Last updated: 2023-05-31

Checks: 7 0

Knit directory: Cardiotoxicity/

This reproducible R Markdown analysis was created with workflowr (version 1.7.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.


Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

The command set.seed(20230109) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version 07a6e06. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:


Ignored files:
    Ignored:    .RData
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/
    Ignored:    data/41588_2018_171_MOESM3_ESMeQTL_ST2_for paper.csv
    Ignored:    data/Arr_GWAS.txt
    Ignored:    data/BC_cell_lines.csv
    Ignored:    data/CADGWASgene_table.csv
    Ignored:    data/Clamp_Summary.csv
    Ignored:    data/Cormotif_24_k1-5_raw.RDS
    Ignored:    data/DAgostres24.RDS
    Ignored:    data/DAtable1.csv
    Ignored:    data/DDEMresp_list.csv
    Ignored:    data/DDE_reQTL.txt
    Ignored:    data/DDEresp_list.csv
    Ignored:    data/DEG-GO/
    Ignored:    data/DEG_cormotif.RDS
    Ignored:    data/DF_Plate_Peak.csv
    Ignored:    data/Da24counts.txt
    Ignored:    data/Dx24counts.txt
    Ignored:    data/Dx_reQTL_specific.txt
    Ignored:    data/Ep24counts.txt
    Ignored:    data/GOplots.R
    Ignored:    data/GTEX_setsimple.csv
    Ignored:    data/HFGWASgene_table.csv
    Ignored:    data/Heart_Left_Ventricle.v8.egenes.txt
    Ignored:    data/Hf_GWAS.txt
    Ignored:    data/K_cluster
    Ignored:    data/K_cluster_kisthree.csv
    Ignored:    data/K_cluster_kistwo.csv
    Ignored:    data/LDH48hoursdata.csv
    Ignored:    data/Mt24counts.txt
    Ignored:    data/RINsamplelist.txt
    Ignored:    data/Seonane2019supp1.txt
    Ignored:    data/TOP2Bi-24hoursGO_analysis.csv
    Ignored:    data/TR24counts.txt
    Ignored:    data/Top2biresp_cluster24h.csv
    Ignored:    data/Viabilitylistfull.csv
    Ignored:    data/allexpressedgenes.txt
    Ignored:    data/allgenes.txt
    Ignored:    data/allmatrix.RDS
    Ignored:    data/avgLD50.RDS
    Ignored:    data/backGL.txt
    Ignored:    data/cormotif_3hk1-8.RDS
    Ignored:    data/cormotif_initalK5.RDS
    Ignored:    data/cormotif_initialK5.RDS
    Ignored:    data/cormotif_initialall.RDS
    Ignored:    data/counts24hours.RDS
    Ignored:    data/cpmnorm_counts.csv
    Ignored:    data/cvd_GWAS.txt
    Ignored:    data/dat_cpm.RDS
    Ignored:    data/data_outline.txt
    Ignored:    data/efit2.RDS
    Ignored:    data/efit2results.RDS
    Ignored:    data/ensembl_backup.RDS
    Ignored:    data/ensgtotal.txt
    Ignored:    data/filenameonly.txt
    Ignored:    data/filtered_cpm_counts.csv
    Ignored:    data/filtered_raw_counts.csv
    Ignored:    data/filtermatrix_x.RDS
    Ignored:    data/folder_05top/
    Ignored:    data/gene_prob_tran3h.RDS
    Ignored:    data/gene_probabilityk5.RDS
    Ignored:    data/gostresTop2bi_ER.RDS
    Ignored:    data/gostresTop2bi_LR
    Ignored:    data/gostresTop2bi_LR.RDS
    Ignored:    data/gostresTop2bi_TI.RDS
    Ignored:    data/gostrescoNR
    Ignored:    data/gtex/
    Ignored:    data/heartgenes.csv
    Ignored:    data/individualDRCfile.RDS
    Ignored:    data/individual_LDH48.RDS
    Ignored:    data/knowfig4.csv
    Ignored:    data/knowfig5.csv
    Ignored:    data/knowles56.GMT
    Ignored:    data/knowlesGMT.GMT
    Ignored:    data/mymatrix.RDS
    Ignored:    data/nonresponse_cluster24h.csv
    Ignored:    data/norm_LDH.csv
    Ignored:    data/norm_counts.csv
    Ignored:    data/old_sets/
    Ignored:    data/plan2plot.png
    Ignored:    data/raw_counts.csv
    Ignored:    data/response_cluster24h.csv
    Ignored:    data/sigVDA24.txt
    Ignored:    data/sigVDA3.txt
    Ignored:    data/sigVDX24.txt
    Ignored:    data/sigVDX3.txt
    Ignored:    data/sigVEP24.txt
    Ignored:    data/sigVEP3.txt
    Ignored:    data/sigVMT24.txt
    Ignored:    data/sigVMT3.txt
    Ignored:    data/sigVTR24.txt
    Ignored:    data/sigVTR3.txt
    Ignored:    data/siglist.RDS
    Ignored:    data/table3a.omar
    Ignored:    data/toplistall.RDS
    Ignored:    data/tvl24hour.txt
    Ignored:    data/tvl24hourw.txt
    Ignored:    data/venn_code.R

Untracked files:
    Untracked:  .RDataTmp
    Untracked:  .RDataTmp1
    Untracked:  .RDataTmp2
    Untracked:  cormotif_probability_genelist.csv
    Untracked:  individual-legenddark2.png
    Untracked:  installed_old.rda
    Untracked:  motif_ER.txt
    Untracked:  motif_LR.txt
    Untracked:  motif_NR.txt
    Untracked:  motif_TI.txt
    Untracked:  output/Sup4seoane.csv
    Untracked:  output/figure_1.Rmd
    Untracked:  output/output-old/
    Untracked:  output/plan2plot.png
    Untracked:  output/plan48ldh.png
    Untracked:  reneebasecode.R

Unstaged changes:
    Modified:   analysis/DRC_analysis.Rmd
    Modified:   code/Compare_tnni_ldh.R
    Modified:   code/sequencing_info_collection.R

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/other_analysis.Rmd) and HTML (docs/other_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 were in that past version.

