Contents

1 Getting started

library(lattice)
library(knitr)
library(parallel)
library(NestLink)

cv <- 1 - 1:7 / 10
t <- trellis.par.get("strip.background")
t$col <- (rgb(cv,cv,cv))
trellis.par.set("strip.background", t)
n.simulation <- 10

2 Load data

# filename <- system.file("extdata/PGexport2_normalizedAgainstSBstandards_Peptides.csv",
#                        package = "NestLink")
# library(ExperimentHub)
# eh <- ExperimentHub()
# filename <- query(eh, c("NestLink", "PGexport2_normalizedAgainstSBstandards_Peptides.csv"))[[1]]

filename <- getExperimentHubFilename("PGexport2_normalizedAgainstSBstandards_Peptides.csv")
P <- read.csv(filename,
              header = TRUE, sep=';')
dim(P)
## [1] 721  27

2.1 Clean

remove modifications

P <- P[P$Modifications == '', ]
dim(P)
## [1] 697  27

select rows

P <- P[,c('Accession', 'Sequence', "X20170919_05_62465_nl5idx1.3_6titratecoli",
          "X20170919_16_62465_nl5idx1.3_6titratecoli", 
          "X20170919_09_62466_nl5idx1.3_7titratesmeg",
          "X20170919_14_62466_nl5idx1.3_7titratesmeg")]
dim(P)
## [1] 697   6

rename column names

names(P)<-c('Accession','Sequence','coli1', 'coli2', 'smeg1', 'smeg2')
dim(P)
## [1] 697   6

remove all rows with invalid identidfier

P<- P[grep("^P[0-9][A-Z][0-9]", P$Accession), ] 

P<-droplevels(P)

add flycode set annotation

P$ConcGr <- NA

P$ConcGr[P$Accession %in% c('P1A4', 'P1B4', 'P1C4', 'P1D4', 'P1E4', 'P1F4')] <- 92

P$ConcGr[P$Accession %in% c('P1A5', 'P1B5', 'P1C5', 'P1D5', 'P1G4', 'P1H4')] <- 295

P$ConcGr[P$Accession %in% c('P1A6', 'P1B6', 'P1E5', 'P1F5', 'P1G5', 'P1H5')] <- 943

P$ConcGr[P$Accession %in% c('P1C6', 'P1D6', 'P1E6', 'P1F6', 'P1G6', 'P1H6')] <- 3017

2.2 Sanity check

table(P$ConcGr)
## 
##   92  295  943 3017 
##   82  122  135  165
Pabs <- P
table(P$Accession)
## 
## P1A4 P1A5 P1A6 P1B4 P1B5 P1B6 P1C4 P1C5 P1C6 P1D4 P1D5 P1D6 P1E4 P1E5 P1E6 P1F4 
##   11   22   24   15   22   23   16   19   26   15   16   29   13   23   30   12 
## P1F5 P1F6 P1G4 P1G5 P1G6 P1H4 P1H5 P1H6 
##   19   28   22   24   24   21   22   28

2.3 Camera ready summary table

P.summary <- aggregate(. ~ ConcGr * Accession, data=P[,c('Accession', 'coli1',
    'coli2', 'smeg1', 'smeg2', 'ConcGr')], FUN=sum)
P.summary$nDetectedFlycodes <- aggregate(Sequence ~ ConcGr * Accession,
    data=na.omit(P), FUN=length)[,3]
P.summary$nTotalFlycodes <- 30
P.summary$coverage <- round(100 * P.summary$nDetectedFlycodes  / P.summary$nTotalFlycodes)
kable(P.summary[order(P.summary$ConcGr),], row.names = FALSE)
ConcGr Accession coli1 coli2 smeg1 smeg2 nDetectedFlycodes nTotalFlycodes coverage
92 P1A4 3473996 3670152 3524234 3629077 11 30 37
92 P1B4 3972002 3951215 3685668 3632472 15 30 50
92 P1C4 5582053 5716917 5700899 5623891 16 30 53
92 P1D4 5188468 5642192 5245252 5281381 15 30 50
92 P1E4 4697745 4824760 5051017 5146804 13 30 43
92 P1F4 3968239 4026336 4246879 4251951 12 30 40
295 P1A5 23970332 24546550 26479936 25950084 22 30 73
295 P1B5 16033017 16352277 17209165 17490691 22 30 73
295 P1C5 24016078 24734329 22810916 22589773 19 30 63
295 P1D5 17658145 17864382 17760463 17773297 16 30 53
295 P1G4 16016015 16618336 18270569 19036154 22 30 73
295 P1H4 19519544 20301903 20111340 20174474 21 30 70
943 P1A6 65538576 67507722 66123186 66400185 24 30 80
943 P1B6 55078431 58048226 50652047 50278919 23 30 77
943 P1E5 60305499 62836186 62173846 61952054 23 30 77
943 P1F5 46800670 49088967 47748401 47930977 19 30 63
943 P1G5 54312219 55033298 51968903 51106582 24 30 80
943 P1H5 59931716 61370897 55971370 56588298 22 30 73
3017 P1C6 168463728 180586807 158342929 158748952 26 30 87
3017 P1D6 159828074 163103139 140519542 138765621 29 30 97
3017 P1E6 166626467 176697478 159850632 161744675 30 30 100
3017 P1F6 191955804 203793127 181359940 182939796 28 30 93
3017 P1G6 183248427 189386707 177588746 178178834 24 30 80
3017 P1H6 181057205 185326019 161715001 152753819 28 30 93
# write.csv(P.summary, file='Figs/FigureS6b.csv')

The following function defines the computation and rendering of the by the paris function called panels.

panel.cor <- function(x, y, digits = 2, prefix = "", cex.cor, ...)
{
    usr <- par("usr"); on.exit(par(usr))
    par(usr = c(0, 1, 0, 1))
    r <- abs(cor(x, y))
    txt <- format(c(r, 0.123456789), digits = digits)[1]
    txt <- paste0(prefix, txt)
    if(missing(cex.cor)) cex.cor <- 0.8/strwidth(txt)
    text(0.5, 0.5, txt, cex = cex.cor * r)
}
rv <-lapply(unique(P$ConcGr), function(q){
pairs((P[P$ConcGr == q ,c('coli1', 'coli2', 'smeg1', 'smeg2')]),
      pch=16, col=rgb(0.5,0.5,0.5,alpha = 0.3),
      lower.panel = panel.cor,
      asp=1,
      main=q)
})

2.4 Define FCfill2max

to conduct a fair random experiment we add dummy flycodes to the input data.frame.

