Run_all_analysis
ERM
20230414
Last updated: 2023-04-16
Checks: 7 0
Knit directory: Cardiotoxicity/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 9e88c22 | reneeisnowhere | 2023-04-16 | updated run data |
| Rmd | 6d925a2 | reneeisnowhere | 2023-04-16 | updating cormotif with updated RNAseq counts |
| html | 8d08bd2 | reneeisnowhere | 2023-04-11 | Build site. |
| Rmd | 0aaa63d | reneeisnowhere | 2023-04-11 | cormotif analysis update |
| Rmd | 575fd81 | reneeisnowhere | 2023-04-11 | updating cormotif |
| html | 4cd8ac4 | reneeisnowhere | 2023-04-11 | Build site. |
| html | 08936e7 | reneeisnowhere | 2023-04-10 | Build site. |
| Rmd | fa2cbeb | reneeisnowhere | 2023-04-10 | monday end |
| html | 85526c5 | reneeisnowhere | 2023-04-10 | Build site. |
| Rmd | 1444a85 | reneeisnowhere | 2023-04-10 | update push of new data |
| html | b266b76 | reneeisnowhere | 2023-04-10 | Build site. |
| Rmd | d3f8cf7 | reneeisnowhere | 2023-04-10 | update push of new data |
| html | f0a75e1 | reneeisnowhere | 2023-04-10 | Build site. |
| Rmd | 8ca4c7e | reneeisnowhere | 2023-04-10 | first rmd commit |
| Rmd | 2e69969 | reneeisnowhere | 2023-04-10 | adding data |
| Rmd | 0f1f1da | reneeisnowhere | 2023-04-10 | final run analysis |
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(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 <- genesInitial 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
Importance of components:
PC1 PC2 PC3 PC4 PC5 PC6
Standard deviation 63.9885 47.1161 34.21502 32.5878 28.22245 23.90977
Proportion of Variance 0.2907 0.1576 0.08312 0.0754 0.05655 0.04059
Cumulative Proportion 0.2907 0.4483 0.53146 0.6069 0.66342 0.70401
PC7 PC8 PC9 PC10 PC11 PC12
Standard deviation 21.56133 20.74271 19.52290 17.47711 16.26239 15.49112
Proportion of Variance 0.03301 0.03055 0.02706 0.02169 0.01878 0.01704
Cumulative Proportion 0.73702 0.76757 0.79463 0.81632 0.83510 0.85213
PC13 PC14 PC15 PC16 PC17 PC18
Standard deviation 14.03690 13.01039 11.78935 11.40888 10.15831 9.76192
Proportion of Variance 0.01399 0.01202 0.00987 0.00924 0.00733 0.00677
Cumulative Proportion 0.86612 0.87814 0.88801 0.89725 0.90458 0.91135
PC19 PC20 PC21 PC22 PC23 PC24 PC25
Standard deviation 8.77010 8.66331 8.08961 7.8745 7.45127 7.07877 6.6073
Proportion of Variance 0.00546 0.00533 0.00465 0.0044 0.00394 0.00356 0.0031
Cumulative Proportion 0.91681 0.92214 0.92678 0.9312 0.93513 0.93869 0.9418
PC26 PC27 PC28 PC29 PC30 PC31 PC32
Standard deviation 6.30345 6.04266 5.85249 5.72714 5.52443 5.49059 5.26497
Proportion of Variance 0.00282 0.00259 0.00243 0.00233 0.00217 0.00214 0.00197
