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* checking for file ‘BloodCancerMultiOmics2017/DESCRIPTION’ ... OK
* preparing ‘BloodCancerMultiOmics2017’:
* checking DESCRIPTION meta-information ... OK
* installing the package to build vignettes
* creating vignettes ... ERROR
--- re-building ‘BloodCancerMultiOmics2017-dataOverview.Rmd’ using rmarkdown
--- finished re-building ‘BloodCancerMultiOmics2017-dataOverview.Rmd’

--- re-building ‘BloodCancerMultiOmics2017.Rmd’ using rmarkdown











``` r
options(stringsAsFactors=FALSE)
```


# Associations of drug responses with mutations in CLL (IGHV not included)

In this part, we use both gene mutations and chromosome aberrations to test for gene-drug response associations. In contrast to the analysis done previously, we exclude IGHV status from testing. Additionally, we use information on patient treatment status to account for its effect on drug response screening.

## Additional functions

Accessor functions:

``` r
# get drug responsee data
get.drugresp <- function(lpd) {
  drugresp = t(Biobase::exprs(lpd[fData(lpd)$type == 'viab'])) %>%
    dplyr::tbl_df() %>% dplyr::select(-ends_with(":5")) %>%
    dplyr::mutate(ID = colnames(lpd)) %>%
    tidyr::gather(drugconc, viab, -ID) %>%
    dplyr::mutate(drug = drugs[substring(drugconc, 1, 5), "name"],
           conc = sub("^D_([0-9]+_)", "", drugconc)) %>%
    dplyr::mutate(conc = as.integer(gsub("D_CHK_", "", conc)))
  
  drugresp
}

# extract mutations and IGHV status
get.somatic <- function(lpd) {
  somatic = t(Biobase::exprs(lpd[Biobase::fData(lpd)$type == 'gen' | 
                                   Biobase::fData(lpd)$type == 'IGHV']))
  ## rename IGHV Uppsala to 'IGHV' (simply)
  colnames(somatic)[grep("IGHV", colnames(somatic))] = "IGHV"
  
  ## at least 3 patients should have this mutation
  min.samples = which(Matrix::colSums(somatic, na.rm = T) > 2)
  somatic = dplyr::tbl_df(somatic[, min.samples]) %>%
    dplyr::select(-one_of("del13q14_bi", "del13q14_mono", 
                          "Chromothripsis", "RP11-766F14.2")) %>%
    dplyr::rename(del13q14 = del13q14_any) %>% 
    dplyr::mutate(ID = colnames(lpd)) %>%
    tidyr::gather(mutation, mut.value, -ID)
  somatic
}
```

Define the ggplot themes

``` r
t1<-theme(                              
  plot.background = element_blank(), 
  panel.grid.major = element_line(),
  panel.grid.major.x = element_line(linetype = "dotted", colour = "grey"),
  panel.grid.minor = element_blank(), 
  panel.border = element_blank(), 
  panel.background = element_blank(),
  axis.line = element_line(size=.4),
  axis.line.x = element_line(),
  axis.line.y = element_line(),
  axis.text.x  = element_text(angle=90, size=12, 
                              face="bold", hjust = 1, vjust = 0.4),
  axis.text.y = element_text(size = 14),
  axis.ticks.x = element_line(linetype = "dotted"),
  axis.ticks.length = unit(0.3,"cm"),
  axis.title.x = element_text(face="bold", size=16), 
  axis.title.y = element_text(face="bold", size=20),
  plot.title = element_text(face="bold", size=16, hjust = 0.5)
)

## theme for the legend
t.leg <-  theme(legend.title = element_text(face='bold', 
                                            hjust = 1, size=11),
                legend.position = c(0, 0.76),
                legend.key = element_blank(),
                legend.text = element_text(size=12),
                legend.background = element_rect(color = "black"))
```

Define the main color palette: 

``` r
colors= c("#015872","#3A9C94","#99977D","#ffbf00","#5991C7","#99cc00",
          "#D5A370","#801416","#B2221C","#ff5050","#33bbff","#5c5cd6",
          "#E394BB","#0066ff","#C0C0C0")
```

Get pretreatment status:

``` r
get.pretreat <- function(patmeta, lpd) {
  patmeta = patmeta[rownames(patmeta) %in% colnames(lpd),]
  data.frame(ID=rownames(patmeta), pretreat=!patmeta$IC50beforeTreatment) %>% 
    mutate(pretreat = as.factor(pretreat))
  
}
```

Merge drug response, pretreatment information and somatic mutation data sets

``` r
make.dr <- function(resp, features, patmeta, lpd) {
  treat = get.pretreat(patmeta, lpd)
  dr = full_join(resp, features) %>% 
    inner_join(treat) 
}
```

Summarize viabilities using Tukey's medpolish

``` r
get.medp <- function(drugresp) {
  tab = drugresp %>% group_by(drug, conc) %>% 
    do(data.frame(v = .$viab, ID = .$ID)) %>% spread(ID, v)
  
  med.p = foreach(n=unique(tab$drug), .combine = cbind) %dopar% {
    tb = filter(tab, drug == n) %>% ungroup() %>% dplyr::select(-(drug:conc)) %>% 
      as.matrix %>% `rownames<-`(1:5)
    mdp = stats::medpolish(tb)
    df = as.data.frame(mdp$col) + mdp$overall
    colnames(df) <- n
    df
  }
  
  medp.viab = dplyr::tbl_df(med.p) %>% dplyr::mutate(ID = rownames(med.p)) %>%
    tidyr::gather(drug, viab, -ID) 
  medp.viab
}
```

