Run_all_analysis
ERM
20230414
Last updated: 2023-04-17
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Knit directory: Cardiotoxicity/
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This starts the documentation of the RNA-seq cardiotoxicity analysis for my manuscript
library(edgeR)#
library(limma)#
library(RColorBrewer)
library(mixOmics)
library(gridExtra)#
library(reshape2)#
library(data.table)
library(AnnotationHub)
library(tidyverse)
library(scales)
library(biomaRt)#
library(Homo.sapiens)
library(cowplot)#
library(ggrepel)#
library(corrplot)
library(Hmisc)
library(ggpubr) ###now we add genenames to the geneid###
geneid <- rownames(mymatrix) ### pulls the names we have in the counts file
genes <- select(Homo.sapiens, keys=geneid, columns=c("SYMBOL"),
keytype="ENTREZID")
genes <- genes[!duplicated(genes$ENTREZID),]
mymatrix$genes <- genes
#saveRDS(mymatrix, "data/allmatrix.RDS")
##note-not filtered!Initial RNA-seq quality checks
Initial histograms from count matrix
[1] 28395 72[1] 14084 72
PCA by treatment and as a whole
### Vehicle 
### Daunorubicin 
### Doxorubicin 
### Epirubicin 
### Mitoxantrone 
### Trastuzumab 
samplenames indv drug time RIN group PC1 PC2
Da.1.3h MCW_RM_R_11 1 Daunorubicin 3h 9.3 1 -18.33154 61.71013
Do.1.3h MCW_RM_R_12 1 Doxorubicin 3h 9.8 2 -12.36280 73.97678
Ep.1.3h MCW_RM_R_13 1 Epirubicin 3h 9.8 3 -11.16205 66.48794
Mi.1.3h MCW_RM_R_14 1 Mitoxantrone 3h 10 4 -10.19948 73.48343
Tr.1.3h MCW_RM_R_15 1 Trastuzumab 3h 9.6 5 -12.17619 80.01454
Ve.1.3h MCW_RM_R_16 1 Vehicle 3h 9.9 6 -14.98226 76.62199
PC3 PC4 PC5 PC6
Da.1.3h 44.039139 -4.547031 24.642107 -35.03245
Do.1.3h 24.576395 -8.626528 -19.908580 -18.97447
Ep.1.3h 33.025628 -9.349549 18.083569 -43.06551
Mi.1.3h 19.016766 -14.639651 -9.065324 -24.29908
Tr.1.3h 2.640624 -17.019296 -34.253925 -11.77881
Ve.1.3h 12.706808 -4.173412 -39.846595 -17.16213
PC1 PC2 PC3 PC4 PC5 PC6
Standard deviation 63.98853 47.11608 34.21502 32.58775 28.22245 23.90977
Proportion of Variance 0.29072 0.15762 0.08312 0.07540 0.05655 0.04059
Cumulative Proportion 0.29072 0.44834 0.53146 0.60687 0.66342 0.70401
PC7
Standard deviation 21.56133
Proportion of Variance 0.03301
Cumulative Proportion 0.73702Typical genes expressed in iPSC-CMS
correlation heatmap of counts matrix
now to get the counts set for DEG!!
