alexander-r
6/14/2016 - 2:53 PM
MetNorm based normalization
MetNorm based normalization
# EX00543 POS untargeted data analysis
setwd("M:/DataAnalysis/_Reports/EX00543 (Untargeted and lipidomics scleroderma_PAH_Discovery cohort)/A003 - Untargeted/Documents/ByBatch/No HPE/Filtered/POS")
library(ggplot2)
library(reshape2)
library(dplyr)
# read data
posdat.b1 <- read.delim("EX00543-Batch-1-no-HPE-POS-DATA-4R.txt", check.names=FALSE)
posdat.b2 <- read.delim("EX00543-Batch-2-no-HPE-POS-DATA-4R.txt", check.names=FALSE)
posdat.b3 <- read.delim("EX00543-Batch-3-no-HPE-POS-DATA-4R.txt", check.names=FALSE)
posdat.b4 <- read.delim("EX00543-Batch-4-no-HPE-POS-DATA-4R.txt", check.names=FALSE)
posdat.b1$BATCH <- 1
posdat.b2$BATCH <- 2
posdat.b3$BATCH <- 3
posdat.b4$BATCH <- 4
# Combine batch data in a single table matching on column names
posdat.all.na <- bind_rows(list(posdat.b1, posdat.b2, posdat.b3, posdat.b4))
rm(posdat.b1, posdat.b2, posdat.b3, posdat.b4)
# replace NA with small random #
posdat.all <- posdat.all.na
posdat.all[is.na(posdat.all)] <- sample(runif(10, 10, 100), sum(is.na(posdat.all)),replace=TRUE)
rnm <- posdat.all$`Sample name`
posdat.l2 <- log2(posdat.all[ , !(names(posdat.all) %in% c("Sample name","Sample type", "BATCH"))])
row.names(posdat.l2) <- rnm
# Select Internal Standards only
IS <- posdat.l2[,grep("ISTD", colnames(posdat.l2))]
# Select data WITHOUT Internal Standards
nois <- posdat.l2[,grep("ISTD", colnames(posdat.l2), invert = TRUE)]
###################################
# Normalize 3 separate batches
###################################
library(MetNorm)
# Batch 1
# Select ~1/3 of compound data
pos.13 <- as.data.frame(cbind(IS, nois[,c(1:1628)]))
row.names(pos.13) <- rnm
# FInd compounds highly correlated to internal standards
r<-numeric(dim(pos.13)[2])
for(j in 1:length(r)){
r[j]<-cor(IS, pos.13[,j])
}
ctl<-logical(length(r))
ctl[which(r>round(quantile(r,0.7),2))]<-TRUE
# Run random normalization
ruv13<-NormalizeRUVRand(Y=as.matrix(pos.13),ctl=ctl,k=200)
# Run normalization for clustering
ruvclust13<-NormalizeRUVRandClust(RUVRand=ruv13,
maxIter=200,
lambdaUpdate=FALSE,
p=2)
# Batch 2
pos.23 <- as.data.frame(cbind(IS, nois[,c(1629:3257)]))
row.names(pos.23) <- rnm
r<-numeric(dim(pos.23)[2])
for(j in 1:length(r)){
r[j]<-cor(IS, pos.23[,j])
}
ctl<-logical(length(r))
ctl[which(r>round(quantile(r,0.7),2))]<-TRUE
ruv23<-NormalizeRUVRand(Y=as.matrix(pos.23),ctl=ctl,k=200)
ruvclust23<-NormalizeRUVRandClust(RUVRand=ruv23,
maxIter=200,
lambdaUpdate=FALSE,
p=2)
# Batch 3
pos.33 <- as.data.frame(cbind(IS, nois[,c(3258:4884)]))
row.names(pos.33) <- rnm
r<-numeric(dim(pos.33)[2])
for(j in 1:length(r)){
r[j]<-cor(IS, pos.33[,j])
}
ctl<-logical(length(r))
ctl[which(r>round(quantile(r,0.7),2))]<-TRUE
ruv33<-NormalizeRUVRand(Y=as.matrix(pos.33),ctl=ctl,k=200)
ruvclust33<-NormalizeRUVRandClust(RUVRand=ruv33,
maxIter=200,
lambdaUpdate=FALSE,
p=2)
# Combine and export raw results
posorig.l2 <- as.data.frame(cbind(ruv13$unadjY, ruv23$unadjY[,-(1:5)], ruv33$unadjY[,-(1:5)]))
