21  Digital Luminex

Author

Chrissy h Roberts

21.0.1 Background

This script demonstrates how to read data from a Luminex .LXB file and to apply a ‘digital’ luminex approach. This counts the number of beads that fluoresce above a threshold, rather than looking at the mean fluorescence intensity for a marker on all beads. This is analogous to a ‘digital PCR’ approach.

In theory this might be a method that requires less inter-plate normalisation as the method is less dependent on average signal strength and more dependent on a within-plate EM model that simply asks what fraction of beads have a positive signal for each marker. Specifically, the actual fluorescence intensity of the markers on the beads is actually very variable from plate to plate, but whatever the fluorescence intensity is, it should still be possible to define a positive and negative population.

The work uses an approach to reading the LXB files which was originally implemented in the lxb package. This is no longer maintained, so a copy of the tar file is included in the packages directory of this repo.

21.1 Libraries

library(tidyverse)
library(knitr)
library(data.table)
library(mclust)
library(mixtools)
install.packages("packages/lxb-1.5.tar.gz", repos = NULL, type="source")
library(lxb)

21.2 Define Functions

21.2.1 Convert LXB Files to CSV

Create function to import an lxb file, add the well name, convert to a data frame and export a CSV with similar name to original file.

lxb.parse<-function(lxbfile)
{
#get the file
outputfile<-gsub(x = lxbfile,pattern = ".lxb",replacement = ".csv")
well<-unlist(strsplit(lxbfile,split = "_"))
well<-well[length(well)]
well<-gsub(well,pattern = ".lxb",replacement = "")
lxbdata <- readLxb(lxbfile)  
lxbdata <- as.data.frame(lxbdata) 
names(lxbdata)<-gsub(names(lxbdata),pattern = " ",replacement = ".")
lxbdata$well<-well
#remove invalid beads
lxbdata<-lxbdata[which(lxbdata$Bead.Valid==1),]
write.csv(x = lxbdata,file = outputfile,row.names = F)
}

21.2.2 Aggregate the csv files in a single df

#define a function to 
lxb.aggregate.data<-function(path)
{
  #list only the csv files
  lxbs<-list.files(path = path,pattern = "*.csv",full.names = T)
  tables <- lapply(lxbs, fread)
  data.table::rbindlist(tables)
}

21.2.3 Apply mixed EM to find thresholds on each bead

lxb.digital.thresholds<-function(df,pdf.out=FALSE,sd.threshold=5)
{
  df2<-df %>% group_by(Bead.ID) %>% summarise(n=n())
  #get bead values from DF
  df2$EM.threshold<-NA
  
  for(i in 1:length(df2$Bead.ID))
  {
    fit<-normalmixEM(df$RP1L.Peak[which(df$Bead.ID==df2$Bead.ID[i])])
    #plot(fit,which=2,breaks=40)
    
    df2$EM.threshold[i]<-fit$mu[which(fit$mu==min(fit$mu))]+(sd.threshold*fit$sigma[which(fit$mu==min(fit$mu))])
    if(pdf.out==TRUE){
                      pdf(file = paste("data/Luminex_LXB_Files/",df2$Bead.ID[i],".pdf",sep=""))
                      par(mfrow=c(1,2))
                      plot(sort(df$RP1L.Peak[which(df$Bead.ID==df2$Bead.ID[i])]))
                      abline(h=df2$EM.threshold[i],lty=2,col="red")
                      plot(fit,whichplots = 2)
                      abline(v=df2$EM.threshold[i],lty=2,col="red")
                                        dev.off()
                      }
  }
  df<-merge(df,df2,by="Bead.ID")
  print(df2)
  df$classification<-as.numeric(df$RP1L.Peak>df$EM.threshold)  
  return(df)
}

21.2.4 Manually select threshold values

lxb.digital.thresholds.locator<-function(df,pdf.out=FALSE,sd.threshold=4)
{
  df2<-df %>% group_by(Bead.ID) %>% summarise(n=n())
  #get bead values from DF
  df2$EM.threshold<-NA
  df2$EM.threshold<-as.numeric(df2$EM.threshold)
  
  for(i in 1:length(df2$Bead.ID))
  {
    fit<-normalmixEM(df$RP1L.Peak[which(df$Bead.ID==df2$Bead.ID[i])])
    #plot(fit,which=2,breaks=40)
    
    
      plot(fit,whichplots = 2)
      EM.threshold<-locator(n = 1)
      df2$EM.threshold[i]<-EM.threshold$x
      abline(v=df2$EM.threshold[i],lty=2,col="red")
  }  
    
  
  df<-merge(df,df2,by="Bead.ID")
  print(df2)
  df$classification<-as.numeric(df$RP1L.Peak>df$EM.threshold)  
  return(df)
}