FCfill2max <- function(P, n = max(table(P$Accession))){
   for (i in unique(P$Accession)){
     idx <- which(P$Accession == i)
     # determine the number of missing rows for Accession i
     ndiff <- n - length(idx)
     
     if(length(idx) < n){
       cand <- P[idx[1], ]  
       cand[,2 ] <- "xxx"
       cand[,3:6 ] <- NA
       
       for (j in 1:ndiff){
         P <- rbind(P, cand)
       }
     }
   }
  P
}

2.5 Plot compute CVs for each row

P.mean <- apply(P[, c('coli1', 'coli2', 'smeg1', 'smeg2')],1, FUN=mean)
P.sd <- apply(P[, c('coli1', 'coli2', 'smeg1', 'smeg2')],1, FUN=sd)

boxplot(100*P.sd/P.mean ~ P$ConcGr,log='y', ylab='CV%')

P <- FCfill2max(P)

3 Absolute Quantification

3.1 Define function FlycodeAbsoluteStatistics using coli1 column

FlycodeAbsoluteStatistics <- function(P){
  
  PP <- aggregate(P$coli1 ~ P$Accession + P$ConcGr, FUN=sum)
  
  names(PP) <- c('Accession', 'ConcGr', 'coli1_sum')
  PPP <- aggregate(PP$coli1_sum ~ PP$ConcGr, FUN=mean)

  P.mean <- aggregate(PP$coli1_sum ~ PP$ConcGr, FUN=mean)
  P.sd <- aggregate(PP$coli1_sum ~ PP$ConcGr, FUN=sd)
  P.cv <- aggregate(PP$coli1_sum ~ PP$ConcGr,
      FUN = function(x){ 100 * sd(x) / mean(x) })
  P.length <- max(aggregate(P$coli1 ~ P$Accession, FUN=length)[,2])
 
 rv <- data.frame(ConcGr=P.sd[,1],
                  mean=P.mean[,2],
                  sd=P.sd[,2],
                  cv=round(P.cv[,2],2))
 rv$nFCAccession <-  P.length
 rv
}

3.2 Camera ready absolute table

kable(FlycodeAbsoluteStatistics(P))
ConcGr mean sd cv nFCAccession
92 4480417 811894.8 18.12 30
295 19535522 3685706.9 18.87 30
943 56994519 6440051.4 11.30 30
3017 175196618 12124519.7 6.92 30

3.3 Define FCrandom

# TODO(cp); make it work for n = 0
FCrandom <- function(P, n = 1){
  if(n == 0){
    return (P)
  }
  for (i in unique(P$Accession)){
    idx <- which(P$Accession == i)
    stopifnot(length(idx) >= n)
    smp <- sample(length(idx), n)
    P <- P[-idx[smp],]
  }
  P$n <- n
  P
}

3.4 Conduct the random experiment

set.seed(8)
S <- do.call('rbind', lapply(1:29, function(i){
  FlycodeAbsoluteStatistics(FCrandom(P, i))
  }))
xyplot(cv ~ nFCAccession | ConcGr, 
       data = S, 
       layout = c(4, 1),
       strip = strip.custom(strip.names = TRUE, strip.levels = TRUE)
       )

set.seed(1)

S.rep <- do.call('rbind', 
    lapply(1:n.simulation, function(s){
       S <- do.call('rbind', 
           lapply(1:29, function(i){
                FlycodeAbsoluteStatistics(FCrandom(P, i))
             }))
       }))

3.5 Camera ready plot of absolute flycode simulation

NestLink_absolute_flycode_simulation <- xyplot(cv ~ nFCAccession |  ConcGr,
       data = S.rep,
       panel = function(x,y,...){
         panel.abline(h=c(10, 50), col='red')
         panel.xyplot(x, y, ...)
         xy.median <- (aggregate(y, by=list(x), FUN=median, na.rm = TRUE))
         xy.quantile <- aggregate(y, by=list(x), FUN=function(d){quantile(d, c(0.05, 0.5, 0.95), na.rm = TRUE)})
         panel.points( xy.median[,1], xy.median[,2], pch=16, cex=0.5)
         # print((xy.quantile[,2])[,3])
         panel.points( xy.quantile[,1],(xy.quantile[,2])[,1], pch='-')
         panel.points( xy.quantile[,1],(xy.quantile[,2])[,3], pch='-')
       },
       xlab= 'Number of flycodes',
       ylab ='CV [%]',
       strip = strip.custom(strip.names = TRUE, strip.levels = TRUE),
       scales=list(x=list(at=c(1,5,10,15,20,25,30)),
                   y=list(at=c(0,10,50,100,150,200))),
       ylim=c(0,175),
       
       pch=16,
       col=rgb(0.5, 0.5, 0.5, alpha = 0.01),
       layout = c(4,1))

print(NestLink_absolute_flycode_simulation)

# png("NestLink_absolute_flycode_simulation.png", 1200,800)
# print(NestLink_absolute_flycode_simulation)
# dev.off()

4 Relative Quantification

4.1 Normalization

P <- na.omit(P)
P <- P[P$coli1 >0,]


P$coli1 <- P$coli1 / sum(P$coli1)
P$coli2 <- P$coli2 / sum(P$coli2)
P$smeg1 <- P$smeg1 / sum(P$smeg1)
P$smeg2 <- P$smeg2 / sum(P$smeg2)

sanity check

sum(P$coli1)
## [1] 1
sum(P$coli2)
## [1] 1
sum(P$smeg1)
## [1] 1
sum(P$smeg2)
## [1] 1

4.2 Define ratios

P$ratios <- (0.5* (P$coli1 + P$coli2)) / (0.5 * (P$smeg1 + P$smeg2))
b <- boxplot(P$coli1 / P$coli2, P$coli1 / P$smeg1, P$coli1 / P$smeg2,P$coli2 / P$smeg1, P$coli2 / P$smeg2 , P$smeg1 / P$smeg2, P$ratios, 
        ylab='ratios', ylim = c(0,2 ))
axis(1, 1:6, c('coli[12]', 'coli1-smeg1', 'coli1-smeg2', 'coli2-smeg1', 'coli2- smeg2','smeg[12]'))

op <- par(mfrow = c(1, 4))
boxplot(P$coli1 ~ P$ConcGr)
boxplot(P$coli2 ~ P$ConcGr)
boxplot(P$smeg1 ~ P$ConcGr)
boxplot(P$smeg2 ~ P$ConcGr)