posorig.raw <- as.data.frame(2^posorig.l2)
posorig.raw$File = row.names(posorig.raw)
write.table(t(posorig.raw), file = "EX00543-posorig-raw.txt", quote = F, col.names = F, sep = "\t")
# Combine and export norm results
posnorm <- as.data.frame(cbind(ruv13$newY, ruv23$newY[,-(1:5)], ruv33$newY[,-(1:5)]))
# Convert data from Z-scored to raw
library(DMwR)
posnorm.l2 <- unscale(posnorm,scale(posorig.l2))
posnorm.raw <- as.data.frame(2^posnorm.l2)
posnorm.raw$File = row.names(posnorm.raw)
write.table(t(posnorm.raw), file = "EX00543-posnorm-raw.txt", quote = F, col.names = F, sep = "\t")
# Combine and export normalized results for clustering
posnorm.clust <- as.data.frame(cbind(ruvclust13$newY, ruvclust23$newY[,-(1:5)], ruvclust33$newY[,-(1:5)]))
posnorm.clust.l2 <- unscale(posnorm.clust,scale(posorig.l2))
posnorm.clust.raw <- as.data.frame(2^posnorm.clust.l2)
posnorm.clust.raw$File = row.names(posnorm.clust.raw)
write.table(t(posnorm.clust.raw), file = "EX00543-posnorm-clust-raw.txt", quote = F, col.names = F, sep = "\t")
# Remove imputed data for normalized matrix
posdat.all.no.imp <- bind_rows(list(posdat.all.na[1,!(names(posdat.all.na) %in% c("Sample name","Sample type", "BATCH"))], posnorm.raw))
posdat.all.no.imp <- posdat.all.no.imp[-1,]
posdat.all.no.imp$File == posdat.all.na$`Sample name` # Check if raws in the same order -> OK
row.names(posdat.all.no.imp) <- posdat.all.no.imp$File
posdat.all.no.imp <- posdat.all.no.imp[,!(names(posdat.all.no.imp) %in% c("File","Var.882"))]
posdat.orig <- posdat.all.na[ , !(names(posdat.all.na) %in% c("Sample name","Sample type", "BATCH"))]
posdat.all.no.imp[is.na(posdat.orig)] <- NA
row.names(posdat.all.no.imp) <- posdat.all.na$`Sample name`
write.table(t(posdat.all.no.imp), file = "EX00543-posnorm-raw-with-NA.txt", quote = F, col.names = F, sep = "\t")
# Remove imputed data for original matrix
posdat.all.no.imp.orig <- bind_rows(list(posdat.all.na[1,!(names(posdat.all.na) %in% c("Sample name","Sample type", "BATCH"))], posorig.raw))
posdat.all.no.imp.orig <- posdat.all.no.imp.orig[-1,]
posdat.all.no.imp.orig$File == posdat.all.na$`Sample name` # Check if raws in the same order -> OK
row.names(posdat.all.no.imp.orig) <- posdat.all.no.imp.orig$File
posdat.all.no.imp.orig <- posdat.all.no.imp.orig[,!(names(posdat.all.no.imp.orig) %in% c("File","Var.882"))]
posdat.orig <- posdat.all.na[ , !(names(posdat.all.na) %in% c("Sample name","Sample type", "BATCH"))]
posdat.all.no.imp.orig[is.na(posdat.orig)] <- NA
row.names(posdat.all.no.imp.orig) <- posdat.all.na$`Sample name`
write.table(t(posdat.all.no.imp.orig), file = "EX00543-posorig-raw-with-NA.txt", quote = F, col.names = F, sep = "\t")
# Bind normalized and original data
posdat.all.no.imp$NORM = "YES"
posdat.all.no.imp.orig$NORM = "NO"
posdat.all.no.imp$File = row.names(posdat.all.no.imp)
posdat.all.no.imp.orig$File = row.names(posdat.all.no.imp.orig)
