Comparisons with other data sets
ERM
2023-06-15
Last updated: 2023-06-15
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Knit directory: Cardiotoxicity/
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Data set comparison order:
ArrGWAS
HFGWAS
CADGWAS
Seaone 2019
Supplemental 1 (408 genes)
Supplemental 4 (54 genes)
GWAS
ArrGWAS to 24 hour DEG genes p < 0.05
24 hour data set
| 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
| id | sigcount | ARR | ARRcount |
|---|---|---|---|
| Daunorubicin | notsig | no | 13419 |
| Daunorubicin | notsig | y | 110 |
| Daunorubicin | sig | no | 545 |
| Daunorubicin | sig | y | 10 |
| Doxorubicin | notsig | no | 13948 |
| Doxorubicin | notsig | y | 120 |
| Doxorubicin | sig | no | 16 |
| Epirubicin | notsig | no | 13747 |
| Epirubicin | notsig | y | 117 |
| Epirubicin | sig | no | 217 |
| Epirubicin | sig | y | 3 |
| Mitoxantrone | notsig | no | 13909 |
| Mitoxantrone | notsig | y | 117 |
| Mitoxantrone | sig | no | 55 |
| Mitoxantrone | sig | y | 3 |
| 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
| 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
| 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
| 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
| 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 
[1] "This is for GWAS 24 hours -log(chi square pvalue)"
Seoane 2019
Seoane, Jose Chromatin gene comparison: comes from supp data NAT. MED 2019 ### 24 hours in Pairwise with supplemental data 1
| 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 0.000003263 hours data Pairwise
| 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 |
[1] "These are the chisquare values from the 54 genes"# A tibble: 8 × 3
# Groups: id [4]
id time pvalue
<fct> <chr> <dbl>
1 Daunorubicin 24_hours 0.546
2 Daunorubicin 3_hours 0.732
3 Doxorubicin 24_hours 0.501
4 Doxorubicin 3_hours 1.00
5 Epirubicin 24_hours 0.320
6 Epirubicin 3_hours 0.748
7 Mitoxantrone 24_hours 0.0202
8 Mitoxantrone 3_hours 1.00 
Mitoxantrone is significantly enriched at 24 hours in the 54 genes from supplemental 4 Seoane.
Using Baysian gene sets to look at enrichment of Seoane data
Seoane Supplemental 4
<environment: R_GlobalEnv>| chrom | none | ER | TI | LR | NR |
|---|---|---|---|---|---|
| no | 63 | 7482 | 5525 | 525 | 439 |
| y | NA | 22 | 20 | 3 | 5 |
Pearson's Chi-squared test with Yates' continuity correction
data: chi_list4[, c("NR", "LR")]
X-squared = 0.36327, df = 1, p-value = 0.5467
Pearson's Chi-squared test with Yates' continuity correction
data: chi_list4[, c("NR", "ER")]
X-squared = 6.3065, df = 1, p-value = 0.01203
Pearson's Chi-squared test with Yates' continuity correction
data: chi_list4[, c("NR", "TI")]
X-squared = 4.099, df = 1, p-value = 0.04291
Pearson's Chi-squared test with Yates' continuity correction