4.3 Determine outliers (removal)

op <- par(mfrow=c(1,1), mar=c(5,5,5,2) )

b <- boxplot(df<-cbind(P$coli1/P$coli2, P$coli1/P$smeg1, P$coli1/P$smeg2, P$coli2/P$smeg1, P$coli2/P$smeg2, P$smeg1/P$smeg2, P$ratios),
             log='y',
        ylab='normalized ratios',
        #ylim = c(0, 2),
        axes=FALSE,
        main=paste("ConcGr = all"))
axis(1, 1:7, c('coli[12]', 'coli1-smeg1', 'coli1-smeg2', 'coli2-smeg1', 'coli2- smeg2','smeg[12]', 'ratio'))
abline(h=1, col='red')
box()
axis(2)
#axis(3, 1:7, b$n)
outliers.idx <- sapply(1:length(b$group),
    function(i){
      q <- df[, b$group[i]] == b$out[i];
      text(b$group[i], b$out[i], P[q, 2], pos=4, cex=0.4);
      text(b$group[i], b$out[i], P[q, 1], pos=2, cex=0.4);
      which(q)})

b <- boxplot(df<-cbind(P$coli1/P$coli2, P$coli1/P$smeg1, P$coli1/P$smeg2, P$coli2/P$smeg1, P$coli2/P$smeg2, P$smeg1/P$smeg2, P$ratio),
             log='',
        ylab='normalized ratios',
        ylim = c(0, 2),
        axes=FALSE,
        main=paste("ConcGr = all"))
axis(1, 1:7, c('coli[12]', 'coli1-smeg1', 'coli1-smeg2', 'coli2-smeg1', 'coli2- smeg2','smeg[12]', 'ratio'))
abline(h=1, col='red')
box()
axis(2)
axis(3, 1:length(b$n), b$n)
outliers.idx <- sapply(1:length(b$group),
    function(i){
      q <- df[, b$group[i]] == b$out[i];
      text(b$group[i], b$out[i], P[q, 2], pos=4, cex=0.4);
      text(b$group[i], b$out[i], P[q, 1], pos=2, cex=0.4);
      which(q)})