DEG analysis
summary
V.DA V.DX V.EP V.MT V.TR V.DA24 V.DX24 V.EP24 V.MT24 V.TR24
Down 109 3 30 24 0 3540 3336 3105 428 0
NotSig 13552 14065 13874 14009 14084 7067 7439 7756 12969 14084
Up 423 16 180 51 0 3477 3309 3223 687 0cormotif analysis More In-depth
written summary so far: pending T values sorting. Response sets look similar to previous results. This data is based on the filtered count matrix (using rowmeans>0 of cpm(log=true)). Classification of patterns appear to be:
motif 1- No Response set: 7362 (gene list made by filtering each column by posterior probability of <0.55)
motif 2- Time-independent Top2Bi response cluster: 432
motif 3- Early Top2Bi response cluster: 481
motif 4- All BC drug response set (only 1!) (I could not really isolate this gene from the p.post data)
motif 5- Late Top2Bi response cluster: 4850
NOTE: these are based on the most recent counts (motif numbers have changed a little)
More analysis on corMotif (aka Baysian gene anaylsis can be found on this page:) CorMotif
Volcano plots from pairwise gene analysis
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] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] ggpubr_0.6.0
[2] Hmisc_4.8-0
[3] Formula_1.2-5
[4] survival_3.5-3
[5] corrplot_0.92
[6] ggrepel_0.9.3
[7] cowplot_1.1.1
[8] Homo.sapiens_1.3.1
[9] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
[10] org.Hs.eg.db_3.15.0
[11] GO.db_3.15.0
[12] OrganismDbi_1.38.1
[13] GenomicFeatures_1.48.4
[14] GenomicRanges_1.48.0
[15] GenomeInfoDb_1.32.4
[16] AnnotationDbi_1.58.0
[17] IRanges_2.30.1
[18] S4Vectors_0.34.0
[19] Biobase_2.56.0
[20] biomaRt_2.52.0
[21] scales_1.2.1
[22] lubridate_1.9.2
[23] forcats_1.0.0
[24] stringr_1.5.0
[25] dplyr_1.1.0
[26] purrr_1.0.1
[27] readr_2.1.4
[28] tidyr_1.3.0
[29] tibble_3.1.8
[30] tidyverse_2.0.0
[31] AnnotationHub_3.4.0
[32] BiocFileCache_2.4.0
[33] dbplyr_2.3.1
[34] BiocGenerics_0.42.0
[35] data.table_1.14.8
[36] reshape2_1.4.4
[37] gridExtra_2.3
[38] mixOmics_6.20.0
[39] ggplot2_3.4.1
[40] lattice_0.20-45
[41] MASS_7.3-58.2
[42] RColorBrewer_1.1-3
[43] edgeR_3.38.4
[44] limma_3.52.4
[45] workflowr_1.7.0
loaded via a namespace (and not attached):
[1] utf8_1.2.3 tidyselect_1.2.0
[3] RSQLite_2.3.0 htmlwidgets_1.6.1
[5] grid_4.2.2 BiocParallel_1.30.4
[7] munsell_0.5.0 codetools_0.2-19
[9] statmod_1.5.0 interp_1.1-3
[11] withr_2.5.0 colorspace_2.1-0
[13] filelock_1.0.2 highr_0.10
[15] knitr_1.42 rstudioapi_0.14
[17] ggsignif_0.6.4 MatrixGenerics_1.8.1
[19] labeling_0.4.2 git2r_0.31.0
[21] GenomeInfoDbData_1.2.8 pheatmap_1.0.12
[23] farver_2.1.1 bit64_4.0.5
[25] rprojroot_2.0.3 vctrs_0.5.2
[27] generics_0.1.3 xfun_0.37
[29] timechange_0.2.0 R6_2.5.1
[31] locfit_1.5-9.7 bitops_1.0-7
[33] cachem_1.0.7 DelayedArray_0.22.0
[35] promises_1.2.0.1 BiocIO_1.6.0
[37] nnet_7.3-18 gtable_0.3.1
[39] processx_3.8.0 rlang_1.0.6
[41] splines_4.2.2 rtracklayer_1.56.1
[43] rstatix_0.7.2 broom_1.0.3
[45] checkmate_2.1.0 BiocManager_1.30.20
[47] yaml_2.3.7 abind_1.4-5
[49] backports_1.4.1 httpuv_1.6.9
[51] RBGL_1.72.0 tools_4.2.2
[53] ellipsis_0.3.2 jquerylib_0.1.4
[55] Rcpp_1.0.10 plyr_1.8.8
[57] base64enc_0.1-3 progress_1.2.2
[59] zlibbioc_1.42.0 RCurl_1.98-1.10
[61] ps_1.7.2 prettyunits_1.1.1
[63] rpart_4.1.19 deldir_1.0-6
[65] SummarizedExperiment_1.26.1 cluster_2.1.4
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[73] mime_0.12 evaluate_0.20
[75] xtable_1.8-4 XML_3.99-0.13
[77] jpeg_0.1-10 compiler_4.2.2
[79] ellipse_0.4.3 crayon_1.5.2
[81] htmltools_0.5.4 corpcor_1.6.10
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