Compare the clinical associations between batches
Junyan Lu
2021-02-16
Last updated: 2021-03-17
Checks: 6 1
Knit directory: CLLproteomics_batch13/analysis/
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Load packages and datasets
Load associations of different batches
Batch1
load("../output/resOutcome_batch1.RData")
cTab_b1 <- cTab %>% mutate(batch = "original")
uniRes_b1 <- uniRes %>% mutate(batch = "original")Batch2
load("../output/resOutcome_batch2.RData")
cTab_b2 <- cTab %>% mutate(batch = "batch2") #multi-vai
uniRes_b2 <- uniRes %>% mutate(batch = "batch2")Batch3
load("../output/resOutcome_batch3.RData")
cTab_b3 <- cTab %>% mutate(batch = "batch3")
uniRes_b3 <- uniRes %>% mutate(batch = "batch3")Combined batch 1 and batch3
load("../output/resOutcome_batch13.RData")
cTab_b13 <- cTab %>% mutate(batch = "extended")
uniRes_b13 <- uniRes %>% mutate(batch = "extended")Combined all batchs (only focus on TTT)
uniResTab <- bind_rows(uniRes_b1, uniRes_b2, uniRes_b3, uniRes_b13) %>% filter(outcome=="TTT") %>%
mutate(dir = ifelse(HR >1, "higer risk", "lower risk")) %>%
mutate(group =sprintf("%s (%s)", batch,dir))
cTab <- bind_rows(cTab_b1, cTab_b2, cTab_b3, cTab_b13) %>% filter(outcome == "TTT")Compare Univariate results
Overlap of all significant assocations (Only TTT)
5% FDR Cut-off
compareTab <- uniResTab %>% filter(outcome == "TTT", p.adj < 0.05)
overList <- lapply(unique(compareTab$group), function(bb) {
filter(compareTab, group ==bb)$id
})
names(overList) <- unique(compareTab$group)
upset(fromList(overList))
P < 0.05
compareTab <- uniResTab %>% filter(outcome == "TTT", p<=0.05) %>%
mutate(dir = ifelse(HR >1, "higer", "lower")) %>%
mutate(group =paste0(batch,"_",dir))
overList <- lapply(unique(compareTab$group), function(bb) {
filter(compareTab, group ==bb)$id
})
names(overList) <- unique(compareTab$group)
upset(fromList(overList))
Proteins that identified by batch1 (10%) and also show significant p-values (P<0.05) in batch 3
resB1 <- filter(uniResTab, batch == "original", p.adj < 0.1) %>%
select(name, dir) %>% dplyr::rename(dirB1 = dir)
resB3 <- filter(uniResTab, batch == "batch3", p < 0.05) %>%
select(name, dir) %>% dplyr::rename(dirB3 = dir)
resCom <- left_join(resB1, resB3, by = "name") %>%
filter(dirB1 == dirB3)List of such proteins
resCom# A tibble: 12 x 3
name dirB1 dirB3
<chr> <chr> <chr>
1 PHF1 lower risk lower risk
2 CLTB lower risk lower risk
3 TPD52L2 lower risk lower risk
4 MRPL46 higer risk higer risk
5 RELA lower risk lower risk
6 MTCH2 higer risk higer risk
7 RAB14 lower risk lower risk
8 MTX2 higer risk higer risk
9 PES1 higer risk higer risk
10 PURA lower risk lower risk
11 MRPL44 higer risk higer risk
12 CDKN1B lower risk lower riskProteins that identified by combined batch 1 and 3 (10%) and also show significant p-values (P<0.05) in batch 2
resB13 <- filter(uniResTab, batch == "extended", p.adj < 0.1) %>%
select(name, dir) %>% dplyr::rename(dirB13 = dir)
resB2 <- filter(uniResTab, batch == "batch2", p < 0.05) %>%
select(name, dir) %>% dplyr::rename(dirB2 = dir)
resCom <- left_join(resB13, resB2, by = "name") %>%
filter(dirB13 == dirB2)List of such proteins
resCom# A tibble: 7 x 3
name dirB13 dirB2
<chr> <chr> <chr>
1 MRPL44 higer risk higer risk
2 HSPD1 higer risk higer risk
3 APPL1 lower risk lower risk
4 TRAP1 higer risk higer risk
5 FKBP5 higer risk higer risk
6 CAP1 lower risk lower risk
7 NOP10 higer risk higer riskReproducibility of the candidates mentioned in the manuscript
PRMT5
filter(uniResTab, name == "PRMT5")# A tibble: 3 x 11
p HR lower higher id p.adj name outcome batch dir group
<dbl> <dbl> <dbl> <dbl> <chr> <dbl> <chr> <chr> <chr> <chr> <chr>
1 1.25e-5 2.85 1.78 4.57 O14744 0.0290 PRMT5 TTT orig… higer… original…
2 9.66e-1 0.990 0.611 1.60 O14744 0.993 PRMT5 TTT batc… lower… batch3 (…
3 3.00e-3 1.65 1.19 2.30 O14744 0.0778 PRMT5 TTT exte… higer… extended…PES1
filter(uniResTab, name == "PES1")# A tibble: 3 x 11
p HR lower higher id p.adj name outcome batch dir group