kable(P[unique(outliers.idx),])
Accession Sequence coli1 coli2 smeg1 smeg2 ConcGr ratios
38 P1H6 GSSEDDAEGWLR 0.0000121 0.0000003 0.0000001 0.0000003 3017 31.1061717
61 P1F6 GSPAADDVSWQSR 0.0000135 0.0000090 0.0000103 0.0000099 3017 1.1148184
69 P1F6 GSGTAEESYWQEGGR 0.0000287 0.0000086 0.0000209 0.0000089 3017 1.2538971
105 P1C6 GSNDPEVDGWLTVR 0.0000133 0.0000093 0.0000168 0.0000153 3017 0.7040914
115 P1D6 GSELAPSVGWQEGGR 0.0000604 0.0000103 0.0000095 0.0000101 3017 3.6002928
117 P1D6 GSAAVAPNVWR 0.0000051 0.0000007 0.0000020 0.0000013 3017 1.7807121
119 P1D6 GSDTTSVDTWQEGGR 0.0000311 0.0000110 0.0000386 0.0000396 3017 0.5385343
126 P1D6 GSVSTYDPVWR 0.0062807 0.0049329 0.0053530 0.0057151 3017 1.0131416
169 P1G6 GSEPVADADWQSR 0.0000282 0.0000066 0.0000275 0.0000121 3017 0.8813330
216 P1A6 GSANTVEPGWQSR 0.0000027 0.0000019 0.0000096 0.0000103 943 0.2370577
270 P1G5 GSPPDVFSTWQEGGR 0.0000212 0.0000015 0.0000046 0.0000041 943 2.6019477
303 P1H4 GSDGAVADSWLTVR 0.0003608 0.0002935 0.0003736 0.0003876 295 0.8596582
307 P1H4 GSEAVVTVDWLR 0.0000450 0.0000649 0.0000700 0.0000755 295 0.7553914
347 P1G4 GSETTYYVDWQSR 0.0002854 0.0002333 0.0002281 0.0002364 295 1.1168094
348 P1G4 GSAVWEPDYWLR 0.0000360 0.0000473 0.0002191 0.0002166 295 0.1911726
349 P1G4 GSPDLADDVWLTVR 0.0002689 0.0001850 0.0002258 0.0001999 295 1.0662495
352 P1G4 GSQENGGADWQSR 0.0000062 0.0000156 0.0000256 0.0000313 295 0.3831785
355 P1H5 GSSVGQAVEWQEGGR 0.0000634 0.0000139 0.0000543 0.0000162 943 1.0965878
452 P1D5 GSDEPAYAVWLR 0.0000139 0.0000110 0.0000139 0.0000136 295 0.9059102
468 P1C4 GSADVTVGLWR 0.0000098 0.0000079 0.0000099 0.0000097 92 0.9012831
508 P1B4 GSVVTVGETWQSR 0.0001717 0.0001229 0.0001143 0.0001188 92 1.2636110
511 P1B4 GSVAAVVPDWR 0.0000332 0.0000252 0.0000361 0.0000342 92 0.8299291
527 P1E4 GSDTEADPEWLTVR 0.0001087 0.0000872 0.0001407 0.0001372 92 0.7051292
551 P1F4 GSVEGDVETWLTVR 0.0000389 0.0000587 0.0000522 0.0000428 92 1.0273511
23 P1E6 GSEVVPDTVWR 0.0011784 0.0010995 0.0005598 0.0005781 3017 2.0017582
28 P1E6 GSTEVAEVAWLR 0.0002027 0.0001978 0.0001375 0.0001396 3017 1.4453411
29 P1E6 GSTDEAAFAWQSR 0.0000664 0.0000622 0.0000367 0.0000401 3017 1.6748641
30 P1E6 GSWYEVDGTWLTVR 0.0000208 0.0000258 0.0001270 0.0001108 3017 0.1960714
56 P1H6 GSVEYTTAAWR 0.0000943 0.0000880 0.0000622 0.0000537 3017 1.5729054
83 P1F6 GSPWEGEAAWQSR 0.0002075 0.0001782 0.0001289 0.0001387 3017 1.4418672
111 P1C6 GSGEPSAYSWR 0.0001660 0.0001797 0.0000974 0.0000950 3017 1.7973706
112 P1C6 GSEVTEEVVWQSR 0.0004082 0.0003699 0.0002116 0.0001778 3017 1.9980931
163 P1E5 GSVWDGSVDWLR 0.0003851 0.0003446 0.0008695 0.0007912 943 0.4394173
166 P1E5 GSTAATEAAWLR 0.0000796 0.0000764 0.0000434 0.0000487 943 1.6937723
236 P1A6 GSDPPAVVGWR 0.0000216 0.0000268 0.0000141 0.0000164 943 1.5894671
255 P1B6 GSTDDTVTVWR 0.0001116 0.0001050 0.0000642 0.0000718 943 1.5927951
256 P1B6 GSTTPPLLVWQEGGR 0.0002999 0.0002926 0.0001905 0.0001922 943 1.5481110
259 P1B6 GSVAATEELWLTVR 0.0000609 0.0000537 0.0000328 0.0000276 943 1.8979393
285 P1G5 GSFFSYQGDWLR 0.0000119 0.0000127 0.0000717 0.0000768 943 0.1652766
371 P1H5 GSYTDALEVWLR 0.0000306 0.0000357 0.0002221 0.0002426 943 0.1427107
374 P1H5 GSFVGGGGWWR 0.0000264 0.0000315 0.0000589 0.0000574 943 0.4976272
397 P1B5 GSEDDGDVGWQEGGR 0.0000121 0.0000131 0.0000343 0.0000351 295 0.3621272
451 P1D5 GSVTETASTWQEGGR 0.0000333 0.0000292 0.0000219 0.0000234 295 1.3821678
530 P1E4 GSWLATPDVWLR 0.0000068 0.0000085 0.0000325 0.0000314 92 0.2390561
571 P1A4 GSPGTVWYDWR 0.0000187 0.0000163 0.0000423 0.0000412 92 0.4179539
21 P1E6 GSSPVEDTSWLR 0.0008161 0.0007886 0.0006707 0.0005384 3017 1.3272331
54 P1H6 GSAAPVSAVWQSR 0.0002341 0.0002321 0.0001699 0.0001553 3017 1.4331093
95 P1C6 GSVDVGSAVWQSR 0.0040710 0.0038983 0.0029346 0.0027473 3017 1.4025928
104 P1C6 GSVVASVEAWQSR 0.0010448 0.0009853 0.0008741 0.0006633 3017 1.3204326
184 P1G6 GSSAEDYAVWQEGGR 0.0024441 0.0023660 0.0018494 0.0016301 3017 1.3823930
467 P1C4 GSQSVDTTVWR 0.0000086 0.0000094 0.0000082 0.0000058 92 1.2894310
572 P1A4 GSSTGTVTPWQSR 0.0001916 0.0002032 0.0001307 0.0001230 92 1.5565379
213 P1F5 GSVETETAYWQEGGR 0.0000315 0.0000338 0.0000247 0.0000222 943 1.3915324
8 P1E6 GSVSEGEDTWQEGGR 0.0000133 0.0000128 0.0000119 0.0000152 3017 0.9595206
31 P1H6 GSVATDVPDWQEGGR 0.0233676 0.0221316 0.0198411 0.0166058 3017 1.2483702
35 P1H6 GSPDTVEYDWQSR 0.0129522 0.0136876 0.0123987 0.0101462 3017 1.1816328
39 P1H6 GSGTYVSDDWR 0.0046986 0.0040630 0.0038690 0.0047031 3017 1.0221084
62 P1F6 GSPTGTDPVWLR 0.0084956 0.0081820 0.0081703 0.0067783 3017 1.1156570
64 P1F6 GSVDAEPTVWQSR 0.0070020 0.0071016 0.0060481 0.0052024 3017 1.2535815
113 P1D6 GSTAATELEWQEGGR 0.0178374 0.0173241 0.0158409 0.0133260 3017 1.2055235
121 P1D6 GSYATGAEPWR 0.0031632 0.0030784 0.0028792 0.0035680 3017 0.9681174
122 P1D6 GSPQLAPDGWR 0.0000541 0.0000579 0.0000554 0.0000462 3017 1.1025485
208 P1F5 GSEVATTAVWQEGGR 0.0005998 0.0005649 0.0005134 0.0004285 943 1.2364966
250 P1B6 GSAADDGYSWLR 0.0007042 0.0006728 0.0006125 0.0004923 943 1.2463635
268 P1G5 GSASENDEDWLTVR 0.0032788 0.0030958 0.0028999 0.0024500 943 1.1915266
292 P1H4 GSVVDGNVTWR 0.0003710 0.0003775 0.0003789 0.0003051 295 1.0943579
329 P1A5 GSVATAYESWQSR 0.0000360 0.0000295 0.0000406 0.0000304 295 0.9217020
342 P1G4 GSPDVVGTAWQEGGR 0.0003646 0.0003627 0.0004204 0.0003465 295 0.9483575
382 P1B5 GSVEPEADVWR 0.0004167 0.0004184 0.0004865 0.0004043 295 0.9374374
406 P1C5 GSAAVPGGVWQEGGR 0.0012621 0.0012378 0.0012112 0.0010131 295 1.1239207
442 P1D5 GSETSGYDVWQSR 0.0007695 0.0007892 0.0007024 0.0006113 295 1.1864482
453 P1C4 GSAVVPDADWQSR 0.0005392 0.0005215 0.0004984 0.0003983 92 1.1829564
rv <-lapply(unique(P$ConcGr), function(q){
pairs((P[P$ConcGr == q ,c('coli1', 'coli2', 'smeg1', 'smeg2')]),
      pch=16, col=rgb(0.5,0.5,0.5,alpha = 0.3),
      lower.panel = panel.cor,
      asp=1,
      main=q)
})

bwplot(ratios ~ Accession | ConcGr,
       data = P,
         strip = strip.custom(strip.names = TRUE, strip.levels = TRUE),
       panel = function(...){
         
         panel.abline(h=1, col='red')
         panel.bwplot(...)
       },
        ylim=c(-0,2),
       scales = list(x = list(relation = "free", rot=45)),
       layout = c(4,1))

bwplot(ratios ~ ConcGr,
       data = P,
       horizontal = FALSE,
       panel = function(...){
         
         panel.abline(h=1, col='red')
         panel.bwplot(...)
       },
       ylim=c(0,2),
       scales = list(x = list(relation = "free", rot=45)),
       layout = c(1,1))

boxplot(ratios ~ ConcGr,data=P,ylim=c(0,2))
abline(h=1, col=rgb(1,0,0,alpha=0.4))