data: chi_list4[, c("ER", "TI")]
X-squared = 0.26698, df = 1, p-value = 0.6054
Pearson's Chi-squared test with Yates' continuity correction
data: chi_list4[, c("ER", "LR")]
X-squared = 0.47936, df = 1, p-value = 0.4887
Pearson's Chi-squared test with Yates' continuity correction
data: chi_list4[, c("TI", "LR")]
X-squared = 0.13763, df = 1, p-value = 0.7107Seoane Supplemental 1
| chrom | none | ER | TI | LR | NR |
|---|---|---|---|---|---|
| no | 61 | 7363 | 5406 | 514 | 410 |
| y | 2 | 141 | 139 | 14 | 34 |
Pearson's Chi-squared test with Yates' continuity correction
data: chi_list1[, c("NR", "LR")]
X-squared = 11.832, df = 1, p-value = 0.0005824
Pearson's Chi-squared test with Yates' continuity correction
data: chi_list1[, c("NR", "ER")]
X-squared = 62.351, df = 1, p-value = 2.873e-15
Pearson's Chi-squared test with Yates' continuity correction
data: chi_list1[, c("NR", "TI")]
X-squared = 37.066, df = 1, p-value = 1.142e-09
Pearson's Chi-squared test with Yates' continuity correction
data: chi_list1[, c("ER", "TI")]
X-squared = 5.6896, df = 1, p-value = 0.01707
Pearson's Chi-squared test with Yates' continuity correction
data: chi_list1[, c("ER", "LR")]
X-squared = 1.1741, df = 1, p-value = 0.2786
Pearson's Chi-squared test with Yates' continuity correction
data: chi_list1[, c("TI", "LR")]
X-squared = 0.003306, df = 1, p-value = 0.9541chi square heatmap
Crispr list
[1] " number of unique crispr_genes after conversion from hgnc symbol to entrezid"[1] 154| one | two | p.value |
|---|---|---|
| LR | NR | 0.861985991471947 |
| ER | NR | 0.402681880154749 |
| TI | NR | 0.309642007916355 |
| time | id | sigcount | crisp | n |
|---|---|---|---|---|
| 3_hours | Daunorubicin | notsig | no | 13414 |
| 3_hours | Daunorubicin | notsig | y | 115 |
| 3_hours | Daunorubicin | sig | no | 553 |
| 3_hours | Daunorubicin | sig | y | 2 |
| 3_hours | Doxorubicin | notsig | no | 13951 |
| 3_hours | Doxorubicin | notsig | y | 117 |
| 3_hours | Doxorubicin | sig | no | 16 |
| 3_hours | Epirubicin | notsig | no | 13749 |
| 3_hours | Epirubicin | notsig | y | 115 |
| 3_hours | Epirubicin | sig | no | 218 |
| 3_hours | Epirubicin | sig | y | 2 |
| 3_hours | Mitoxantrone | notsig | no | 13909 |
| 3_hours | Mitoxantrone | notsig | y | 117 |
| 3_hours | Mitoxantrone | sig | no | 58 |
| 24_hours | Daunorubicin | notsig | no | 7165 |
| 24_hours | Daunorubicin | notsig | y | 55 |
| 24_hours | Daunorubicin | sig | no | 6802 |
| 24_hours | Daunorubicin | sig | y | 62 |
| 24_hours | Doxorubicin | notsig | no | 7512 |
| 24_hours | Doxorubicin | notsig | y | 56 |
| 24_hours | Doxorubicin | sig | no | 6455 |
| 24_hours | Doxorubicin | sig | y | 61 |
| 24_hours | Epirubicin | notsig | no | 7825 |
| 24_hours | Epirubicin | notsig | y | 57 |
| 24_hours | Epirubicin | sig | no | 6142 |
| 24_hours | Epirubicin | sig | y | 60 |