<dbl> <dbl> <dbl> <dbl> <chr> <dbl> <chr> <chr> <chr> <chr> <chr>
1 3.52e-4 2.51 1.51 4.15 O00541 0.0887 PES1 TTT orig… higer… origina…
2 1.97e-2 1.87 1.11 3.17 O00541 0.390 PES1 TTT batc… higer… batch3 …
3 1.23e-5 2.20 1.55 3.14 O00541 0.00436 PES1 TTT exte… higer… extende…PYGB
filter(uniResTab, name == "PYGB")# A tibble: 4 x 11
p HR lower higher id p.adj name outcome batch dir group
<dbl> <dbl> <dbl> <dbl> <chr> <dbl> <chr> <chr> <chr> <chr> <chr>
1 4.36e-5 0.352 0.213 0.580 P11216 0.0331 PYGB TTT orig… lower… origina…
2 6.03e-2 0.595 0.346 1.02 P11216 0.951 PYGB TTT batc… lower… batch2 …
3 9.80e-2 0.700 0.459 1.07 P11216 0.579 PYGB TTT batc… lower… batch3 …
4 2.08e-5 0.516 0.380 0.700 P11216 0.00534 PYGB TTT exte… lower… extende…Compare multi-variate results
cTab.com <- select(cTab, name, outcome, p, p.adj, batch) Proteins that identified by batch1 (10%) and also show significant p-values (P<0.05) in batch 3
resB1 <- filter(cTab.com, batch == "original", p.adj < 0.1) %>%
mutate(dir = "1") %>%
select(name, dir) %>% dplyr::rename(dirB1 = dir)
resB3 <- filter(cTab.com, batch == "batch3", p < 0.05) %>%
mutate(dir = "1") %>%
select(name, dir) %>% dplyr::rename(dirB3 = dir)
resCom <- left_join(resB1, resB3, by = "name") %>%
filter(dirB1 == dirB3)List of such proteins
resCom# A tibble: 3 x 3
name dirB1 dirB3
<chr> <chr> <chr>
1 PURA 1 1
2 CDKN1B 1 1
3 MTCH2 1 1 Proteins that identified by batch1&3 (10%) and also show significant p-values (P<0.05) in batch 2
resB13 <- filter(cTab.com, batch == "extended", p.adj < 0.1) %>%
mutate(dir = "1") %>%
select(name, dir) %>% dplyr::rename(dirB13 = dir)
resB2 <- filter(cTab.com, batch == "batch2", p < 0.05) %>%
mutate(dir = "1") %>%
select(name, dir) %>% dplyr::rename(dirB2 = dir)
resCom <- left_join(resB13, resB2, by = "name") %>%
filter(dirB13 == dirB2)List of such proteins
resCom# A tibble: 11 x 3
name dirB13 dirB2
<chr> <chr> <chr>
1 PURA 1 1
2 MTCH2 1 1
3 NOP2 1 1
4 AP3B1 1 1
5 PSMD5 1 1
6 MRPL50 1 1
7 PPA1 1 1
8 PNPT1 1 1
9 FIS1 1 1
10 NFKB1 1 1
11 LRRC59 1 1 Reproducibility of the candidates mentioned in the manuscript
PRMT5
filter(cTab.com, name == "PRMT5")# A tibble: 2 x 5
name outcome p p.adj batch
<chr> <chr> <dbl> <dbl> <chr>
1 PRMT5 TTT 0.000185 0.000598 original
2 PRMT5 TTT 0.0152 0.0210 extendedPES1
filter(cTab.com, name == "PES1")# A tibble: 2 x 5
name outcome p p.adj batch
<chr> <chr> <dbl> <dbl> <chr>
1 PES1 TTT 0.0000309 0.000204 original
2 PES1 TTT 0.00000940 0.000261 extendedPYGB
filter(cTab.com, name == "PYGB")# A tibble: 3 x 5
name outcome p p.adj batch
<chr> <chr> <dbl> <dbl> <chr>
1 PYGB TTT 0.0000142 0.000204 original
2 PYGB TTT 0.417 0.719 batch2
3 PYGB TTT 0.0000161 0.000306 extended
sessionInfo()R version 4.0.2 (2020-06-22)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS 10.16
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] forcats_0.5.1 stringr_1.4.0 dplyr_1.0.5 purrr_0.3.4
[5] readr_1.4.0 tidyr_1.1.3 tibble_3.1.0 ggplot2_3.3.3
[9] tidyverse_1.3.0 UpSetR_1.4.0
loaded via a namespace (and not attached):
[1] Rcpp_1.0.6 lubridate_1.7.10 assertthat_0.2.1 rprojroot_2.0.2
[5] digest_0.6.27 utf8_1.1.4 R6_2.5.0 cellranger_1.1.0
[9] plyr_1.8.6 backports_1.2.1 reprex_1.0.0 evaluate_0.14
[13] highr_0.8 httr_1.4.2 pillar_1.5.1 rlang_0.4.10
[17] readxl_1.3.1 rstudioapi_0.13 jquerylib_0.1.3 rmarkdown_2.7
[21] labeling_0.4.2 munsell_0.5.0 broom_0.7.5 compiler_4.0.2
[25] httpuv_1.5.5 modelr_0.1.8 xfun_0.21 pkgconfig_2.0.3
[29] htmltools_0.5.1.1 tidyselect_1.1.0 gridExtra_2.3 workflowr_1.6.2
[33] fansi_0.4.2 crayon_1.4.1 dbplyr_2.1.0 withr_2.4.1
[37] later_1.1.0.1 grid_4.0.2 jsonlite_1.7.2 gtable_0.3.0
[41] lifecycle_1.0.0 DBI_1.1.1 git2r_0.28.0 magrittr_2.0.1
[45] scales_1.1.1 cli_2.3.1 stringi_1.5.3 farver_2.1.0
[49] fs_1.5.0 promises_1.2.0.1 xml2_1.3.2 bslib_0.2.4
[53] ellipsis_0.3.1 generics_0.1.0 vctrs_0.3.6 tools_4.0.2
[57] glue_1.4.2 hms_1.0.0 yaml_2.2.1 colorspace_2.0-0
[61] rvest_1.0.0 knitr_1.31 haven_2.3.1 sass_0.3.1