P<-na.omit(P)
xyplot(ratios ~ Accession |  ConcGr,
       data = P,
         strip = strip.custom(strip.names = TRUE, strip.levels = TRUE),
       panel =function(x,y, ...){
         panel.abline(h=mean(y), col='red')
          panel.xyplot(x,y, pch=16, col=rgb(0.5,0.5,0.5,alpha = 0.5))
          xy.mean <- (aggregate(y, by=list(x), FUN=mean, na.rm = TRUE))
           xy.sd <- (aggregate(y, by=list(x), FUN=sd, na.rm = TRUE))
          panel.points( xy.mean[,1], xy.mean[,2])
          panel.points( xy.mean[,1], xy.mean[,2] + xy.sd[,2] , pch='-', col='red', cex=4)
           panel.points( xy.mean[,1], xy.mean[,2] - xy.sd[,2] , pch='-', col='red', cex=4)},
        ylim=c(0,2),
       scales = list(x = list(relation = "free", rot=45)),
       layout = c(4,1))
## Warning in order(as.numeric(x)): NAs introduced by coercion
## Warning in diff(as.numeric(x[ord])): NAs introduced by coercion
## Warning in order(as.numeric(x)): NAs introduced by coercion
## Warning in diff(as.numeric(x[ord])): NAs introduced by coercion
## Warning in order(as.numeric(x)): NAs introduced by coercion
## Warning in diff(as.numeric(x[ord])): NAs introduced by coercion
## Warning in order(as.numeric(x)): NAs introduced by coercion
## Warning in diff(as.numeric(x[ord])): NAs introduced by coercion
## Warning in panel.xyplot(x, y, pch = 16, col = rgb(0.5, 0.5, 0.5, alpha = 0.5)):
## NAs introduced by coercion
## Warning in xy.coords(x, y, recycle = TRUE): NAs introduced by coercion

## Warning in xy.coords(x, y, recycle = TRUE): NAs introduced by coercion

## Warning in xy.coords(x, y, recycle = TRUE): NAs introduced by coercion
## Warning in panel.xyplot(x, y, pch = 16, col = rgb(0.5, 0.5, 0.5, alpha = 0.5)):
## NAs introduced by coercion
## Warning in xy.coords(x, y, recycle = TRUE): NAs introduced by coercion

## Warning in xy.coords(x, y, recycle = TRUE): NAs introduced by coercion

## Warning in xy.coords(x, y, recycle = TRUE): NAs introduced by coercion
## Warning in panel.xyplot(x, y, pch = 16, col = rgb(0.5, 0.5, 0.5, alpha = 0.5)):
## NAs introduced by coercion
## Warning in xy.coords(x, y, recycle = TRUE): NAs introduced by coercion

## Warning in xy.coords(x, y, recycle = TRUE): NAs introduced by coercion

## Warning in xy.coords(x, y, recycle = TRUE): NAs introduced by coercion
## Warning in panel.xyplot(x, y, pch = 16, col = rgb(0.5, 0.5, 0.5, alpha = 0.5)):
## NAs introduced by coercion
## Warning in xy.coords(x, y, recycle = TRUE): NAs introduced by coercion

## Warning in xy.coords(x, y, recycle = TRUE): NAs introduced by coercion

## Warning in xy.coords(x, y, recycle = TRUE): NAs introduced by coercion

P <- na.omit(P)
xyplot(ratios ~ Accession |  ConcGr,
       data = P,
       strip = strip.custom(strip.names = TRUE, strip.levels = TRUE),
       panel = function(x,y, ...){
         panel.abline(h=mean(y), col='red')
          panel.xyplot(x,y, pch=16, col=rgb(0.5,0.5,0.5,alpha = 0.5))
          xy.mean <- (aggregate(y, by=list(x), FUN=mean, na.rm = TRUE))
           xy.sd <- (aggregate(y, by=list(x), FUN=sd, na.rm = TRUE))
          panel.points( xy.mean[,1], xy.mean[,2])
          panel.points( xy.mean[,1], xy.mean[,2] + xy.sd[,2] , pch='-', col='red', cex=4)
           panel.points( xy.mean[,1], xy.mean[,2] - xy.sd[,2] , pch='-', col='red', cex=4)
           ltext(xy.mean[,1], (xy.mean[,2] + xy.sd[,2]) , round(xy.sd[,2],2), pos=3, cex=0.5)
           },
        
       layout = c(4,1),
       scales = list(y=list(log=TRUE),
                     x = list(relation = "free", rot=45)),
       )
## Warning in order(as.numeric(x)): NAs introduced by coercion
## Warning in diff(as.numeric(x[ord])): NAs introduced by coercion
## Warning in order(as.numeric(x)): NAs introduced by coercion
## Warning in diff(as.numeric(x[ord])): NAs introduced by coercion
## Warning in order(as.numeric(x)): NAs introduced by coercion
## Warning in diff(as.numeric(x[ord])): NAs introduced by coercion
## Warning in order(as.numeric(x)): NAs introduced by coercion
## Warning in diff(as.numeric(x[ord])): NAs introduced by coercion
## Warning in panel.xyplot(x, y, pch = 16, col = rgb(0.5, 0.5, 0.5, alpha = 0.5)):
## NAs introduced by coercion
## Warning in xy.coords(x, y, recycle = TRUE): NAs introduced by coercion

## Warning in xy.coords(x, y, recycle = TRUE): NAs introduced by coercion

## Warning in xy.coords(x, y, recycle = TRUE): NAs introduced by coercion

## Warning in xy.coords(x, y, recycle = TRUE): NAs introduced by coercion
## Warning in panel.xyplot(x, y, pch = 16, col = rgb(0.5, 0.5, 0.5, alpha = 0.5)):
## NAs introduced by coercion
## Warning in xy.coords(x, y, recycle = TRUE): NAs introduced by coercion