pos.nor.nonorm <- bind_rows(list(posdat.all.no.imp, posdat.all.no.imp.orig))
write.table(t(pos.nor.nonorm), file = "EX00543-pos-norm-nonorm.txt", quote = F, col.names = F, sep = "\t", na="")
write.table(pos.nor.nonorm, file = "EX00543-pos-norm-nonorm-t.txt", quote = F, row.names = F, sep = "\t", na="")
# Plot normalized vs non-normalized
pos.m <- melt(pos.nor.nonorm, id.vars = c("File", "NORM"), variable.name = "TARGET", value.name = "AREA", na.rm = T)
pos.m.list <-split(pos.m, pos.m$TARGET)
# Function to plot original and normalized values across all samples
dotpf <- function(x){
if(nrow(x) > 0){
p <- ggplot(x, aes(File, AREA)) + geom_point(aes(colour = NORM)) + ggtitle(x$TARGET[[1]])
p
}
}
# Create multi-page PDF with plots
pdf("EX00543-POS-Norm-vs-original-scatter.pdf", onefile = T)
lapply(pos.m.list, dotpf)
dev.off()
Sample name Sample type ISO-BUTYRYLCARNITINE (*) (M+H)+ | 232.1531 @ 2.838 IBUPROFEN (M+Na)+ | 229.1192 @ 18.827 N-ACETYL-D-TRYPTOPHAN (M+H)+ | 247.1069 @ 7.941 PROLINE (M+Na)+ | 138.0531 @ .753 CREATINE (M+H)+ | 132.0764 @ .762 OMEPRAZOLE (M+H)+ | 346.1210 @ 11.091 ISOVALERYLCARNITINE (*) (M+H)+ | 246.1691 @ 4.610 DIETHYLSTILBESTROL (M+H)+ | 269.1528 @ 18.829 SEBACIC ACID (M+H)+[-H2O] | 185.1163 @ 12.982 10-HYDROXYDECANOIC ACID (M+Na)+ | 211.1295 @ 13.522 SEROTONIN (M+Na)+ | 199.0832 @ 1.683 DEOXYCHOLIC ACID (M+Na)+ | 415.2809 @ 21.648 4-ACETAMIDOBUTANOIC ACID (M+Na)+ | 168.0631 @ 1.839 GLUCOSE (M+Na)+ | 203.0514 @ .764 N-ACETYL-L-LEUCINE (M+H)+ | 174.1112 @ 7.062 CAFFEINE (M+H)+ | 195.0878 @ 5.907 ISOLEUCINE (M+H)+ | 132.1016 @ 1.600 THEOPHYLLINE (M+H)+ | 181.0720 @ 4.396 OMEPRAZOLE SULFONE (2M+Na)+ | 745.2097 @ 12.257 4-QUINOLINECARBOXYLIC ACID #2 (M+H)+ | 174.0549 @ 4.951 CHOLESTEROL (M+H)+[-H2O] | 369.3515 @ 24.589 KYNURENINE (M+Na)+ | 231.0743 @ 2.229 CHOLIC ACID (2M+H)+ | 817.5808 @ 20.276 AZELAIC ACID (M+Na)+ | 211.0963 @ 10.643 N-ALPHA-ACETYL-L-LYSINE (M+H)+ | 189.1254 @ .782 ZEATIN [ISTD] (M+H)+ | 220.1185 @ 3.932 ISOLEUCINE (M+Na)+ | 154.0833 @ 1.548 KYNURENINE (M+H)+ | 209.0912 @ 2.234 OLEIC ACID (M+Na)+ | 305.2443 @ 22.529 LUMICHROME (M+Na)+ | 265.0708 @ 10.894 DEHYDROEPIANDROSTERONE (M+H)+ | 289.2135 @ 17.265 DODECENOYLCARNITINE (*) II M+ | 342.2629 @ 16.718 TAUROCHENODEOXYCHOLIC ACID (M+Na)+ | 522.2853 @ 20.184 HOMOCYSTEINE (M+H)+ | 136.0433 @ .780 METHYL INDOLE-3-ACETATE (M+H)+ | 190.0861 @ 11.599 DEBRISOQUIN (M+H)+ | 176.1189 @ 5.081 ORNITHINE (M+H)+ | 133.0970 @ .601 20:0 LYSO PC (M+H)+ | 552.3999 @ 22.952 THEOBROMINE (M+Na)+ | 203.0517 @ 3.495 DOXYLAMINE (M+H)+ | 271.1769 @ 5.881 URIDINE (M+H)+ | 245.0777 @ 1.380 DIHYDRO-4,4-DIMETHYL-2,3-FURANDIONE (M+Na)+ | 151.0391 @ 3.573 N-ACETYL-DL-METHIONINE (M+H)+ | 192.0669 @ 4.046 SERINE (M+H)+ | 106.0487 @ 1.053 PROLINE (M+H)+ | 116.0704 @ .735 CARNITINE (M+H)+ | 162.1109 @ .688 1,7-DIMETHYL URIC ACID (M+H)+ | 197.0697 @ 4.054 S-ADENOSYL-L-HOMOCYSTEINE (M+H)+ | 385.1287 @ 1.558 OLEOYL-GLYCEROL (M+H)+ | 357.2974 @ 22.748 1-AMINOCYCLOPROPANE-1-CARBOXYLIC ACID (M+H)+ | 102.0546 @ 1.048 ACETAMINOPHEN (TN TYLENOL) (M+H)+ | 152.0703 @ 3.093 GLYCOCHENODEOXYCHOLIC ACID (M+H)+ | 450.3195 @ 20.326 CREATININE (M+H)+ | 114.0656 @ .687 CORTISONE (M+Na)+ | 383.1845 @ 13.331