File Version Author Date Message
Rmd 07a6e06 reneeisnowhere 2023-05-31 adding in more data including Cormotif enrichment numbers
html 6fd877b reneeisnowhere 2023-05-31 Build site.
Rmd b2ba055 reneeisnowhere 2023-05-31 adding Seoane data with cormotif things
html 4c0812e reneeisnowhere 2023-05-26 Build site.
Rmd c7e0fcc reneeisnowhere 2023-05-26 adding in Gtex and chisquare values
html e1bcef0 reneeisnowhere 2023-05-26 Build site.
Rmd 0f512c3 reneeisnowhere 2023-05-26 adding in Gtex and chisquare values
Rmd 1f8c483 reneeisnowhere 2023-05-26 updating code with gtex and chisq
Rmd 25d32da reneeisnowhere 2023-05-26 Adding 3 hour and chisq test to populations
html 5610749 reneeisnowhere 2023-05-22 Build site.
Rmd 889832a reneeisnowhere 2023-05-22 add Seoane data again
html 36cbdab reneeisnowhere 2023-05-22 Build site.
Rmd de54fd5 reneeisnowhere 2023-05-22 add Seoane data
html 7243a18 reneeisnowhere 2023-05-22 Build site.
Rmd e2b3215 reneeisnowhere 2023-05-22 add Seoane data
html c3481d8 reneeisnowhere 2023-05-22 Build site.
Rmd acbd0a8 reneeisnowhere 2023-05-22 updates on GWAS enrichment
Rmd e8c82ec reneeisnowhere 2023-05-18 adding other_analysis and genes of interest log2cpm

Data set comparison order:

Knowles data: meQTLS(aka K4 supp) meQTLS(aka K4 supp)

#Pairwise Data enrichment ## Knowles Comparison data:

Determining the genetic basis of anthracycline-cardiotoxicity by molecular response QTL mapping in induced cardiomyocytes David A Knowles, Courtney K Burrows†, John D Blischak, Kristen M Patterson, Daniel J Serie, Nadine Norton, Carole Ober, Jonathan K Pritchard, Yoav Gilad

Knowles \(~~et~ al.~\) eLife 2018;7:e33480. DOI: https://doi.org/10.7554/eLife.33480 My first question was about transcription response at the 24 hour mark with my treatments. 3 hour RNA-seq had low levels of DEGs,so my focus is at 24 hours. This also happens to be when the Knowles paper collected their RNA-seq data

Supplementary 4 contains a list of 518 SNPs within 1 Mb of TSS, which had a detectable marginal effect on expression (5% FDR). When converted from ensembl gene id to entrez gene id, my list of unique Entrezgeneids = 521. I will call these meSNPs for marginal effect snps. In the meSNPs, 503 are within my DEG of 14084. Using an adj. P value of 0.05, There are 199/6864 in 24 hour daunorubicin, 184/6516 in 24 hour doxorubicin, 182/6202 in 24 hour epirubicin, 30/1327 in 24 hour mitoxantrone and 0 in Trastuzumb

Supplementary 5 contains a list of 376 response eQTLs (reQTLs). These are variants that were associated with modulation of transcriptomic response to doxorubicin treatment. After database name conversion, I have 377 unique Entregene ids. Of the reQTLs list, 374 are within my DEG of 14084. Using an adj. P value of 0.05, There are 187/6864 in 24 hour daunorubicin, 180/6516 in 24 hour doxorubicin, 176/6202 in 24 hour epirubicin, 40/1327 in 24 hour mitoxantrone and 0 in Trastuzumb.

Count of genes in each treatment by using adj. P value of <0.05
time id n K4 K5
24_hours Daunorubicin 6864 199 187
24_hours Doxorubicin 6516 184 180
24_hours Epirubicin 6202 172 176
24_hours Mitoxantrone 1327 30 40
Count of genes in each treatment by total expressed genes
time id n K4 K5
24_hours Daunorubicin 14084 503 374
24_hours Doxorubicin 14084 503 374
24_hours Epirubicin 14084 503 374
24_hours Mitoxantrone 14084 503 374
24_hours Trastuzumab 14084 503 374
[1] "this is the meSNPs (mininmally expressed QTLS)"
# A tibble: 8 × 3
# Groups:   id [4]
  id           time         pvalue
  <fct>        <chr>         <dbl>
1 Daunorubicin 24_hours 0.0000338 
2 Daunorubicin 3_hours  0.00188   
3 Doxorubicin  24_hours 0.0000113 
4 Doxorubicin  3_hours  0.923     
5 Epirubicin   24_hours 0.00000740
6 Epirubicin   3_hours  0.219     
7 Mitoxantrone 24_hours 0.00865   
8 Mitoxantrone 3_hours  0.685     
[1] "below is the reQTLS in knowles data ( reQTLS)"
# A tibble: 8 × 3
# Groups:   id [4]
  id           time     pvalue
  <fct>        <chr>     <dbl>
1 Daunorubicin 24_hours  0.658
2 Daunorubicin 3_hours   0.739
3 Doxorubicin  24_hours  0.497
4 Doxorubicin  3_hours   1.00 
5 Epirubicin   24_hours  0.254
6 Epirubicin   3_hours   0.571
7 Mitoxantrone 24_hours  0.445
8 Mitoxantrone 3_hours   0.395

ArrGWAS to 24 hour DEG genes p < 0.05

24 hour data set

Significant (adj. P value of <0.05) and non-sig gene counts in Arrhythmia 24 hour GWAS
id sigcount ARR ARRcount
Daunorubicin notsig no 7169
Daunorubicin notsig y 51
Daunorubicin sig no 6795
Daunorubicin sig y 69
Doxorubicin notsig no 7512
Doxorubicin notsig y 56
Doxorubicin sig no 6452
Doxorubicin sig y 64
Epirubicin notsig no 7827
Epirubicin notsig y 55
Epirubicin sig no 6137
Epirubicin sig y 65
Mitoxantrone notsig no 12650
Mitoxantrone notsig y 107
Mitoxantrone sig no 1314
Mitoxantrone sig y 13
Trastuzumab notsig no 13964
Trastuzumab notsig y 120

3 hour data set

Significant (adj. P value of <0.05) and non-sig gene counts in Arrhythmia 24 hour GWAS
id sigcount ARR ARRcount
Daunorubicin notsig no 7169
Daunorubicin notsig y 51
Daunorubicin sig no 6795
Daunorubicin sig y 69
Doxorubicin notsig no 7512
Doxorubicin notsig y 56
Doxorubicin sig no 6452
Doxorubicin sig y 64
Epirubicin notsig no 7827
Epirubicin notsig y 55
Epirubicin sig no 6137
Epirubicin sig y 65
Mitoxantrone notsig no 12650
Mitoxantrone notsig y 107
Mitoxantrone sig no 1314
Mitoxantrone sig y 13
Trastuzumab notsig no 13964
Trastuzumab notsig y 120

chi square test ARR

chi_funarr <-  toplistall %>% 
  mutate(id = as.factor(id)) %>%
  dplyr::filter(id!="Trastuzumab") %>% 
  mutate(sigcount = if_else(adj.P.Val <0.05,'sig','notsig'))%>%
  mutate(ARR=if_else(ENTREZID %in%Arr_geneset$entrezgene_id,"y","no")) %>% 
  group_by(id,time) %>% 
  summarise(pvalue= chisq.test(ARR, sigcount)$p.value) 
print("after performing chi square test between DEgenes, and non DE genes")
[1] "after performing chi square test between DEgenes, and non DE genes"
chi_funarr
# A tibble: 8 × 3
# Groups:   id [4]
  id           time      pvalue
  <fct>        <chr>      <dbl>
1 Daunorubicin 24_hours 0.0662 
2 Daunorubicin 3_hours  0.0246 
3 Doxorubicin  24_hours 0.142  
4 Doxorubicin  3_hours  1.00   
5 Epirubicin   24_hours 0.0313 
6 Epirubicin   3_hours  0.644  
7 Mitoxantrone 24_hours 0.708  
8 Mitoxantrone 3_hours  0.00409