21.3 Apply the functions to real data

21.3.1 List the LXB files

Luminex machines spit out one LXB file for each well

lxbs<-list.files(path = "data/Luminex_LXB_Files/",pattern = "*.lxb",full.names = T)
lxbs
 [1] "data/Luminex_LXB_Files//A1.lxb"  "data/Luminex_LXB_Files//A10.lxb"
 [3] "data/Luminex_LXB_Files//A11.lxb" "data/Luminex_LXB_Files//A12.lxb"
 [5] "data/Luminex_LXB_Files//A2.lxb"  "data/Luminex_LXB_Files//A3.lxb" 
 [7] "data/Luminex_LXB_Files//A4.lxb"  "data/Luminex_LXB_Files//A5.lxb" 
 [9] "data/Luminex_LXB_Files//A6.lxb"  "data/Luminex_LXB_Files//A7.lxb" 
[11] "data/Luminex_LXB_Files//A8.lxb"  "data/Luminex_LXB_Files//A9.lxb" 

21.3.2 Apply the lxb.parse function to all lxb files in directory

lapply(lxbs,lxb.parse)
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21.3.3 Aggregate the data

a<-lxb.aggregate.data(path = "data/Luminex_LXB_Files/")
a$col<-substr(a$well,start = 1,stop = 1)
a$row<-substr(a$well,start = 2,stop = 3)
kable(head(a,20))
CL1.X CL1.Y CL1.Peak CL1.Ch.Value CL1.Area CL1.Valid CL1.SR CL1.MBR CL1.Pass.Mini.Ratio CL1.Pass.Max.Ratio CL1.Pass.Nearby CL1.Pass.Edge CL2.X CL2.Y CL2.Peak CL2.Ch.Value CL2.Area CL2.Valid CL2.SR CL2.MBR CL2.Pass.Mini.Ratio CL2.Pass.Max.Ratio CL2.Pass.Nearby CL2.Pass.Edge RP1L.X RP1L.Y RP1L.Peak RP1L.Ch.Value RP1L.Area RP1L.Valid RP1L.SR RP1L.MBR RP1L.Pass.Mini.Ratio RP1L.Pass.Max.Ratio RP1L.Pass.Nearby RP1L.Pass.Edge RP1S.X RP1S.Y RP1S.Peak RP1S.Ch.Value RP1S.Area RP1S.Valid RP1S.SR RP1S.MBR RP1S.Pass.Mini.Ratio RP1S.Pass.Max.Ratio RP1S.Pass.Nearby RP1S.Pass.Edge Bead.Valid CL2.Offset.X CL2.Offset.Y CL1_CL2.Dist RP1.Offset.X RP1.Offset.Y CL1.Value CL2.Value RP1.Value Bead.ID well col row
1149234516 1092558165 1343 0 16947 1 -1560336802 -462012473 1 1 1 1 1149223297 1092479444 1296 0 16974 1 1181464885 -1991868891 1 1 1 1 1149236985 1092988697 59632 0 1135332 1 0 0 1 1 1 1 1149236985 1092988697 59632 0 52185 1 0 0 1 1 1 1 1 0 0 -812668335 0 0 118 118 6103 12 Files//A1 F il
1155884337 1093973789 2535 0 32248 1 -1461695296 -1174163721 1 1 1 1 1155877959 1094254143 3861 0 49562 1 1419346258 -463449520 1 1 1 1 1155884214 1094404032 49395 0 777699 1 0 0 1 1 1 1 1155884214 1094404032 49395 0 27587 1 0 0 1 1 1 1 1 0 0 -1717978224 0 0 224 345 3080 26 Files//A1 F il
1141831731 1094397512 7971 0 100698 1 681421247 -1790273464 1 1 1 1 1141820439 1093943886 2157 0 26850 1 -402982476 -53427154 1 1 1 1 1141835711 1094827668 61485 0 1217522 1 0 0 1 1 1 1 1141835711 1094827668 61485 0 63810 1 0 0 1 1 1 1 1 0 0 1367863707 0 0 701 187 7463 21 Files//A1 F il
1150917202 1097198649 4966 0 60897 1 271482799 -1191083431 1 1 1 1 1150911874 1097454201 4016 0 50462 1 1850697676 -113029935 1 1 1 1 1150918093 1097628233 61767 0 1249945 1 0 0 1 1 1 1 1150918093 1097628233 61767 0 80109 1 0 0 1 1 1 1 1 0 0 459537696 0 0 424 352 9369 27 Files//A1 F il
1152361617 1097643907 16662 0 208370 1 -351809365 -2047823587 1 1 1 1 1152359059 1097576007 2136 0 28456 1 2107587046 76070548 1 1 1 1 1152362214 1098073401 59204 0 848208 1 0 0 1 1 1 1 1152362214 1098073401 59204 0 33077 1 0 0 1 1 1 1 1 0 0 -1127313627 0 0 1450 198 3868 22 Files//A1 F il
1151775855 1098727795 16030 0 203329 1 870860878 1513437910 1 1 1 1 1151770676 1098681510 3822 0 47775 1 -758130026 1252485282 1 1 1 1 1151776571 1099032358 14198 0 166583 1 0 0 1 1 1 1 1151776571 1099032358 14198 0 5589 1 0 0 1 1 1 1 1 0 0 1774791597 0 0 1415 333 660 29 Files//A1 F il
1155033097 1100340701 4580 0 59419 1 -1852063166 -1009139138 1 1 1 1 1155030331 1100114914 3966 0 51219 1 114335912 -500949543 1 1 1 1 1155033148 1100555026 61894 0 1325814 1 0 0 1 1 1 1 1155033148 1100555026 61894 0 81886 1 0 0 1 1 1 1 1 0 0 -730158204 0 0 414 357 9577 27 Files//A1 F il