EX00543_20160323_CS00000091-Pool-1_P POOLED 850597 87142 25705 194801 460111 25706 142721 49731 5950 3432003 2.27E+07 245233 207714 26898 638602 52167 1498 80002 1076 249418 423197 10922 22870 20261 67131 58292 5213 1240 153542 22364 1127 86265 3169 75221
EX00543_20160323_CS00000091-Pool-2_P POOLED 842822 88764 22492 4107 204256 461000 3364 146267 7247 3404402 2.29E+07 244329 204442 27927 648821 55585 1130 88016 246631 435064 13332 23337 16107 67399 2704 9650 5243 197607 24888 1354 20125 85177 7036 76271
EX00543_20160323_CS00000091-Pool-3_P POOLED 792086 89336 18823 199065 452556 145127 11226 4120 3400179 2.28E+07 240516 197214 27260 638834 56767 74673 245595 527288 419285 11249 26363 15469 67591 3672 47300 1883 196910 20933 1645 11402 77334 13355 73220
EX00543_20160323_CS00000091-Pool-4_P POOLED 779958 83362 20876 197983 445927 144902 3829 3324968 2.25E+07 229500 205195 28373 622984 49608 81161 234770 524639 421121 15625 25696 8631 69004 54195 5996 201205 22390 2403 78613 74801
EX00543_20160323_CS00000091-Pool-5_P POOLED 754369 87623 17498 194269 437116 30747 144067 5208 2909 3395852 2.34E+07 232733 199884 27954 599751 56240 91055 239262 526382 423872 9883 26341 11747 69799 3256 44439 1600 2134 203714 21444 1976 14347 83943 7958 73377
EX00543_20160323_CS00000091-Pool-6_P POOLED 723552 84582 32052 195944 426419 3276 146761 8621 3567 3340670 2.33E+07 239902 193850 600672 57971 1185 83080 239711 536739 436938 19647 26414 10944 68608 1732 58949 2011 1503 201221 23811 2140 11838 79953 3405 74324
EX00543_20160323_CS00000091-Pool-7_P POOLED 710397 83585 16905 189629 421221 6585 148241 12009 3246856 2.30E+07 230477 193448 26930 606981 56146 90403 1561 229285 540182 413087 11946 25776 12808 69667 4070 42732 208020 21456 2184 83296 71516
EX00543_20160323_CS0000009-MP-1_P SAMPLE 1156394 111071 28324 268287 608434 4389 8654 5947 5436 3434517 2.23E+07 267237 268358 26531 626338 35942 97685 266695 409215 28527 25793 22096 25930 1416 92994 4315 220500 23941 4004 9035 368550 6112 73068
EX00543_20160323_CS0000009-MP-2_P SAMPLE 1165943 111680 27683 245988 611970 114334 2388 3518980 2.23E+07 257070 268338 26953 649179 37111 97343 262669 510943 408605 11509 26543 21967 24507 70574 2278 2208 217482 4080 6195 23552 2186 8395 383576 72892
EX00543_20160323_CS0000009-MP-3_P SAMPLE 1113271 110308 28317 8144 243233 616167 4988 128296 7020 3396005 2.24E+07 253888 280379 28784 611675 34384 105795 253540 507764 417952 25865 28709 18832 2357 26275 3529 91347 220201 4142 22992 1780 7193 378540 4618 72830
EX00543_20160323_CS0000009-MP-4_P SAMPLE 1101281 108788 26981 241332 597997 4307 126982 7464 5098 3368400 2.21E+07 259098 281988 27914 590681 34292 100469 258082 496799 413472 19696 28062 19378 26801 77285 224369 5691 23272 9473 370468 9001 72598