HFGWAS

24 hours HF

Significant (adj. P value of <0.05) and non-sig gene counts in HFhythmia GWAS
id sigcount HF HFcount
Daunorubicin notsig no 7209
Daunorubicin notsig y 11
Daunorubicin sig no 6842
Daunorubicin sig y 22
Doxorubicin notsig no 7556
Doxorubicin notsig y 12
Doxorubicin sig no 6495
Doxorubicin sig y 21
Epirubicin notsig no 7868
Epirubicin notsig y 14
Epirubicin sig no 6183
Epirubicin sig y 19
Mitoxantrone notsig no 12728
Mitoxantrone notsig y 29
Mitoxantrone sig no 1323
Mitoxantrone sig y 4
Trastuzumab notsig no 14051
Trastuzumab notsig y 33

3 hours HF

Significant (adj. P value of <0.05) and non-sig gene counts in Three hour HFhythmia GWAS
id sigcount HF HFcount
Daunorubicin notsig no 13498
Daunorubicin notsig y 31
Daunorubicin sig no 553
Daunorubicin sig y 2
Doxorubicin notsig no 14035
Doxorubicin notsig y 33
Doxorubicin sig no 16
Epirubicin notsig no 13831
Epirubicin notsig y 33
Epirubicin sig no 220
Mitoxantrone notsig no 13993
Mitoxantrone notsig y 33
Mitoxantrone sig no 58
Trastuzumab notsig no 14051
Trastuzumab notsig y 33

chi square test HF

chi_funhf <-  toplistall %>% 
  mutate(id = as.factor(id)) %>%
  dplyr::filter(id!="Trastuzumab") %>% 
  mutate(sigcount = if_else(adj.P.Val <0.05,'sig','notsig'))%>%
  mutate(HF=if_else(ENTREZID %in%HF_geneset$entrezgene_id,"y","no")) %>% 
  group_by(id,time) %>% 
  summarise(pvalue= chisq.test(HF, sigcount)$p.value) 
print("after performing chi square test between DEgenes, and non DE genes")
[1] "after performing chi square test between DEgenes, and non DE genes"
chi_funhf
# A tibble: 8 × 3
# Groups:   id [4]
  id           time     pvalue
  <fct>        <chr>     <dbl>
1 Daunorubicin 24_hours 0.0589
2 Daunorubicin 3_hours  0.858 
3 Doxorubicin  24_hours 0.0674
4 Doxorubicin  3_hours  1.00  
5 Epirubicin   24_hours 0.164 
6 Epirubicin   3_hours  0.983 
7 Mitoxantrone 24_hours 0.816 
8 Mitoxantrone 3_hours  1.00  

CAD GWAS

24 hour data set

Significant (adj. P value of <0.05) and non-sig gene counts in CAD GWAS
id sigcount CAD CADcount
Daunorubicin notsig no 7107
Daunorubicin notsig y 113
Daunorubicin sig no 6748
Daunorubicin sig y 116
Doxorubicin notsig no 7447
Doxorubicin notsig y 121
Doxorubicin sig no 6408
Doxorubicin sig y 108
Epirubicin notsig no 7762
Epirubicin notsig y 120
Epirubicin sig no 6093
Epirubicin sig y 109
Mitoxantrone notsig no 12547
Mitoxantrone notsig y 210
Mitoxantrone sig no 1308
Mitoxantrone sig y 19
Trastuzumab notsig no 13855
Trastuzumab notsig y 229

3 hour data set

Significant (adj. P value of <0.05) and non-sig gene counts in 3 hour CAD GWAS
id sigcount CAD CADcount
Daunorubicin notsig no 13317
Daunorubicin notsig y 212
Daunorubicin sig no 538
Daunorubicin sig y 17
Doxorubicin notsig no 13839
Doxorubicin notsig y 229
Doxorubicin sig no 16
Epirubicin notsig no 13643
Epirubicin notsig y 221
Epirubicin sig no 212
Epirubicin sig y 8
Mitoxantrone notsig no 13798
Mitoxantrone notsig y 228
Mitoxantrone sig no 57
Mitoxantrone sig y 1
Trastuzumab notsig no 13855
Trastuzumab notsig y 229

chi square test CAD

chi_funCAD <-  toplistall %>% 
  mutate(id = as.factor(id)) %>%
  dplyr::filter(id!="Trastuzumab") %>% 
  mutate(sigcount = if_else(adj.P.Val <0.05,'sig','notsig'))%>%
  mutate(CAD=if_else(ENTREZID %in%CAD_geneset$entrezgene_id,"y","no")) %>% 
  group_by(id,time) %>% 
  summarise(pvalue= chisq.test(CAD, sigcount)$p.value) 
print("after performing chi square test between DEgenes, and non DE genes")
[1] "after performing chi square test between DEgenes, and non DE genes"
chi_funCAD
# A tibble: 8 × 3
# Groups:   id [4]
  id           time     pvalue
  <fct>        <chr>     <dbl>
1 Daunorubicin 24_hours 0.604 
2 Daunorubicin 3_hours  0.0105
3 Doxorubicin  24_hours 0.836 
4 Doxorubicin  3_hours  1.00  
5 Epirubicin   24_hours 0.304 
6 Epirubicin   3_hours  0.0351
7 Mitoxantrone 24_hours 0.636 
8 Mitoxantrone 3_hours  1.00  

GTEx V8 heart-left ventrical egenes

####chi square
Significant (adj. P value of <0.05) and non-sig gene counts in Arrhythmia 24 hour GWAS
id time sigcount no y
Daunorubicin 24_hours notsig 866 6354
Daunorubicin 24_hours sig 797 6067
Daunorubicin 3_hours notsig 1628 11901
Daunorubicin 3_hours sig 35 520
Doxorubicin 24_hours notsig 920 6648
Doxorubicin 24_hours sig 743 5773
Doxorubicin 3_hours notsig 1662 12406
Doxorubicin 3_hours sig 1 15
Epirubicin 24_hours notsig 953 6929
Epirubicin 24_hours sig 710 5492
Epirubicin 3_hours notsig 1651 12213
Epirubicin 3_hours sig 12 208
Mitoxantrone 24_hours notsig 1503 11254
Mitoxantrone 24_hours sig 160 1167
Mitoxantrone 3_hours notsig 1660 12366
Mitoxantrone 3_hours sig 3 55
Trastuzumab 24_hours notsig 1663 12421
Trastuzumab 3_hours notsig 1663 12421
[1] "after performing chi square test between DEgenes, and non DE genes"
# A tibble: 8 × 3
# Groups:   id [4]
  id           time        pvalue
  <fct>        <chr>        <dbl>
1 Daunorubicin 24_hours 0.498    
2 Daunorubicin 3_hours  0.0000556
3 Doxorubicin  24_hours 0.175    
4 Doxorubicin  3_hours  0.763    
5 Epirubicin   24_hours 0.251    
6 Epirubicin   3_hours  0.00454  
7 Mitoxantrone 24_hours 0.802    
8 Mitoxantrone 3_hours  0.172    