## Warning in xy.coords(x, y, recycle = TRUE): NAs introduced by coercion

## Warning in xy.coords(x, y, recycle = TRUE): NAs introduced by coercion

## Warning in xy.coords(x, y, recycle = TRUE): NAs introduced by coercion
## Warning in panel.xyplot(x, y, pch = 16, col = rgb(0.5, 0.5, 0.5, alpha = 0.5)):
## NAs introduced by coercion
## Warning in xy.coords(x, y, recycle = TRUE): NAs introduced by coercion

## Warning in xy.coords(x, y, recycle = TRUE): NAs introduced by coercion

## Warning in xy.coords(x, y, recycle = TRUE): NAs introduced by coercion

## Warning in xy.coords(x, y, recycle = TRUE): NAs introduced by coercion
## Warning in panel.xyplot(x, y, pch = 16, col = rgb(0.5, 0.5, 0.5, alpha = 0.5)):
## NAs introduced by coercion
## Warning in xy.coords(x, y, recycle = TRUE): NAs introduced by coercion

## Warning in xy.coords(x, y, recycle = TRUE): NAs introduced by coercion

## Warning in xy.coords(x, y, recycle = TRUE): NAs introduced by coercion

## Warning in xy.coords(x, y, recycle = TRUE): NAs introduced by coercion

## Detect outlier

# P.cv <- aggregate(P$ratios ~ P$Accession, FUN=function(x){100*sd(x)/mean(x)})
P<-na.omit(P)
trellis.par.set("strip.background",t)

xyplot(ratios ~ ConcGr | ConcGr,
       data = P,
         strip = strip.custom(strip.names = TRUE, strip.levels = TRUE),
       panel = function(x,y){
         panel.abline(h=1, col='red')
          panel.xyplot(x,y, pch=16, col=rgb(0.5,0.5,0.5,alpha = 0.5))
          panel.points(x, mean(y))
         #   panel.points(x, median(y),col='green')
          panel.points(x, mean(y) + sd(y), pch='-', col='red',cex=5)
          ltext(x, mean(y) + sd(y), round(sd(y),3), pos=4)
         
           panel.points(x, mean(y) - sd(y), pch='-', col='red',cex=5)
         },
        #ylim=c(-1,3),
       scales = list(x = list(relation = "free", rot=45)),
       layout = c(4,1))
measured ratios were plotted; red represents the sd and blue points the mean.

Figure 1: measured ratios were plotted; red represents the sd and blue points the mean

outlier.idx <- which(P$ratios > 2)
P[outlier.idx, ]
##     Accession        Sequence        coli1        coli2        smeg1
## 23       P1E6     GSEVVPDTVWR 1.178365e-03 1.099467e-03 5.598233e-04
## 38       P1H6    GSSEDDAEGWLR 1.210304e-05 3.215364e-07 1.065422e-07
## 115      P1D6 GSELAPSVGWQEGGR 6.038516e-05 1.025768e-05 9.528549e-06
## 270      P1G5 GSPPDVFSTWQEGGR 2.117599e-05 1.510987e-06 4.601046e-06
##            smeg2 ConcGr    ratios
## 23  5.780923e-04   3017  2.001758
## 38  2.928825e-07   3017 31.106172
## 115 1.009287e-05   3017  3.600293
## 270 4.118184e-06    943  2.601948
# We do not remove outliers.
# P <- P[-outlier.idx,]
P<-na.omit(P)
trellis.par.set("strip.background",t)

xyplot(ratios ~ ConcGr | ConcGr,
       data = P,
         strip = strip.custom(strip.names = TRUE, strip.levels = TRUE),
       panel = function(x,y){
         panel.abline(h=1, col='red')
          panel.xyplot(x,y, pch=16, col=rgb(0.5,0.5,0.5,alpha = 0.5))
          panel.points(x, mean(y))
         #   panel.points(x, median(y),col='green')
          panel.points(x, mean(y) + sd(y), pch='-', col='red',cex=5)
          ltext(x, mean(y) + sd(y), round(sd(y),3), pos=4)
         
           panel.points(x, mean(y) - sd(y), pch='-', col='red',cex=5)
         },
        #ylim=c(-1,3),
       scales = list(x = list(relation = "free", rot=45)),
       layout = c(4,1))
measured ratios were plotted; red represents the sd and blue points the mean.

Figure 2: measured ratios were plotted; red represents the sd and blue points the mean

4.4 Define function FlycodeRelativeStatistics

to make the random experiment fair!

P <- FCfill2max(P)
FlycodeRelativeStatistics <- function(X, mode='bio'){
   nFlycodesConcGr <- aggregate(X$Sequence ~ X$ConcGr, FUN=length)
  names(nFlycodesConcGr) <- c('ConcGr', 'nFlycodesConcGr')
  nFlycodesAccession.max <- max(aggregate(X$Sequence ~ X$Accession, FUN=length)[,2])
 
  
  P.sum.coli1 <- aggregate(X$coli1 ~ X$Accession + X$ConcGr, FUN=sum)
  P.sum.coli2 <- aggregate(X$coli2 ~ X$Accession + X$ConcGr, FUN=sum)[,3]
  P.sum.smeg1 <- aggregate(X$smeg1 ~ X$Accession + X$ConcGr, FUN=sum)[,3]
  P.sum.smeg2 <- aggregate(X$smeg2 ~ X$Accession + X$ConcGr, FUN=sum)[,3]
  
  X <- P.sum.coli1
  names(X) <- c('Accession', 'ConcGr', 'coli1')
  
  X$coli2 <- P.sum.coli2
  X$smeg1 <- P.sum.smeg1
  X$smeg2 <- P.sum.smeg2
  
  
  if(!"ratios" %in% names(X)){
    if (mode == 'tech_smeg'){
      X$ratios <- X$smeg1 / X$smeg2
    }
    else if(mode == 'tech_coli'){
      X$ratios <- X$coli1 / X$coli2
    }else{
      # bio
      X$ratios <- ((0.5 * (X$coli1 + X$coli2)) / (0.5 * (X$smeg1 + X$smeg2)))
    }
    #warning("define ratios.")
  }
  
  #nFlycodesConcGr <- aggregate(X$Sequence ~ X$ConcGr, FUN=length)
  #names(nFlycodesConcGr) <- c('ConcGr', 'nFlycodesConcGr')
  X <- na.omit(X)
  
  
  P.mean <- aggregate(X$ratios ~ X$ConcGr, FUN=mean)
  names(P.mean) <- c('ConcGr', 'mean')
  
