.4.1 rstudioapi_0.14
[40] shiny_1.7.4 jquerylib_0.1.4 generics_0.1.3
[43] jsonlite_1.8.4 crosstalk_1.2.0 vroom_1.6.0
[46] googlesheets4_1.0.1 RCurl_1.98-1.9 magrittr_2.0.3
[49] Rcpp_1.0.9 munsell_0.5.0 fansi_1.0.3
[52] lifecycle_1.0.3 stringi_1.7.12 whisker_0.4.1
[55] yaml_2.3.6 grid_4.2.2 parallel_4.2.2
[58] promises_1.2.0.1 crayon_1.5.2 haven_2.5.1
[61] hms_1.1.2 knitr_1.41 ps_1.7.2
[64] pillar_1.8.1 reprex_2.0.2 glue_1.6.2
[67] evaluate_0.20 getPass_0.2-2 data.table_1.14.6
[70] modelr_0.1.10 vctrs_0.5.1 tzdb_0.3.0
[73] httpuv_1.6.8 cellranger_1.1.0 gtable_0.3.1
[76] assertthat_0.2.1 cachem_1.0.6 xfun_0.36
[79] mime_0.12 xtable_1.8-4 broom_1.0.2
[82] later_1.3.0 googledrive_2.0.0 viridisLite_0.4.1
[85] gargle_1.2.1 timechange_0.2.0 ellipsis_0.3.2