Seaone 2019

Seoane, Jose Chromatin gene comparison: comes from supp data NAT. MED 2019 ### 24 hours in Pairwise with supplemental data 1

Significant (adj. P value of <0.05) and non-sig gene counts in Seoane geneset
id notsig_no notsig_y sig_no sig_y
Daunorubicin 7065 155 6689 175
Doxorubicin 7407 161 6347 169
Epirubicin 7717 165 6037 165
Mitoxantrone 12483 274 1271 56
Trastuzumab 13754 330 NA NA
# A tibble: 4 × 2
  id               pvalue
  <fct>             <dbl>
1 Daunorubicin 0.128     
2 Doxorubicin  0.0771    
3 Epirubicin   0.0314    
4 Mitoxantrone 0.00000326

3 hours data Pairwise

Significant (adj. P value of <0.05) and non-sig gene counts in 3 hours Seoane geneset
id notsig_no notsig_y sig_no sig_y
Daunorubicin 13227 302 527 28
Doxorubicin 13738 330 16 NA
Epirubicin 13551 313 203 17
Mitoxantrone 13698 328 56 2
Trastuzumab 13754 330 NA NA

chi square test Seaone

##remove Trastuzumab in order to perform chi square tests by time and drug between  DE and non DE enrichment
chi_fun <-  toplistall %>% 
  mutate(id = as.factor(id)) %>%
  dplyr::filter(id!="Trastuzumab") %>% 
  mutate(sigcount = if_else(adj.P.Val <0.05,'sig','notsig'))%>%
  mutate(chrom=if_else(ENTREZID %in%chrom_genes,"y","no")) %>% 
  group_by(id,time) %>% 
  summarise(pvalue= chisq.test(chrom, sigcount)$p.value) 
print("after performing chi square test between DEgenes, and non DE genes")
[1] "after performing chi square test between DEgenes, and non DE genes"
chi_fun
# A tibble: 8 × 3
# Groups:   id [4]
  id           time          pvalue
  <fct>        <chr>          <dbl>
1 Daunorubicin 24_hours 0.128      
2 Daunorubicin 3_hours  0.0000332  
3 Doxorubicin  24_hours 0.0771     
4 Doxorubicin  3_hours  1.00       
5 Epirubicin   24_hours 0.0314     
6 Epirubicin   3_hours  0.000000346
7 Mitoxantrone 24_hours 0.00000326 
8 Mitoxantrone 3_hours  0.902      