| 24_hours | Mitoxantrone | notsig | no | 12652 |
| 24_hours | Mitoxantrone | notsig | y | 105 |
| 24_hours | Mitoxantrone | sig | no | 1315 |
| 24_hours | Mitoxantrone | sig | y | 12 |
| time | id | pvalue |
|---|---|---|
| 3_hours | Daunorubicin | 0.2128915 |
| 3_hours | Doxorubicin | 0.7141341 |
| 3_hours | Epirubicin | 0.8973055 |
| 3_hours | Mitoxantrone | 0.4848796 |
| 24_hours | Daunorubicin | 0.3551194 |
| 24_hours | Doxorubicin | 0.2008692 |
| 24_hours | Epirubicin | 0.1128575 |
| 24_hours | Mitoxantrone | 0.7563882 |
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] grid stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] readxl_1.4.2 ComplexHeatmap_2.12.1 broom_1.0.5
[4] kableExtra_1.3.4 sjmisc_2.8.9 scales_1.2.1
[7] ggpubr_0.6.0 cowplot_1.1.1 RColorBrewer_1.1-3
[10] biomaRt_2.52.0 ggsignif_0.6.4 lubridate_1.9.2
[13] forcats_1.0.0 stringr_1.5.0 dplyr_1.1.2
[16] purrr_1.0.1 readr_2.1.4 tidyr_1.3.0
[19] tibble_3.2.1 ggplot2_3.4.2 tidyverse_2.0.0
[22] limma_3.52.4 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] colorspace_2.1-0 rjson_0.2.21 sjlabelled_1.2.0
[4] rprojroot_2.0.3 circlize_0.4.15 XVector_0.36.0
[7] GlobalOptions_0.1.2 fs_1.6.2 clue_0.3-64
[10] rstudioapi_0.14 farver_2.1.1 bit64_4.0.5
[13] AnnotationDbi_1.58.0 fansi_1.0.4 xml2_1.3.4
[16] codetools_0.2-19 doParallel_1.0.17 cachem_1.0.8
[19] knitr_1.43 jsonlite_1.8.5 cluster_2.1.4
[22] dbplyr_2.3.2 png_0.1-8 compiler_4.2.2
[25] httr_1.4.6 backports_1.4.1 fastmap_1.1.1
[28] cli_3.6.1 later_1.3.1 htmltools_0.5.5
[31] prettyunits_1.1.1 tools_4.2.2 gtable_0.3.3
[34] glue_1.6.2 GenomeInfoDbData_1.2.8 rappdirs_0.3.3
[37] Rcpp_1.0.10 carData_3.0-5 Biobase_2.56.0
[40] cellranger_1.1.0 jquerylib_0.1.4 vctrs_0.6.2
[43] Biostrings_2.64.1 svglite_2.1.1 iterators_1.0.14
[46] insight_0.19.2 xfun_0.39 ps_1.7.5
[49] rvest_1.0.3 timechange_0.2.0 lifecycle_1.0.3
[52] rstatix_0.7.2 XML_3.99-0.14 getPass_0.2-2
[55] zlibbioc_1.42.0 vroom_1.6.3 hms_1.1.3
[58] promises_1.2.0.1 parallel_4.2.2 yaml_2.3.7
[61] curl_5.0.1 memoise_2.0.1 sass_0.4.6
[64] stringi_1.7.12 RSQLite_2.3.1 highr_0.10
[67] S4Vectors_0.34.0 foreach_1.5.2 BiocGenerics_0.42.0
[70] filelock_1.0.2 shape_1.4.6 GenomeInfoDb_1.32.4
[73] matrixStats_1.0.0 rlang_1.1.1 pkgconfig_2.0.3
[76] systemfonts_1.0.4 bitops_1.0-7 evaluate_0.21
[79] labeling_0.4.2 bit_4.0.5 processx_3.8.1
[82] tidyselect_1.2.0 magrittr_2.0.3 R6_2.5.1
[85] IRanges_2.30.1 generics_0.1.3 DBI_1.1.3
[88] pillar_1.9.0 whisker_0.4.1 withr_2.5.0
[91] KEGGREST_1.36.3 abind_1.4-5 RCurl_1.98-1.12
[94] crayon_1.5.2 car_3.1-2 utf8_1.2.3
[97] BiocFileCache_2.4.0 tzdb_0.4.0 rmarkdown_2.22
[100] GetoptLong_1.0.5 progress_1.2.2 blob_1.2.4
[103] callr_3.7.3 git2r_0.32.0 digest_0.6.31
[106] webshot_0.5.4 httpuv_1.6.11 stats4_4.2.2
[109] munsell_0.5.0 viridisLite_0.4.2 bslib_0.5.0