EX00543_20160323_S00021269_P SAMPLE 1212018 30416 11907 7785 193876 410364 5389 131718 7498 1902364 2.31E+07 315048 268478 24956 599358 32224 89055 224909 568420 31129 19721 9743 86103 2634 181266 15727 1154614 17523
EX00543_20160323_S00021270_P SAMPLE 1048940 28263 9157 161862 362984 4831 120421 11837 4784 1865787 2.31E+07 299852 238246 21381 573807 37657 100416 223546 709033 550266 55527 1877 13893 6812 60774 5719 183074 18586 1074326
EX00543_20160323_S00021275_P SAMPLE 794442 10812 22185 583758 3645 108471 5446 2501 6262609 1.95E+07 451566 37029 546545 42742 141358 248456 342529 337394 10930 9356 20921 19276 3462 165958 193859 11968 1414 5492 113960
EX00543_20160323_S00021276_P SAMPLE 879256 12127 2921 427279 103267 4769 6483913 2.01E+07 505119 42616 571457 46139 171154 268304 370353 345113 20738 6267 18520 20440 2537 63969 111142 2390 5649 8638 121560
EX00543_20160323_S00021279_P SAMPLE 1645357 57264 1382 3240 10192 656966 32434 9450 7165 101282 2.28E+07 588213 31268 452997 78607 143448 245162 499531 6239 20965 40860 61910 2993 118544 16513 4059 5033 94809
EX00543_20160323_S00021280_P SAMPLE 1610053 56352 2573 13082 587312 3483 55751 12713 7373 103426 1.25E+07 10979 575396 27926 608519 87980 76816 254405 586578 564318 18295 1077 26693 37403 96154 3655 1164 2110 142356 16979 2491 113871
EX00543_20160323_S00021287_P SAMPLE 1009605 2066 3722 5509 1514726 1471869 4199 44358 12105 2392 2703734 2.12E+07 311806 422099 15671 392108 126907 166811 238902 393221 654164 34937 10473 11314 19933 180879 54423 2906 17704 7234 6705 115200
EX00543_20160323_S00021288_P SAMPLE 941368 2152 1453714 1217875 1004 8877 29708 17825 6238 2506035 2.14E+07 151140 509894 318179 130186 209121 238235 417909 685878 31961 6529 9193 25599 1914 194684 2258 29246 8433 14742 7292 4037 120895
EX00543_20160323_S00021289_P SAMPLE 1695380 1004 10136 1562 982088 274297 7556 702768 6556 6652 2.35E+07 536384 15393 513421 27765 405319 79891 225881 621532 238154 16560 6847 6431 13072 1139 48578 2616 248618 14346 5004 50012 16599 64109
EX00543_20160323_S00021290_P SAMPLE 1015566 8933 975069 195129 4998 730069 7882 2.47E+07 519118 504243 26450 390642 94584 216732 685185 252918 5935 46502 10730 190841 17257 9553 23734 58377
EX00543_20160323_S00021291_P SAMPLE 823951 30781 187362 4118 519597 18313 1717 4023968 2.17E+07 313051 8087 540874 74163 220668 76606 1558 245058 587523 491913 10785 17799 9952 72897 4946 266063 4979 6163 39300
EX00543_20160323_S00021292_P SAMPLE 487472 29513 136914 493031 14865 1639 3378101 2.22E+07 141074 6464 572051 75964 212053 80171 271503 614727 534345 20148 4967 6097 8026 1717 8315 148699 4833 1420 250159 16613 7112 8908 48998
EX00543_20160323_S00021297_P SAMPLE 288012 2457 6304 475614 176915 68726 9534 2539 1233849 2.16E+07 116563 1270974 14367 397780 26311 85416 224386 638381 210584 6365 49108 41035 89268 4429 238783 12890 9321 13587 83270