Supplemental 4 Seoane 3 hours followed by 24 hours

entrez gene pval.exp pval.anthr pval.expAnth adjpval
11176 BAZ2A 0.0020064 0.0000768 0.0004553 0.1299006
10284 SAP18 0.0013141 0.0000648 0.0006081 0.1299006
8819 SAP30 0.0023742 0.0000576 0.0007455 0.1299006
23522 KAT6B 0.0050327 0.0001601 0.0012776 0.1343691
7786 MAP3K12 0.0062822 0.0001296 0.0014816 0.1343691
2146 EZH2 0.0075650 0.0001626 0.0020478 0.1343691
4297 KMT2A 0.0096126 0.0001301 0.0023292 0.1343691
79913 ACTR5 0.0087568 0.0001883 0.0035210 0.1373866
8242 KDM5C 0.0139853 0.0001783 0.0036176 0.1373866
51780 KDM3B 0.0155602 0.0001675 0.0039239 0.1373866
6872 TAF1 0.0105527 0.0001952 0.0043619 0.1447734
23135 KDM6B 0.0074796 0.0001950 0.0047811 0.1514738
6877 TAF5 0.0233826 0.0002047 0.0067329 0.1624738
23030 KDM4B 0.0239951 0.0004270 0.0069023 0.1624738
64324 NSD1 0.0164702 0.0003839 0.0069286 0.1624738
79885 HDAC11 0.0256039 0.0002383 0.0071964 0.1624738
10847 SRCAP 0.0174738 0.0003660 0.0077132 0.1624738
7404 UTY 0.0114041 0.0002112 0.0078450 0.1624738
51773 RSF1 0.0283587 0.0001948 0.0080182 0.1624738
5253 PHF2 0.0119978 0.0002989 0.0093089 0.1624738
9126 SMC3 0.0347884 0.0002127 0.0095907 0.1624738
3054 HCFC1 0.0317868 0.0003159 0.0097354 0.1624738
9734 HDAC9 0.0353794 0.0001985 0.0103307 0.1649465
53335 BCL11A 0.0063102 0.0004723 0.0105391 0.1649465
83444 INO80B 0.0255912 0.0003477 0.0112276 0.1701220
27350 APOBEC3C 0.0051330 0.0004220 0.0122160 0.1745980
6601 SMARCC2 0.0336512 0.0003435 0.0122745 0.1745980
1108 CHD4 0.0238388 0.0003994 0.0127656 0.1778779
8289 ARID1A 0.0492112 0.0004149 0.0146053 0.1870798
890 CCNA2 0.0444477 0.0004539 0.0147624 0.1870798
64151 NCAPG 0.0003946 0.0003956 0.0154184 0.1919043
10445 MCRS1 0.0185317 0.0003143 0.0162352 0.1977683
7150 TOP1 0.0468031 0.0003256 0.0175446 0.2072644
8110 DPF3 0.0612773 0.0004235 0.0182917 0.2124890
54531 MIER2 0.0244962 0.0004771 0.0198964 0.2273412
51409 HEMK1 0.0718548 0.0004890 0.0223436 0.2395917
27097 TAF5L 0.0450661 0.0003586 0.0237889 0.2512251
9739 SETD1A 0.0590016 0.0005136 0.0245980 0.2558930
6595 SMARCA2 0.0491644 0.0005485 0.0267793 0.2645703
9555 H2AFY 0.0852250 0.0004323 0.0277200 0.2645703
22823 MTF2 0.0823105 0.0005160 0.0278843 0.2645703
54556 ING3 0.0701823 0.0004542 0.0280892 0.2645703
10592 SMC2 0.0788583 0.0006366 0.0286097 0.2658792
8360 HIST1H4D 0.0801302 0.0004891 0.0300157 0.2715200
7528 YY1 0.1017709 0.0005254 0.0342873 0.2836505
9031 BAZ1B 0.1069563 0.0005045 0.0354054 0.2836505
51377 UCHL5 0.1048249 0.0005627 0.0372967 0.2954064
7799 PRDM2 0.0130131 0.0006154 0.0382200 0.2993182
6602 SMARCD1 0.1110653 0.0006993 0.0446426 0.3241241
8202 NCOA3 0.1179716 0.0006899 0.0454845 0.3251323
51564 HDAC7 0.1331938 0.0007507 0.0463305 0.3251323
26038 CHD5 0.0624026 0.0005717 0.0477023 0.3265622
79858 NEK11 0.1358428 0.0006363 0.0490482 0.3265622
10856 RUVBL2 0.1277997 0.0007652 0.0498579 0.3278390
56950 SMYD2 0.1018914 0.0005905 0.0518406 0.3309161
23081 KDM4C 0.1535477 0.0006316 0.0532858 0.3316091
5252 PHF1 0.0589957 0.0009403 0.0555393 0.3368815
10013 HDAC6 0.1508016 0.0007079 0.0565497 0.3368815
605 BCL7A 0.1751811 0.0007178 0.0624106 0.3655476
57680 CHD8 0.1404978 0.0008216 0.0641107 0.3689294
1786 DNMT1 0.0742639 0.0010553 0.0645759 0.3689294
8932 MBD2 0.0413684 0.0006937 0.0670724 0.3720616
7703 PCGF2 0.1080288 0.0008390 0.0672593 0.3720616
387893 SETD8 0.0325632 0.0009001 0.0686467 0.3767463
580 BARD1 0.1608293 0.0008545 0.0703017 0.3780927
10274 STAG1 0.1947375 0.0007542 0.0705194 0.3780927
22992 KDM2A 0.0895616 0.0008314 0.0721146 0.3785189
23133 PHF8 0.1967192 0.0007999 0.0721911 0.3785189
2122 MECOM 0.1604149 0.0005958 0.0738655 0.3819777
10014 HDAC5 0.2023646 0.0008066 0.0739842 0.3819777
3065 HDAC1 0.1594520 0.0008370 0.0756461 0.3832878
5978 REST 0.1957721 0.0010360 0.0804648 0.3900125
3012 HIST1H2AE 0.1401041 0.0007383 0.0809965 0.3900125
5580 PRKCD 0.0509785 0.0008470 0.0816956 0.3900125
3014 H2AFX 0.2039117 0.0009085 0.0874291 0.4029968
3070 HELLS 0.2103033 0.0009403 0.0883885 0.4029968
8986 RPS6KA4 0.2439747 0.0009272 0.0946755 0.4193677
9437 NCR1 0.1757248 0.0008247 0.0961261 0.4193677
10765 KDM5B 0.1822552 0.0009461 0.0962681 0.4193677
8726 EED 0.2536795 0.0009473 0.1022474 0.4392490
8193 DPF1 0.1617765 0.0009552 0.1027225 0.4392490
5926 ARID4A 0.2046667 0.0009423 0.1051371 0.4441245
54892 NCAPG2 0.2719518 0.0009517 0.1128685 0.4611639
8940 TOP3B 0.2249933 0.0010145 0.1133632 0.4611639
8846 ALKBH1 0.2036862 0.0009869 0.1135440 0.4611639
3010 HIST1H1T 0.2667468 0.0011193 0.1144639 0.4611639
1106 CHD2 0.2880572 0.0010542 0.1174722 0.4667835
8208 CHAF1B 0.1937594 0.0009776 0.1202905 0.4667835
10362 HMG20B 0.1361831 0.0012375 0.1262857 0.4815909
9112 MTA1 0.1427047 0.0009402 0.1279681 0.4821284
9085 CDY1 0.3218780 0.0009130 0.1329480 0.4907047
94239 H2AFV 0.3003611 0.0012008 0.1387756 0.5090874
6881 TAF10 0.3189882 0.0011176 0.1399581 0.5103299
23210 JMJD6 0.0919038 0.0007698 0.1413109 0.5103299
6594 SMARCA1 0.1546347 0.0009547 0.1437595 0.5150159
8363 HIST1H4J 0.3520914 0.0011282 0.1508657 0.5257670
84148 KAT8 0.2983520 0.0011745 0.1549367 0.5293672
29781 NCAPH2 0.3655076 0.0010676 0.1562522 0.5312574
8353 HIST1H3E 0.1915487 0.0012986 0.1581816 0.5346205
55729 ATF7IP 0.2948714 0.0013894 0.1682765 0.5558707
983 CDK1 0.0629620 0.0013244 0.1718000 0.5637297
8085 KMT2D 0.0700734 0.0013433 0.1722732 0.5637297
29947 DNMT3L 0.3907083 0.0011692 0.1741601 0.5672412