1156429977 1102179833 15608 0 198756 1 1505756983 1583439109 1 1 1 1 1156424231 1102098349 2208 0 28248 1 -1090034562 391667932 1 1 1 1 1156429743 1102393783 59594 0 1228318 1 0 0 1 1 1 1 1156429743 1102393783 59594 0 53059 1 0 0 1 1 1 1 1 0 0 493372241 0 0 1383 197 6205 22 Files//A1 F il
1133053433 1102435101 2559 0 32062 1 -1841237300 -413394956 1 1 1 1 1133042566 1102219953 1325 0 16486 1 926199853 962368627 1 1 1 1 1133063386 1102648999 40484 0 566360 1 0 0 1 1 1 1 1133063386 1102648999 40484 0 20078 1 0 0 1 1 1 1 1 0 0 2138917700 0 0 223 115 2243 13 Files//A1 F il
1146917648 1102574438 15451 0 199489 1 9330036 -2058791451 1 1 1 1 1146911794 1102523540 2114 0 26610 1 1844585360 -208857109 1 1 1 1 1146920590 1102788307 59663 0 1092730 1 0 0 1 1 1 1 1146920590 1102788307 59663 0 45526 1 0 0 1 1 1 1 1 0 0 -1874985181 0 0 1388 185 5324 22 Files//A1 F il
1132034886 1104184846 15495 0 196309 1 -614810869 -1382596637 1 1 1 1 1132011269 1104132015 2002 0 27293 1 -1153969394 -422053358 1 1 1 1 1132055122 1104398386 60985 0 1203448 1 0 0 1 1 1 1 1132055122 1104398386 60985 0 51501 1 0 0 1 1 1 1 1 0 0 -199422534 0 0 1366 190 6023 22 Files//A1 F il
1148738278 1104800157 2225 0 30448 1 336334840 -1722049420 1 1 1 1 1148727247 1104737582 2088 0 27703 1 -1264850604 711461030 1 1 1 1 1148740848 1105013572 4811 0 52665 1 0 0 1 1 1 1 1148740848 1105013572 4811 0 1513 1 0 0 1 1 1 1 1 0 0 1664716139 0 0 212 193 209 19 Files//A1 F il
1154128511 1104521200 16433 0 201733 1 -1224775220 -434514866 1 1 1 1 1154122887 1104494251 3611 0 47364 1 317673209 429097738 1 1 1 1 1154128747 1104734672 14749 0 226132 1 0 0 1 1 1 1 1154128747 1104734672 14749 0 7379 1 0 0 1 1 1 1 1 0 0 -510452285 0 0 1404 330 895 29 Files//A1 F il
1121352736 1105125885 4568 0 58170 1 1999822347 898421215 1 1 1 1 1121307392 1105088368 1294 0 16234 1 -983333522 -1739258039 1 1 1 1 1121397014 1105339233 6723 0 93542 1 0 0 1 1 1 1 1121397014 1105339233 6723 0 2880 1 0 0 1 1 1 1 1 0 0 1329047872 0 0 405 113 370 14 Files//A1 F il
1155909558 1106777421 1199 0 16460 1 821266378 -1246218943 1 1 1 1 1155906397 1106722413 2283 0 29468 1 1183943161 -426756626 1 1 1 1 1155909430 1106990432 17653 0 332506 1 0 0 1 1 1 1 1155909430 1106990432 17653 0 11300 1 0 0 1 1 1 1 1 0 0 -250784161 0 0 115 205 1317 18 Files//A1 F il
1155824328 1107578705 4538 0 59342 1 1559411272 1151799898 1 1 1 1 1155818790 1107546496 4086 0 51988 1 -919791236 351277236 1 1 1 1 1155824218 1107685100 60610 0 1258483 1 0 0 1 1 1 1 1155824218 1107685100 60610 0 58400 1 0 0 1 1 1 1 1 0 0 1128053269 0 0 413 362 6830 27 Files//A1 F il
1155536851 1108324761 15984 0 204814 1 27751286 468010390 1 1 1 1 1155531489 1108306151 3837 0 49674 1 -124177506 392542407 1 1 1 1 1155536799 1108431004 27421 0 513624 1 0 0 1 1 1 1 1155536799 1108431004 27421 0 17913 1 0 0 1 1 1 1 1 0 0 583326885 0 0 1426 346 2034 29 Files//A1 F il
1155406742 1108697607 2641 0 33191 1 -1232077645 -149200003 1 1 1 1 1155400871 1108654290 2163 0 29448 1 0 2050056381 1 1 1 1 1155406717 1108803773 3061 0 42222 1 0 0 1 1 1 1 1155406717 1108803773 3061 0 1392 1 0 0 1 1 1 1 1 0 0 -1202766431 0 0 231 205 167 19 Files//A1 F il
1156984613 1108597201 1279 0 16629 1 -1691806559 -659651601 1 1 1 1 1156981428 1108475694 2288 0 29547 1 -763044262 583186408 1 1 1 1 1156984266 1108703388 47656 0 1013692 1 0 0 1 1 1 1 1156984266 1108703388 47656 0 36156 1 0 0 1 1 1 1 1 0 0 -1178951951 0 0 116 206 4014 18 Files//A1 F il
1142762190 1109171906 4698 0 58567 1 1518863941 -1049339851 1 1 1 1 1142752128 1109138271 2183 0 28897 1 1038826580 -1612340562 1 1 1 1 1142765981 1109277976 39473 0 517176 1 0 0 1 1 1 1 1142765981 1109277976 39473 0 18003 1 0 0 1 1 1 1 1 0 0 -1169600992 0 0 408 201 2048 20 Files//A1 F il