Cumulative Proportion 0.94461 0.94720 0.94963 0.95196 0.95413 0.95627 0.95824
PC33 PC34 PC35 PC36 PC37 PC38 PC39
Standard deviation 5.18567 5.07323 4.83532 4.80110 4.65658 4.52528 4.46361
Proportion of Variance 0.00191 0.00183 0.00166 0.00164 0.00154 0.00145 0.00141
Cumulative Proportion 0.96014 0.96197 0.96363 0.96527 0.96681 0.96826 0.96968
PC40 PC41 PC42 PC43 PC44 PC45 PC46
Standard deviation 4.39495 4.30843 4.22660 4.20932 4.12976 4.07652 4.04834
Proportion of Variance 0.00137 0.00132 0.00127 0.00126 0.00121 0.00118 0.00116
Cumulative Proportion 0.97105 0.97237 0.97363 0.97489 0.97610 0.97728 0.97845
PC47 PC48 PC49 PC50 PC51 PC52 PC53
Standard deviation 3.99248 3.89519 3.86841 3.81632 3.80862 3.78710 3.7466
Proportion of Variance 0.00113 0.00108 0.00106 0.00103 0.00103 0.00102 0.0010
Cumulative Proportion 0.97958 0.98066 0.98172 0.98275 0.98378 0.98480 0.9858
PC54 PC55 PC56 PC57 PC58 PC59 PC60
Standard deviation 3.70669 3.65110 3.64619 3.59910 3.5568 3.50638 3.46507
Proportion of Variance 0.00098 0.00095 0.00094 0.00092 0.0009 0.00087 0.00085
Cumulative Proportion 0.98677 0.98772 0.98866 0.98958 0.9905 0.99136 0.99221
PC61 PC62 PC63 PC64 PC65 PC66 PC67
Standard deviation 3.44803 3.40615 3.34532 3.27967 3.22481 3.1318 3.11900
Proportion of Variance 0.00084 0.00082 0.00079 0.00076 0.00074 0.0007 0.00069
Cumulative Proportion 0.99305 0.99388 0.99467 0.99543 0.99617 0.9969 0.99756
PC68 PC69 PC70 PC71 PC72
Standard deviation 3.10497 3.05639 2.88429 2.6597 9.29e-14
Proportion of Variance 0.00068 0.00066 0.00059 0.0005 0.00e+00
Cumulative Proportion 0.99824 0.99891 0.99950 1.0000 1.00e+00Typical 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
[1] 14084 10Response 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: 7490 (gene list made by filtering each column by posterior probability of <0.5) motif 2- Time-independent Top2Bi response cluster: 116
motif 3- Early Top2Bi response cluster: 424
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:1615 **these are based on the most recent plots (motif numbers have changed a little)
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] Cormotif_1.42.0
[2] affy_1.74.0
[3] ggpubr_0.6.0
[4] Hmisc_4.8-0
[5] Formula_1.2-5
[6] survival_3.5-3
[7] corrplot_0.92
[8] ggrepel_0.9.3
[9] cowplot_1.1.1
[10] Homo.sapiens_1.3.1
[11] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
[12] org.Hs.eg.db_3.15.0
[13] GO.db_3.15.0
[14] OrganismDbi_1.38.1
[15] GenomicFeatures_1.48.4
[16] GenomicRanges_1.48.0
[17] GenomeInfoDb_1.32.4
[18] AnnotationDbi_1.58.0
[19] IRanges_2.30.1
[20] S4Vectors_0.34.0
[21] Biobase_2.56.0
[22] biomaRt_2.52.0