8341 HIST1H2BN 0.3038599 0.0012809 0.1756827 0.5695389
54904 WHSC1L1 0.2716103 0.0011647 0.1804500 0.5796020
2648 KAT2A 0.4161649 0.0013730 0.1855535 0.5841609
8314 BAP1 0.3409185 0.0011776 0.1876702 0.5841609
7156 TOP3A 0.4236425 0.0012461 0.1903988 0.5872035
79903 NAA60 0.4195751 0.0011239 0.1929107 0.5923293
8520 HAT1 0.4012166 0.0011749 0.1967219 0.5948079
8850 KAT2B 0.2666902 0.0011467 0.1994806 0.5948079
60 ACTB 0.4095978 0.0012664 0.2013874 0.5948079
9682 KDM4A 0.0481174 0.0015790 0.2017269 0.5948079
23186 RCOR1 0.4301622 0.0011904 0.2022517 0.5948079
3020 H3F3A 0.2045507 0.0013618 0.2060137 0.5962376
6760 SS18 0.4294569 0.0013025 0.2109426 0.6050494
8358 HIST1H3B 0.4201442 0.0012949 0.2151399 0.6136120
25855 BRMS1 0.4468722 0.0012664 0.2169290 0.6136120
3066 HDAC2 0.4660358 0.0012886 0.2183377 0.6136120
3015 H2AFZ 0.0874045 0.0013310 0.2207807 0.6136120
1107 CHD3 0.1755650 0.0013172 0.2213914 0.6136120
5885 RAD21 0.4212044 0.0012773 0.2220577 0.6136120
23067 SETD1B 0.4112018 0.0012331 0.2227261 0.6136120
8369 HIST1H4G 0.0541162 0.0012376 0.2257280 0.6141468
8464 SUPT3H 0.1407375 0.0016483 0.2269937 0.6141468
1387 CREBBP 0.3879121 0.0012180 0.2372847 0.6229271
6597 SMARCA4 0.4942178 0.0012715 0.2418715 0.6229271
7403 KDM6A 0.2449474 0.0016652 0.2421998 0.6229271
6599 SMARCC1 0.4398084 0.0013837 0.2434911 0.6239459
55818 KDM3A 0.4796703 0.0013475 0.2453545 0.6244196
4221 MEN1 0.1602106 0.0015565 0.2474853 0.6257047
8354 HIST1H3I 0.2038803 0.0013008 0.2566278 0.6427487
64919 BCL11B 0.3397001 0.0013853 0.2572176 0.6427487
2778 GNAS 0.5313339 0.0013673 0.2607783 0.6486002
23063 WAPAL 0.4803188 0.0014101 0.2640332 0.6486002
10474 TADA3 0.4182951 0.0012877 0.2646696 0.6486002
5925 RB1 0.0180124 0.0013884 0.2654438 0.6486002
8352 HIST1H3C 0.5101982 0.0013060 0.2699923 0.6486002
23047 PDS5B 0.4115119 0.0012520 0.2737249 0.6486002
10629 TAF6L 0.0816771 0.0019828 0.2760177 0.6486002
9985 REC8 0.1586687 0.0018821 0.2763251 0.6486002
79068 FTO 0.5262576 0.0015592 0.2766267 0.6486002
22933 SIRT2 0.4597759 0.0016624 0.2771808 0.6486002
79831 KDM8 0.4677240 0.0012925 0.2784008 0.6486002
23244 PDS5A 0.1845290 0.0013872 0.2785362 0.6486002
53615 MBD3 0.5558590 0.0014806 0.2804021 0.6493032
2145 EZH1 0.1060722 0.0020869 0.2910505 0.6673804
6944 VPS72 0.4682202 0.0014221 0.2942899 0.6681434
11143 KAT7 0.5797010 0.0013508 0.2965725 0.6689678
10051 SMC4 0.4276521 0.0013049 0.3022513 0.6717176
8284 KDM5D 0.5865781 0.0013536 0.3027745 0.6717176
1105 CHD1 0.0562306 0.0013912 0.3031303 0.6717176
51412 ACTL6B 0.0221382 0.0021366 0.3045909 0.6718349
9575 CLOCK 0.3073567 0.0019029 0.3103947 0.6781978
546 ATRX 0.1651338 0.0015736 0.3115867 0.6786748
55274 PHF10 0.4525002 0.0017498 0.3131804 0.6800210
171023 ASXL1 0.6120455 0.0015709 0.3287267 0.6931019
51111 SUV420H1 0.4922838 0.0015531 0.3321552 0.6973258
63976 PRDM16 0.5048752 0.0014079 0.3413291 0.7059537
7468 WHSC1 0.3778328 0.0014712 0.3453368 0.7100287
51742 ARID4B 0.3576952 0.0013925 0.3464448 0.7102118
9862 MED24 0.5470702 0.0015288 0.3503800 0.7119968
23310 NCAPD3 0.6477900 0.0016613 0.3530529 0.7151499
8330 HIST1H2AK 0.5342719 0.0014776 0.3544273 0.7151499
65980 BRD9 0.3009104 0.0017701 0.3601637 0.7183265
9757 KMT2B 0.5925410 0.0016623 0.3611372 0.7183265
23411 SIRT1 0.0187646 0.0019759 0.3661747 0.7250675
10930 APOBEC2 0.6220587 0.0015421 0.3686738 0.7266228
7994 KAT6A 0.6671120 0.0015130 0.3690451 0.7266228
84159 ARID5B 0.4855813 0.0014913 0.3711648 0.7277438
7153 TOP2A 0.0050316 0.0007244 0.3717027 0.7277438
472 ATM 0.5440090 0.0015634 0.3732830 0.7287907
9918 NCAPD2 0.6144771 0.0020164 0.3777089 0.7353718
1788 DNMT3A 0.1278948 0.0014916 0.3805218 0.7387846
339 APOBEC1 0.5149224 0.0014329 0.3906825 0.7501384
9913 SUPT7L 0.5899114 0.0015518 0.3908648 0.7501384
11177 BAZ1A 0.1380176 0.0016418 0.3934506 0.7511128
86 ACTL6A 0.4151123 0.0015856 0.3944151 0.7511128
6015 RING1 0.1908945 0.0020119 0.3987402 0.7514971
8648 NCOA1 0.4412936 0.0016790 0.3994943 0.7514971
57379 AICDA 0.1088297 0.0016494 0.4000078 0.7514971
6605 SMARCE1 0.0593302 0.0008522 0.4083211 0.7587234
2033 EP300 0.4087541 0.0017250 0.4092930 0.7587234
10042 HMGXB4 0.5989112 0.0016359 0.4213236 0.7587234
8493 PPM1D 0.2754904 0.0017428 0.4231774 0.7587234
55193 PBRM1 0.4636168 0.0013669 0.4237488 0.7587234
55636 CHD7 0.5530869 0.0015638 0.4266136 0.7587234
80012 PHC3 0.4454474 0.0017710 0.4302145 0.7587234
1017 CDK2 0.6040736 0.0016532 0.4344865 0.7587234
8089 YEATS4 0.6796301 0.0015984 0.4370231 0.7587234
8243 SMC1A 0.0511580 0.0016305 0.4370911 0.7587234
85236 HIST1H2BK 0.6323675 0.0014129 0.4389658 0.7587234
54145 H2BFS 0.6015056 0.0014664 0.4390000 0.7587234
79813 EHMT1 0.6604409 0.0015757 0.4410421 0.7587234
23492 CBX7 0.1290072 0.0021702 0.4430422 0.7587234
23309 SIN3B 0.2406406 0.0017645 0.4511831 0.7662102
10919 EHMT2 0.7371111 0.0017541 0.4569279 0.7662102
5931 RBBP7 0.1589186 0.0019033 0.4602643 0.7662102
55870 ASH1L 0.1526004 0.0016161 0.4631444 0.7662102
8841 HDAC3 0.1869212 0.0019228 0.4645445 0.7662102
5977 DPF2 0.0306372 0.0019654 0.4693999 0.7662102
8359 HIST1H4A 0.7614716 0.0016568 0.4708443 0.7663836
23512 SUZ12 0.4344670 0.0014710 0.4747729 0.7669624
6419 SETMAR 0.7561483 0.0016581 0.4759176 0.7669624
8535 CBX4 0.7280592 0.0018291 0.4764630 0.7669624
3017 HIST1H2BD 0.7819131 0.0016560 0.4835593 0.7730294
200316 APOBEC3F 0.3982946 0.0011894 0.4887242 0.7794984
6603 SMARCD2 0.7900836 0.0016116 0.4960757 0.7876191