EX00543_20160323_S00021298_P SAMPLE 526200 2294 4453 527162 234852 85581 55291 1601948 2.07E+07 259282 1626005 15685 442499 36128 125966 245704 586786 231882 2200 7281 17360 47775 35869 166506 3117 202285 10511 6230 12966 112250
EX00543_20160323_S00021303_P SAMPLE 3598918 6461 18931 12328 846558 5491 16564 297593 1.88E+07 461100 274352 16563 287638 110067 383267 77509 277192 4020 69199 3579 51532 5076 31658 157027 2037 9418 7249 8277 37325
EX00543_20160323_S00021304_P SAMPLE 965815 5071 7877 16922 12946 372503 28998 2696 3731 342918 2.38E+07 40779 383754 21277 581551 41409 135105 233875 808624 295492 10702 5791 1771 67816 26251 6183 2674 163477 18406 1921 79118
EX00543_20160323_S00021309_P SAMPLE 851852 39176 791030 4427 101748 3707 7221 6398190 2.28E+07 352883 45795 624696 66247 98401 259479 521286 422213 11882 6191 108999 20522 107650 68771 14567 3938 83920
EX00543_20160323_S00021310_P SAMPLE 1187807 37999 3876 61781 825304 2880 64931 1596 8123 6311717 2.28E+07 386115 48788 587791 64886 115459 277214 554437 407037 8747 1318 94874 23356 3006 134581 4847 1093 126464 18135 15070 5600 93217
EX00543_20160323_S00021311_P SAMPLE 534671 1967 8356 615950 7471 341660 9279 3747974 2.29E+07 124309 370389 80537 117552 135367 228252 508121 497516 16019 6198 8466 53465 126237 4482 1743 273171 19600 32798 71200
EX00543_20160323_S00021312_P SAMPLE 672912 2184 6692 587307 9840 297664 161145 8692 4910 3506249 2.23E+07 145407 17628 507360 80177 59148 89471 236866 508808 485992 22059 6092 5406 66142 110908 242685 17556 8665 87178
EX00543_20160323_S00021321_P SAMPLE 789225 366545 57762 798393 85240 7990 5132 5981693 2.34E+07 261580 25020 607562 68985 194133 245956 523204 394722 26738 19097 15679 29937 2219 139455 4917 1505 139391 20107 1921 7562 97773
EX00543_20160323_S00021322_P SAMPLE 494177 301111 75466 972142 68727 114237 7056 5157310 2.20E+07 1565660 19624 469508 63202 144177 254674 510277 406092 16493 19679 12814 34884 130915 5306 5378 234927 17492 9344 7780 98793
EX00543_20160323_S00021331_P SAMPLE 1014419 5229 8822 652281 4904 102655 4795 5046 3558890 2.30E+07 354840 18028 578685 59407 154764 240602 545240 404818 13807 19696 18082 76601 1939 279731 16635 14526 18228 63419
EX00543_20160323_S00021332_P SAMPLE 984434 3628 7496 733716 5509 100165 145745 8519 3281 3598639 2.34E+07 364484 18342 604548 53710 127939 236743 417026 16059 1375 16971 15023 2758 79988 6418 296519 3393 21022 68348
EX00543_20160323_S00021343_P SAMPLE 585959 34633 239647 5551 122734 10431 1836300 2.17E+07 163643 26638 493396 61728 231640 611272 447320 10897 28591 192807 13994 2676 84797
EX00543_20160323_S00021344_P SAMPLE 680427 26347 3973 263442 110100 8990 3981 1921848 2.23E+07 175064 27732 594833 62415 98633 230910 494613 19219 13334 2540 59466 3354 194754 1810 18122 1820 89123
EX00543_20160323_S00021351_P SAMPLE 520953 8841 60845 250166 5231 74810 13312 110055 2.22E+07 13608 2483993 7677 618859 59617 134165 214477 662852 473439 9789 7294 12542 17819 39786 6040 197289 23151 10163 99359