Process labels for the legend:

``` r
get.labels <- function(pvals) {
  lev = levels(factor(pvals$mutation))
  lev = gsub("^(gain)([0-9]+)([a-z][0-9]+)$", "\\1(\\2)(\\3)", lev)
  lev =  gsub("^(del)([0-9]+)([a-z].+)$", "\\1(\\2)(\\3)", lev)
  lev = gsub("trisomy12", "trisomy 12", lev)
  lev
}
```

Get order of mutations

``` r
get.mutation.order <- function(lev) {
  ord = c("trisomy 12", "TP53",
          "del(11)(q22.3)", "del(13)(q14)",
          "del(17)(p13)",
          "gain(8)(q24)",
          "BRAF", "CREBBP", "PRPF8",
          "KLHL6", "NRAS", "ABI3BP", "UMODL1")
  mut.order = c(match(ord, lev),
                grep("Other", lev), grep("Below", lev))
  
  mut.order
}
```

Group drugs by pathway/target

``` r
get.drug.order <- function(pvals, drugs) {
  ## determine drug order by column sums of log-p values
  dr.order = pvals %>% 
    mutate(logp = -log10(p.value)) %>% 
    group_by(drug) %>% summarise(logsum = sum(logp)) 
  
  dr.order = inner_join(dr.order, pvals %>%
                          group_by(drug) %>% 
                          summarise(n = length(unique(mutation)))) %>% 
    arrange(desc(n), desc(logsum))
  
  dr.order = inner_join(dr.order, drugs %>% rename(drug = name))
  
  dr.order = left_join(dr.order, dr.order %>% 
                         group_by(`target_category`) ) %>%
    arrange(`target_category`, drug) %>%
    filter(! `target_category` %in% c("ALK", "Angiogenesis", "Other")) %>%
    filter(!is.na(`target_category`))
  
  dr.order
}
```

Add pathway annotations for selected drug classes

``` r
make.annot <- function(g, dr.order) {
  # make a color palette for drug pathways
  drug.class = c("#273649", "#647184", "#B1B2C8",
                 "#A7755D", "#5D2E1C", "#38201C")
  pathways = c("BH3","B-cell receptor","DNA damage",
               "MAPK", "PI3K", "Reactive oxygen species")
  names(pathways) = c("BH3", "BCR inhibitors", "DNA damage",
                      "MAPK", "PI3K", "ROS")
  
  for (i in 1:6) {
    prange = grep(pathways[i], dr.order$`target_category`)
    path.grob <- grobTree(rectGrob(gp=gpar(fill=drug.class[i])),
                          textGrob(names(pathways)[i], 
                                   gp = gpar(cex =0.8, col = "white")))
    g = g + 
      annotation_custom(path.grob, 
                        xmin = min(prange) -0.25 - 0.1 * ifelse(i == 2, 1, 0), 
                        xmax = max(prange) + 0.25 + 0.1 * ifelse(i == 2, 1, 0), 
                        ymin = -0.52, ymax = -0.2)
  }
  g
}
```

Define a function for `glegend`

``` r
g_legend<-function(a.gplot){
  tmp <- ggplot_gtable(ggplot_build(a.gplot))
  leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box")
  legend <- tmp$grobs[[leg]]
  legend
} ## end define
```

## Data setup

Load the data.

``` r
data(list=c("conctab", "drugs", "lpdAll", "patmeta"))
```

Get drug response data.


































Quitting from ./src/part05.Rmd:248-252 [preprocesslpd]
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
<error/lifecycle_error_deprecated>
Error:
! `tbl_df()` was deprecated in dplyr 1.0.0 and is now defunct.
ℹ Please use `tibble::as_tibble()` instead.
---
Backtrace:
     ▆
  1. ├─global get.drugresp(lpdCLL)
  2. │ └─... %>% ...
  3. ├─dplyr::mutate(., conc = as.integer(gsub("D_CHK_", "", conc)))
  4. ├─dplyr::mutate(...)
  5. ├─tidyr::gather(., drugconc, viab, -ID)
  6. ├─dplyr::mutate(., ID = colnames(lpd))
  7. ├─dplyr::select(., -ends_with(":5"))
  8. └─dplyr::tbl_df(.)
  9.   └─lifecycle::deprecate_stop("1.0.0", "tbl_df()", "tibble::as_tibble()")
 10.     └─lifecycle:::deprecate_stop0(msg)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~


Quitting from BloodCancerMultiOmics2017.Rmd:153-154 [p05]
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
<error/lifecycle_error_deprecated>
Error:
! `tbl_df()` was deprecated in dplyr 1.0.0 and is now defunct.
ℹ Please use `tibble::as_tibble()` instead.
---
Backtrace:
     ▆
  1. ├─global get.drugresp(lpdCLL)
  2. │ └─... %>% ...
  3. ├─dplyr::mutate(., conc = as.integer(gsub("D_CHK_", "", conc)))
  4. ├─dplyr::mutate(...)
  5. ├─tidyr::gather(., drugconc, viab, -ID)
  6. ├─dplyr::mutate(., ID = colnames(lpd))
  7. ├─dplyr::select(., -ends_with(":5"))
  8. └─dplyr::tbl_df(.)
  9.   └─lifecycle::deprecate_stop("1.0.0", "tbl_df()", "tibble::as_tibble()")
 10.     └─lifecycle:::deprecate_stop0(msg)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Error: processing vignette 'BloodCancerMultiOmics2017.Rmd' failed with diagnostics:
`tbl_df()` was deprecated in dplyr 1.0.0 and is now defunct.
ℹ Please use `tibble::as_tibble()` instead.
--- failed re-building ‘BloodCancerMultiOmics2017.Rmd’

SUMMARY: processing the following file failed:
  ‘BloodCancerMultiOmics2017.Rmd’

Error: Vignette re-building failed.
Execution halted