54815 GATAD2A 0.4127019 0.0019824 0.5029724 0.7961341
8331 HIST1H2AJ 0.5990501 0.0015006 0.5102847 0.8028633
84193 SETD3 0.7132852 0.0015549 0.5134659 0.8060490
9252 RPS6KA5 0.7349994 0.0018791 0.5212601 0.8127927
9070 ASH2L 0.7763350 0.0016710 0.5328165 0.8222253
8970 HIST1H2BJ 0.6588479 0.0014608 0.5407395 0.8222253
9869 SETDB1 0.7802729 0.0017989 0.5412213 0.8222253
84864 MINA 0.7142664 0.0015278 0.5451808 0.8222253
10735 STAG2 0.8354065 0.0017597 0.5547007 0.8250203
1789 DNMT3B 0.4875240 0.0015198 0.5551428 0.8250203
3007 HIST1H1D 0.2165008 0.0016822 0.5603685 0.8292501
80205 CHD9 0.7937180 0.0017674 0.5618366 0.8296612
26610 ELP4 0.5447353 0.0016525 0.5737150 0.8340234
6598 SMARCB1 0.0514494 0.0029348 0.5755599 0.8340234
4552 MTRR 0.7373405 0.0016159 0.5781484 0.8343052
64431 ACTR6 0.7964734 0.0017459 0.5814011 0.8372656
57332 CBX8 0.6620625 0.0018099 0.5885466 0.8403943
9274 BCL7C 0.6883955 0.0015587 0.5911020 0.8403943
29994 BAZ2B 0.2686543 0.0016847 0.5914767 0.8403943
6299 SALL1 0.7896072 0.0016818 0.5919564 0.8403943
10036 CHAF1A 0.3012395 0.0022595 0.5956765 0.8412318
93973 ACTR8 0.8599990 0.0017483 0.6047645 0.8413589
51616 TAF9B 0.4292715 0.0020349 0.6086993 0.8428907
8355 HIST1H3G 0.8769665 0.0017316 0.6092729 0.8428907
8349 HIST2H2BE 0.8808162 0.0018477 0.6161056 0.8453260
8971 H1FX 0.7975584 0.0017817 0.6207985 0.8500914
29072 SETD2 0.8259135 0.0017726 0.6239638 0.8508431
8332 HIST1H2AL 0.8060010 0.0016974 0.6252378 0.8508431
6839 SUV39H1 0.6644275 0.0017689 0.6262303 0.8508431
84733 CBX2 0.1087613 0.0014613 0.6350208 0.8576195
79685 SAP30L 0.0623680 0.0024484 0.6397383 0.8576195
4591 TRIM37 0.7989001 0.0018329 0.6434199 0.8576195
1911 PHC1 0.5769332 0.0018927 0.6435223 0.8576195
138151 NACC2 0.0128982 0.0027161 0.6464590 0.8598891
328 APEX1 0.2881811 0.0018936 0.6521690 0.8617144
2186 BPTF 0.8288484 0.0017354 0.6534737 0.8617144
8290 HIST3H3 0.6761244 0.0017682 0.6547854 0.8617144
1386 ATF2 0.3682518 0.0015892 0.6574158 0.8617144
9275 BCL7B 0.8056415 0.0015979 0.6623813 0.8617144
55252 ASXL2 0.4017567 0.0019069 0.6626670 0.8617144
6996 TDG 0.1863745 0.0018759 0.6716559 0.8641405
8350 HIST1H3A 0.6120455 0.0020072 0.6725535 0.8641405
10524 KAT5 0.0360253 0.0017899 0.6749384 0.8641405
8473 OGT 0.4284415 0.0021886 0.6756909 0.8641405
8368 HIST1H4L 0.8811659 0.0017446 0.6830448 0.8661871
3018 HIST1H2BB 0.9192624 0.0018086 0.6901696 0.8714642
672 BRCA1 0.0029921 0.0012163 0.6967982 0.8782430
10664 CTCF 0.8870007 0.0017337 0.7065975 0.8874723
8348 HIST1H2BO 0.7704641 0.0016555 0.7100036 0.8874723
29844 TFPT 0.7827575 0.0016687 0.7116461 0.8874723
5927 KDM5A 0.6751438 0.0021356 0.7263597 0.8992410
9759 HDAC4 0.8810723 0.0018060 0.7382706 0.9097977
5928 RBBP4 0.9446749 0.0018818 0.7388027 0.9097977
8365 HIST1H4H 0.9389097 0.0020161 0.7472166 0.9129609
6604 SMARCD3 0.6477411 0.0017066 0.7492305 0.9129609
79828 METTL8 0.7873235 0.0020193 0.7510440 0.9135736
56979 PRDM9 0.0342685 0.0016262 0.7534212 0.9148687
55693 KDM4D 0.9478952 0.0018232 0.7586623 0.9180341
10734 STAG3 0.0779382 0.0028245 0.7631101 0.9218158
200424 TET3 0.5827257 0.0018635 0.7684779 0.9250934
55766 H2AFJ 0.8598732 0.0019617 0.7709295 0.9264446
7155 TOP2B 0.3757660 0.0019205 0.7736113 0.9280673
55506 H2AFY2 0.8900352 0.0018245 0.7774429 0.9294644
55249 YY1AP1 0.7884448 0.0018896 0.7835173 0.9315876
2073 ERCC5 0.3469132 0.0018728 0.7873704 0.9315876
8467 SMARCA5 0.7688751 0.0017718 0.7885748 0.9315876
3005 H1F0 0.9665025 0.0018894 0.7945288 0.9327485
8336 HIST1H2AM 0.7180478 0.0015720 0.7984828 0.9327485
3008 HIST1H1E 0.5308329 0.0017009 0.7988982 0.9327485
2353 FOS 0.3718542 0.0015814 0.8049700 0.9351068
64754 SMYD3 0.5165780 0.0022601 0.8072798 0.9357660
6045 RNF2 0.2465449 0.0023677 0.8083934 0.9357660
8342 HIST1H2BM 0.9485984 0.0018824 0.8184152 0.9381969
55140 ELP3 0.8461810 0.0018393 0.8203656 0.9381969
6883 TAF12 0.9712324 0.0018479 0.8286729 0.9422267
8344 HIST1H2BE 0.8725188 0.0017585 0.8389631 0.9467150
79723 SUV39H2 0.8480378 0.0020914 0.8528568 0.9526949
4255 MGMT 0.4225143 0.0017406 0.8550499 0.9526949
642636 RAD21L1 0.8957358 0.0018693 0.8567153 0.9526949
3720 JARID2 0.9734268 0.0019294 0.8672433 0.9594740
8335 HIST1H2AB 0.8566352 0.0019843 0.8712667 0.9623976
84444 DOT1L 0.9457172 0.0018499 0.8818866 0.9656724
3006 HIST1H1C 0.8244579 0.0016638 0.8836298 0.9656724
92815 HIST3H2A 0.8007033 0.0017142 0.8931993 0.9656724
80853 KDM7A 0.3186267 0.0015589 0.8947532 0.9656724
8607 RUVBL1 0.7677550 0.0017436 0.8976321 0.9656724
8334 HIST1H2AC 0.9736561 0.0020215 0.9002402 0.9656724
3024 HIST1H1A 0.9919747 0.0019290 0.9021685 0.9659162
5929 RBBP5 0.1157755 0.0018672 0.9042386 0.9659844
4798 NFRKB 0.3810459 0.0020732 0.9079597 0.9661800
23397 NCAPH 0.9520029 0.0018458 0.9160706 0.9705259
8345 HIST1H2BH 0.9913906 0.0019194 0.9165494 0.9705259
54107 POLE3 0.4921221 0.0022789 0.9247095 0.9705259
1912 PHC2 0.1336156 0.0018800 0.9250157 0.9705259
8367 HIST1H4E 0.6714368 0.0018814 0.9276948 0.9705259
23028 KDM1A 0.1678295 0.0023331 0.9357884 0.9764139
8340 HIST1H2BL 0.5714782 0.0017358 0.9465857 0.9824445
51317 PHF21A 0.9382791 0.0019681 0.9682743 0.9903361
9329 GTF3C4 0.5973066 0.0016136 0.9689104 0.9903361
8968 HIST1H3F 0.4273824 0.0018692 0.9692350 0.9903361
3009 HIST1H1B 0.0815399 0.0036562 0.9841657 0.9966538
3607 FOXK2 0.8184091 0.0021198 0.9963911 0.9992584
9219 MTA2 0.7586908 0.0018994 0.9999977 0.9999977