EX00543_20160323_S00021352_P SAMPLE 446211 2554 9660 175834 25247 145751 20593 9890 46660 2.07E+07 938104 459137 42183 117012 258658 586136 368376 3308 8013 19317 5337 302233 2222 25966 2898 77017
EX00543_20160323_S00021364_P SAMPLE 487165 3962 12240 43417 16301 254387 4707 33497 5671 7301 2656197 2.27E+07 103464 9520 28985 430110 60896 1025 106043 187742 684616 462423 22308 33061 235365 1007 174945 16351 6307 12303 110984
EX00543_20160323_S00021365_P SAMPLE 452809 1933 10080 51081 11282 218760 21674 12607 5184 2418585 2.31E+07 846022 7193 25890 506222 66897 101219 197416 488318 16206 15282 4294 28736 68675 4943 213261 19773 5188 8220 101724
EX00543_20160323_S00021391_P SAMPLE 1064356 1348 45669 786680 254612 12806 7192 6814617 2.49E+07 364256 29147 616580 97516 133278 251208 559210 589437 5663 17092 19531 31533 116462 2710 171410 16069 3798 19845 16347 100763
EX00543_20160323_S00021392_P SAMPLE 950085 52187 811321 190607 10423 4240 5906057 2.50E+07 2479919 631534 95096 119846 240834 568587 7993 2743 30300 31513 125537 3995 235634 16173 96488
EX00543_20160323_S00021403_P SAMPLE 1171565 7808 3304 35980 610429 2073 82188 7749 105228 2.28E+07 22782 72675 30464 595501 58429 54338 248128 502096 410211 15396 8059 17714 28514 66259 5223 113801 22861 126600
EX00543_20160323_S00021404_P SAMPLE 708916 7502 3024 3001 36221 483201 15510 66664 8235 92065 2.39E+07 65394 33193 621636 59063 54833 244349 546665 453574 21561 7533 16060 29348 89926 4757 105829 20539 8007 115712
EX00543_20160323_S00021409_P SAMPLE 157894 9099 2070 4934 122783 221549 26423 8771 5618 27545 2.17E+07 628226 556547 179838 99132 219474 655959 1172897 1127 20386 43277 6053 3844 59996 17231 7204 19147 4401 82536
EX00543_20160323_S00021410_P SAMPLE 183740 9351 4337 108553 182500 5474 13561 8305 7019 27285 2.39E+07 532985 9174 672711 178747 94119 223854 1362533 26834 1696 10380 69812 5353 5197 83757 11203 23070 8416 6490 73809
EX00543_20160323_S00021413_P SAMPLE 488928 2740 641726 825425 8037 122005 26358 8855 3587272 2.22E+07 599718 506928 118980 418059 93851 21342 250767 581241 509924 38370 38884 1515432 29451 88744 1916 95944 48854 22575 11803 11706 5693 5375 80414
EX00543_20160323_S00021414_P SAMPLE 498611 1023404 800762 6788 6637 21423 4176 3857227 2.53E+07 388245 865017 142864 734138 115507 90014 273133 685581 716544 5618 33479 35149 1642854 2151 90831 2874 2019 8755 64386 58330 32038 4226 14406 1532 119945
EX00543_20160323_S00021419_P SAMPLE 620260 6125 867349 339570 4359 97139 6419 3699 844603 2.41E+07 80744 2166147 378957 49129 113982 220851 378888 11114 51528 47370 123515 15642 11085 425623 8214 101586
EX00543_20160323_S00021420_P SAMPLE 743471 2265 1541 5681 846243 394099 4614 211889 8281 929047 2.46E+07 86963 2274241 15330 522690 53472 88146 214671 740461 413520 19245 11889 61251 5138 137586 19050 6566 7130 440697 115841
EX00543_20160323_S00021423_P SAMPLE 1201007 1893 18977 1095981 4187 147232 9353 3560 3583726 1.27E+07 108087 55404 508324 60480 68970 240535 490460 372129 16464 12097 25627 25187 62617 4413 176816 21011 7856 114781