  P.median <- aggregate(X$ratios ~ X$ConcGr, FUN=median)
  names(P.median) <- c('ConcGr', 'median')
  
  P.sd <- aggregate(X$ratios ~ X$ConcGr, FUN=sd)
  names(P.sd) <- c('ConcGr', 'sd')
  
 rv <- data.frame(ConcGr=P.mean$ConcGr,
                  median=P.median$median,
                  mean=P.mean$mean,
                  sd=P.sd$sd)
         #         nFlycodesConcGr=nFlycodesConcGr[,2])
 
 rv$cv <- 100 * rv$sd / rv$mean 
 rv$length <- nFlycodesAccession.max
 rv
}

4.5 Conduct the random experiment

message(paste("Number of simulations =", n.simulation))
## Number of simulations = 10
set.seed(1)

P.replicate <- do.call('rbind',
  lapply(1:n.simulation, 
    function(run){
      do.call('rbind', lapply(1:29,
        function(i) {
          rv <- FlycodeRelativeStatistics(FCrandom(P, i))
                                                   rv$run = run
                                                   rv
                                                 }))
                       }))
set.seed(1)

P.replicate.smeg <- do.call('rbind',
                       lapply(1:n.simulation/2, function(run){
                         do.call('rbind', lapply(1:29,
                                                 function(i) {
                                                   rv <- FlycodeRelativeStatistics(FCrandom(P, i), mode='tech_smeg')
                                                   rv$run = run
                                                   rv
                                                 }))
                       }))
set.seed(1)

P.replicate.coli <- do.call('rbind',
                       lapply(1:n.simulation/2, function(run){
                         do.call('rbind', lapply(1:29,
                                                 function(i) {
                                                   rv <- FlycodeRelativeStatistics(FCrandom(P, i), mode='tech_coli')
                                                   rv$run = run
                                                   rv
                                                 }))
                       }))
xyplot(mean ~  length | ConcGr, 
       data=P.replicate,
       panel = function(...){
         panel.abline(h=1, col='red')
         panel.xyplot(...)
       },
         strip = strip.custom(strip.names = TRUE, strip.levels = TRUE),
       ylim=c(0,2),
       pch=16, col=rgb(0.5, 0.5, 0.5, alpha = 0.1),
       layout = c(4,1),
       xlab= 'number of FlyCodes',
       )
the grey point cloud represents the means, sd, and cv of `n.simulation` conducted random experiments (removals) with a different number of FlyCodes.

Figure 3: the grey point cloud represents the means, sd, and cv of n.simulation conducted random experiments (removals) with a different number of FlyCodes

xyplot(sd ~  length | ConcGr, 
       data=P.replicate,
       panel = function(...){
         panel.xyplot(...)
       },
         strip = strip.custom(strip.names = TRUE, strip.levels = TRUE),
       pch=16, col=rgb(0.5, 0.5, 0.5, alpha = 0.1),
       layout = c(4,1),
       xlab= 'number of FlyCodes',
       )
the grey point cloud represents the means, sd, and cv of `n.simulation` conducted random experiments (removals) with a different number of FlyCodes.

Figure 4: the grey point cloud represents the means, sd, and cv of n.simulation conducted random experiments (removals) with a different number of FlyCodes

xyplot(cv ~  length | ConcGr, 
       data=P.replicate,
       panel = function(...){
         panel.xyplot(...)
       },
       strip = strip.custom(strip.names = TRUE, strip.levels = TRUE),
       pch=16, col=rgb(0.5, 0.5, 0.5, alpha = 0.01),
       layout = c(4,1),
       xlab= 'number of FlyCodes',
       ylab ='cv [in %]'
       )
the grey point cloud represents the means, sd, and cv of `n.simulation` conducted random experiments (removals) with a different number of FlyCodes.

Figure 5: the grey point cloud represents the means, sd, and cv of n.simulation conducted random experiments (removals) with a different number of FlyCodes

4.6 Camera ready plot of relative flycode simulation

SIM <- P.replicate
SIM$Type <- "Biological replicates"
P.replicate.coli$Type <- "Technical replicates"
P.replicate.smeg$Type <- "Technical replicates"

NestLink_relative_flycode_simulation <- xyplot(cv ~ length | ConcGr, 
       index.cond=list(c(1,2,3,4)),
       data=do.call('rbind', list(SIM, P.replicate.coli, P.replicate.smeg)),
       subset = Type == "Biological replicates",
       panel = function(x,y,...){
         panel.abline(h=10, col='red')
         panel.xyplot(x, y, ...)
         xy.median <- (aggregate(y, by=list(x), FUN=median, na.rm = TRUE))
         xy.quantile <- aggregate(y, by=list(x), FUN=function(d){quantile(d, c(0.05, 0.5, 0.95), na.rm = TRUE)})
         panel.points( xy.median[,1], xy.median[,2], pch=16, cex=0.5)
         # print((xy.quantile[,2])[,3])
         panel.points( xy.quantile[,1],(xy.quantile[,2])[,1], pch='-')
         panel.points( xy.quantile[,1],(xy.quantile[,2])[,3], pch='-')
       },
       strip = strip.custom(strip.names = TRUE, strip.levels = TRUE),
       scales=list(x=list(at=c(1,5,10,15,20,25,30)),
                   y=list(at=c(0, 10,20,30,40,50))),
       pch=16,
       col=rgb(0.5, 0.5, 0.5, alpha = 0.01),
       layout = c(4, 1),
       ylim = c(0, 50),
       xlab= 'Number of flycodes',
       ylab ='CV [%]'
       )

print(NestLink_relative_flycode_simulation)

NestLink_relative_flycode_simulation <- xyplot(cv ~ length | ConcGr, 
       index.cond=list(c(1,2,3,4)),
       data=do.call('rbind', list(SIM, P.replicate.coli, P.replicate.smeg)),
       subset = Type == "Technical replicates",
       panel = function(x,y,...){
         panel.abline(h=10, col='red')
         panel.xyplot(x, y, ...)
         xy.median <- (aggregate(y, by=list(x), FUN=median, na.rm = TRUE))
         xy.quantile <- aggregate(y, by=list(x), FUN=function(d){quantile(d, c(0.05, 0.5, 0.95), na.rm = TRUE)})
         panel.points( xy.median[,1], xy.median[,2], pch=16, cex=0.5)
         # print((xy.quantile[,2])[,3])
         panel.points( xy.quantile[,1],(xy.quantile[,2])[,1], pch='-')
         panel.points( xy.quantile[,1],(xy.quantile[,2])[,3], pch='-')
       },
       strip = strip.custom(strip.names = TRUE, strip.levels = TRUE),
       scales=list(x=list(at=c(1,5,10,15,20,25,30)),
                   y=list(at=c(0, 10,20,30,40,50))),
       pch=16,
       col=rgb(0.5, 0.5, 0.5, alpha = 0.01),
       layout = c(4, 1),
       ylim = c(0, 50),
       xlab= 'Number of flycodes',
       ylab ='CV [%]'
       )

print(NestLink_relative_flycode_simulation)