[1] "Note, this is all 311 genes from the list using the pval.anthr column"
# A tibble: 8 × 3
# Groups:   id [4]
  id           time        pvalue
  <fct>        <chr>        <dbl>
1 Daunorubicin 24_hours 0.127    
2 Daunorubicin 3_hours  0.00486  
3 Doxorubicin  24_hours 0.137    
4 Doxorubicin  3_hours  1.00     
5 Epirubicin   24_hours 0.171    
6 Epirubicin   3_hours  0.00853  
7 Mitoxantrone 24_hours 0.0000543
8 Mitoxantrone 3_hours  1.00     

Using Baysian gene set enrichment on Chromatin from Seoane

<environment: R_GlobalEnv>
[1] 72
# A tibble: 10 × 6
# Groups:   ER, LR, TI, NR [5]
   ER    LR    TI    NR    chrom motifcount
   <chr> <chr> <chr> <chr> <chr>      <int>
 1 no    no    no    no    no            61
 2 no    no    no    no    y              2
 3 no    no    no    y     no          7385
 4 no    no    no    y     y            119
 5 no    no    y     no    no           518
 6 no    no    y     no    y             10
 7 no    y     no    no    no          5431
 8 no    y     no    no    y            114
 9 y     no    no    no    no           420
10 y     no    no    no    y             24

R version 4.2.2 (2022-10-31 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    

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] readxl_1.4.2       broom_1.0.3        kableExtra_1.3.4   sjmisc_2.8.9      
 [5] scales_1.2.1       ggpubr_0.6.0       cowplot_1.1.1      RColorBrewer_1.1-3
 [9] biomaRt_2.52.0     ggsignif_0.6.4     lubridate_1.9.2    forcats_1.0.0     
[13] stringr_1.5.0      dplyr_1.1.0        purrr_1.0.1        readr_2.1.4       
[17] tidyr_1.3.0        tibble_3.1.8       ggplot2_3.4.1      tidyverse_2.0.0   
[21] limma_3.52.4       workflowr_1.7.0   

loaded via a namespace (and not attached):
  [1] colorspace_2.1-0       ellipsis_0.3.2         sjlabelled_1.2.0      
  [4] rprojroot_2.0.3        XVector_0.36.0         fs_1.6.1              
  [7] rstudioapi_0.14        farver_2.1.1           bit64_4.0.5           
 [10] AnnotationDbi_1.58.0   fansi_1.0.4            xml2_1.3.3            
 [13] cachem_1.0.7           knitr_1.42             jsonlite_1.8.4        
 [16] dbplyr_2.3.1           png_0.1-8              compiler_4.2.2        
 [19] httr_1.4.5             backports_1.4.1        fastmap_1.1.1         
 [22] cli_3.6.0              later_1.3.0            htmltools_0.5.4       
 [25] prettyunits_1.1.1      tools_4.2.2            gtable_0.3.1          
 [28] glue_1.6.2             GenomeInfoDbData_1.2.8 rappdirs_0.3.3        
 [31] Rcpp_1.0.10            carData_3.0-5          Biobase_2.56.0        
 [34] cellranger_1.1.0       jquerylib_0.1.4        vctrs_0.5.2           
 [37] Biostrings_2.64.1      svglite_2.1.1          insight_0.19.0        
 [40] xfun_0.37              ps_1.7.2               rvest_1.0.3           
 [43] timechange_0.2.0       lifecycle_1.0.3        rstatix_0.7.2         
 [46] XML_3.99-0.13          getPass_0.2-2          zlibbioc_1.42.0       
 [49] vroom_1.6.1            hms_1.1.2              promises_1.2.0.1      
 [52] parallel_4.2.2         yaml_2.3.7             curl_5.0.0            
 [55] memoise_2.0.1          sass_0.4.5             stringi_1.7.12        
 [58] RSQLite_2.3.0          highr_0.10             S4Vectors_0.34.0      
 [61] BiocGenerics_0.42.0    filelock_1.0.2         GenomeInfoDb_1.32.4   
 [64] rlang_1.0.6            pkgconfig_2.0.3        systemfonts_1.0.4     
 [67] bitops_1.0-7           evaluate_0.20          labeling_0.4.2        
 [70] bit_4.0.5              processx_3.8.0         tidyselect_1.2.0      
 [73] magrittr_2.0.3         R6_2.5.1               IRanges_2.30.1        
 [76] generics_0.1.3         DBI_1.1.3              pillar_1.8.1          
 [79] whisker_0.4.1          withr_2.5.0            KEGGREST_1.36.3       
 [82] abind_1.4-5            RCurl_1.98-1.10        crayon_1.5.2          
 [85] car_3.1-1              utf8_1.2.3             BiocFileCache_2.4.0   
 [88] tzdb_0.3.0             rmarkdown_2.20         progress_1.2.2        
 [91] grid_4.2.2             blob_1.2.3             callr_3.7.3           
 [94] git2r_0.31.0           digest_0.6.31          webshot_0.5.4         
 [97] httpuv_1.6.9           stats4_4.2.2           munsell_0.5.0         
[100] viridisLite_0.4.1      bslib_0.4.2