EX00543_20160323_S00021424_P SAMPLE 1428942 1957 2696 22738 950584 5229 138305 10737 1642 3887534 2.25E+07 126545 58649 487404 55403 127524 240858 467796 361765 22805 2359 22670 25480 1666 58171 5307 184713 15654 6591 125087
EX00543_20160323_S00021431_P SAMPLE 1236647 38667 1339 15702 796074 4979 759365 65172 7975 4787178 2.18E+07 302551 599008 37131 570499 85861 9376 232933 451118 464507 1119 55580 39140 8313 102200 3028 183762 4154 19092 4367 20485 3586 99708
EX00543_20160323_S00021432_P SAMPLE 866432 80259 14079 751364 811458 55844 12419 5013258 2.25E+07 339006 591576 38210 622390 88444 58832 1022 243918 475700 548056 17768 1177 44042 1240 40898 112211 2807 258577 21704 20142 2634 12437 95663
EX00543_20160323_S00021439_P SAMPLE 1151632 7295 6056 188215 4048 81134 9110 5593 3659997 2.14E+07 329796 36930 502860 71779 77029 217305 639410 461840 4685 14812 40012 52743 2496 1990 178545 13987 12898 3099 1185 87660
EX00543_20160323_S00021440_P SAMPLE 829876 8035 21134 149333 7930 77782 11865 4415 3536478 2.20E+07 190495 43676 581124 82527 106634 225055 701117 549374 8110 43486 83709 3079 168374 18521 12188 2768 98929
EX00543_20160323_S00021463_P SAMPLE 738688 34284 363024 4315 3759 2667123 2.20E+07 541869 78237 492586 39095 31027 250134 707758 276739 16060 16964 40259 54994 2558 159599 17654 3440 13928 33792
EX00543_20160323_S00021464_P SAMPLE 1045416 26286 409987 19847 22806 5776 2839848 2.15E+07 562915 81619 474674 43578 19477 260502 671217 288195 2874 28483 45275 58465 183074 12735 2285 4611 50102
EX00543_20160323_S00021467_P SAMPLE 1484489 3790 2073 532206 652639 4579 133597 7931 6886 8945550 2.39E+07 821644 924929 32107 533392 57852 98201 241516 510072 370074 11797 12518 11824 55034 180954 21230 15917 4604 99195
EX00543_20160323_S00021468_P SAMPLE 1377922 6316 2786 79691 4225 425900 802945 3652 122724 7359 6105 8692048 2.41E+07 462164 920286 34174 504224 71500 58729 262372 554088 423728 28071 2893 11707 47881 3741 2760 143121 17241 19752 5637 118030
EX00543_20160323_S00021469_P SAMPLE 928814 4986 1627 41388 6999 703662 12102 276280 11857 3128 6397219 2.33E+07 218955 35831 452428 63365 110439 177487 197827 490536 432214 16442 4575 21480 28618 79604 2802 3763 202759 12865 78933
EX00543_20160323_S00021470_P SAMPLE 1199626 5260 1067 791735 6077 43222 82670 4679 3060 7029346 264048 40147 415088 70922 103542 138488 209667 502353 467832 26073 4246 16883 33445 77079 3638 178334 88974
EX00543_20160323_S00021473_P SAMPLE 444371 3251 3899 678094 475607 3775 21709 14075 3829 1020370 2.35E+07 202996 371994 40182 498719 52169 95924 231302 591067 385607 12829 11186 44227 67054 3820 180565 15450 5548 3188 82671
EX00543_20160323_S00021474_P SAMPLE 494041 2330 3592 449349 451510 5377 16797 19769 3362 971397 2.35E+07 204896 318396 35978 549399 58068 60088 232189 572117 416664 7304 12762 34742 38030 109675 8358 20374 3460 5811 74997