4.7 Camera ready relative table

length corresponds to the number accessions. i indicates the number of removed Flycodes in each accession group and can be ignored.

kable(FlycodeRelativeStatistics(P, mode = 'bio'))
ConcGr median mean sd cv length
92 0.9294538 0.9284512 0.0519245 5.592595 30
295 0.8947774 0.8994735 0.0649453 7.220370 30
943 0.9614609 0.9711361 0.0481363 4.956702 30
3017 1.0179646 1.0278351 0.0444473 4.324363 30
kable(FlycodeRelativeStatistics(P, mode = 'tech_coli'))
ConcGr median mean sd cv length
92 1.015500 1.0080013 0.0316037 3.135279 30
295 1.014147 1.0140046 0.0106994 1.055162 30
943 1.005331 1.0061221 0.0151953 1.510284 30
3017 0.994935 0.9967548 0.0210467 2.111524 30
kable(FlycodeRelativeStatistics(P, mode = 'tech_smeg'))
ConcGr median mean sd cv length
92 0.9918049 0.9912911 0.0172931 1.7445066 30
295 0.9938872 0.9908330 0.0211242 2.1319627 30
943 0.9956908 0.9972972 0.0098568 0.9883481 30
3017 0.9928825 1.0032881 0.0263150 2.6228775 30

5 Session info

Here is the output of the sessionInfo() command.

## R version 4.1.0 (2021-05-18)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.2 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.13-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.13-bioc/R/lib/libRlapack.so
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=C              
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] stats4    parallel  stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
##  [1] lattice_0.20-44             specL_1.26.0               
##  [3] seqinr_4.2-5                RSQLite_2.2.7              
##  [5] DBI_1.1.1                   knitr_1.33                 
##  [7] scales_1.1.1                ggplot2_3.3.3              
##  [9] NestLink_1.8.0              ShortRead_1.50.0           
## [11] GenomicAlignments_1.28.0    SummarizedExperiment_1.22.0
## [13] Biobase_2.52.0              MatrixGenerics_1.4.0       
## [15] matrixStats_0.58.0          Rsamtools_2.8.0            
## [17] GenomicRanges_1.44.0        BiocParallel_1.26.0        
## [19] protViz_0.6.8               gplots_3.1.1               
## [21] Biostrings_2.60.0           GenomeInfoDb_1.28.0        
## [23] XVector_0.32.0              IRanges_2.26.0             
## [25] S4Vectors_0.30.0            ExperimentHub_2.0.0        
## [27] AnnotationHub_3.0.0         BiocFileCache_2.0.0        
## [29] dbplyr_2.1.1                BiocGenerics_0.38.0        
## [31] BiocStyle_2.20.0           
## 
## loaded via a namespace (and not attached):
##  [1] colorspace_2.0-1              hwriter_1.3.2                
##  [3] ellipsis_0.3.2                farver_2.1.0                 
##  [5] bit64_4.0.5                   interactiveDisplayBase_1.30.0
##  [7] AnnotationDbi_1.54.0          fansi_0.4.2                  
##  [9] codetools_0.2-18              splines_4.1.0                
## [11] cachem_1.0.5                  ade4_1.7-16                  
## [13] jsonlite_1.7.2                png_0.1-7                    
## [15] shiny_1.6.0                   BiocManager_1.30.15          
## [17] compiler_4.1.0                httr_1.4.2                   
## [19] assertthat_0.2.1              Matrix_1.3-3                 
## [21] fastmap_1.1.0                 later_1.2.0                  
## [23] prettyunits_1.1.1             htmltools_0.5.1.1            
## [25] tools_4.1.0                   gtable_0.3.0                 
## [27] glue_1.4.2                    GenomeInfoDbData_1.2.6       
## [29] dplyr_1.0.6                   rappdirs_0.3.3               
## [31] Rcpp_1.0.6                    jquerylib_0.1.4              
## [33] vctrs_0.3.8                   nlme_3.1-152                 
## [35] xfun_0.23                     stringr_1.4.0                
## [37] mime_0.10                     lifecycle_1.0.0              
## [39] gtools_3.8.2                  zlibbioc_1.38.0              
## [41] MASS_7.3-54                   hms_1.1.0                    
## [43] promises_1.2.0.1              RColorBrewer_1.1-2           
## [45] yaml_2.2.1                    curl_4.3.1                   
## [47] memoise_2.0.0                 sass_0.4.0                   
## [49] latticeExtra_0.6-29           stringi_1.6.2                
## [51] BiocVersion_3.13.1            highr_0.9                    
## [53] caTools_1.18.2                filelock_1.0.2               
## [55] rlang_0.4.11                  pkgconfig_2.0.3              
## [57] bitops_1.0-7                  evaluate_0.14                
## [59] purrr_0.3.4                   labeling_0.4.2               
## [61] bit_4.0.4                     tidyselect_1.1.1             
## [63] magrittr_2.0.1                bookdown_0.22                
## [65] R6_2.5.0                      magick_2.7.2                 
## [67] generics_0.1.0                DelayedArray_0.18.0          
## [69] pillar_1.6.1                  withr_2.4.2                  
## [71] mgcv_1.8-35                   KEGGREST_1.32.0              
## [73] RCurl_1.98-1.3                tibble_3.1.2                 
## [75] crayon_1.4.1                  KernSmooth_2.23-20           
## [77] utf8_1.2.1                    rmarkdown_2.8                
## [79] progress_1.2.2                jpeg_0.1-8.1                 
## [81] grid_4.1.0                    blob_1.2.1                   
## [83] digest_0.6.27                 xtable_1.8-4                 
## [85] httpuv_1.6.1                  munsell_0.5.0                
## [87] bslib_0.2.5.1

References