[23] scales_1.2.1
[24] lubridate_1.9.2
[25] forcats_1.0.0
[26] stringr_1.5.0
[27] dplyr_1.1.0
[28] purrr_1.0.1
[29] readr_2.1.4
[30] tidyr_1.3.0
[31] tibble_3.1.8
[32] tidyverse_2.0.0
[33] AnnotationHub_3.4.0
[34] BiocFileCache_2.4.0
[35] dbplyr_2.3.1
[36] BiocGenerics_0.42.0
[37] reshape2_1.4.4
[38] gridExtra_2.3
[39] mixOmics_6.20.0
[40] ggplot2_3.4.1
[41] lattice_0.20-45
[42] MASS_7.3-58.2
[43] RColorBrewer_1.1-3
[44] edgeR_3.38.4
[45] limma_3.52.4
[46] 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 preprocessCore_1.58.0
[9] codetools_0.2-19 statmod_1.5.0
[11] interp_1.1-3 withr_2.5.0
[13] colorspace_2.1-0 filelock_1.0.2
[15] highr_0.10 knitr_1.42
[17] rstudioapi_0.14 ggsignif_0.6.4
[19] MatrixGenerics_1.8.1 labeling_0.4.2
[21] git2r_0.31.0 GenomeInfoDbData_1.2.8
[23] pheatmap_1.0.12 farver_2.1.1
[25] bit64_4.0.5 rprojroot_2.0.3
[27] vctrs_0.5.2 generics_0.1.3
[29] xfun_0.37 timechange_0.2.0
[31] R6_2.5.1 locfit_1.5-9.7
[33] bitops_1.0-7 cachem_1.0.7
[35] DelayedArray_0.22.0 promises_1.2.0.1
[37] BiocIO_1.6.0 nnet_7.3-18
[39] gtable_0.3.1 processx_3.8.0
[41] rlang_1.0.6 splines_4.2.2
[43] rtracklayer_1.56.1 rstatix_0.7.2
[45] broom_1.0.3 checkmate_2.1.0
[47] BiocManager_1.30.20 yaml_2.3.7
[49] abind_1.4-5 backports_1.4.1
[51] httpuv_1.6.9 RBGL_1.72.0
[53] tools_4.2.2 affyio_1.66.0
[55] ellipsis_0.3.2 jquerylib_0.1.4
[57] Rcpp_1.0.10 plyr_1.8.8
[59] base64enc_0.1-3 progress_1.2.2
[61] zlibbioc_1.42.0 RCurl_1.98-1.10
[63] ps_1.7.2 prettyunits_1.1.1
[65] rpart_4.1.19 deldir_1.0-6
[67] SummarizedExperiment_1.26.1 cluster_2.1.4
[69] fs_1.6.1 magrittr_2.0.3
[71] data.table_1.14.8 RSpectra_0.16-1
[73] whisker_0.4.1 matrixStats_0.63.0
[75] hms_1.1.2 mime_0.12
[77] evaluate_0.20 xtable_1.8-4
[79] XML_3.99-0.13 jpeg_0.1-10
[81] compiler_4.2.2 ellipse_0.4.3
[83] crayon_1.5.2 htmltools_0.5.4
[85] corpcor_1.6.10 later_1.3.0
[87] tzdb_0.3.0 DBI_1.1.3
[89] rappdirs_0.3.3 Matrix_1.5-3
[91] car_3.1-1 cli_3.6.0
[93] parallel_4.2.2 igraph_1.4.1
[95] pkgconfig_2.0.3 getPass_0.2-2
[97] GenomicAlignments_1.32.1 foreign_0.8-84
[99] xml2_1.3.3 rARPACK_0.11-0
[101] bslib_0.4.2 XVector_0.36.0
[103] callr_3.7.3 digest_0.6.31
[105] graph_1.74.0 Biostrings_2.64.1
[107] rmarkdown_2.20 htmlTable_2.4.1
[109] restfulr_0.0.15 curl_5.0.0
[111] shiny_1.7.4 Rsamtools_2.12.0
[113] rjson_0.2.21 lifecycle_1.0.3
[115] jsonlite_1.8.4 carData_3.0-5
[117] fansi_1.0.4 pillar_1.8.1
[119] KEGGREST_1.36.3 fastmap_1.1.1
[121] httr_1.4.5 interactiveDisplayBase_1.34.0
[123] glue_1.6.2 png_0.1-8
[125] BiocVersion_3.15.2 bit_4.0.5
[127] stringi_1.7.12 sass_0.4.5
[129] blob_1.2.3 latticeExtra_0.6-30
[131] memoise_2.0.1