21.3.4 Trim data to beads we actually used

The data may include beads that weren’t used in the assay. Here we used a subset of beads and so need to filter the data

a<-subset(a, subset = Bead.ID %in% c(12:15,18,19,20,21,22,25:30))

21.3.5 Plot data for all beads

CL1 and CL2 are the bead identification lasers.

ggplot(data = a,aes(x=CL1.Peak,y=CL2.Peak,color=factor(Bead.ID)))+geom_point()+
  scale_x_continuous(trans = "log")

21.3.6 Show table of bead counts

table(factor(a$Bead.ID),a$Bead.Valid)
    
        1
  12 1554
  13 1183
  14 1482
  15 1283
  18 1711
  19 1607
  20 1375
  21 1536
  22 1567
  25 1684
  26 1347
  27 1775
  28 1490
  29 1634
  30 1150

21.3.7 Threshold classify everything based on automated EM algorithm

This also saves a PDF in the directory data/Luminex_LXB_Files/

a<-lxb.digital.thresholds(a,pdf.out = T,sd.threshold = 3)
number of iterations= 15 
number of iterations= 63 
number of iterations= 35 
number of iterations= 22 
number of iterations= 46 
number of iterations= 53 
number of iterations= 25 
number of iterations= 27 
number of iterations= 46 
number of iterations= 49 
number of iterations= 33 
number of iterations= 23 
number of iterations= 480 
number of iterations= 87 
number of iterations= 18 
# A tibble: 15 × 3
   Bead.ID     n EM.threshold
     <int> <int>        <dbl>
 1      12  1554       82664.
 2      13  1183       27482.
 3      14  1482       18306.
 4      15  1283       13884.
 5      18  1711       49013.
 6      19  1607       11781.
 7      20  1375        8491.
 8      21  1536       10624.
 9      22  1567       22790.
10      25  1684       21194.
11      26  1347       14887.
12      27  1775       95263.
13      28  1490        8799.
14      29  1634       12463.
15      30  1150       90940.

21.4 Summarise based on bead counts

aa<-a %>% group_by(Bead.ID,well) %>% summarise(count=n(),bead.pos=sum(classification==1),bead.neg=sum(classification==0),bead.fraction=(bead.pos=sum(classification==1)/n()))
aa$col<-substr(aa$well,start = 1,stop = 1)
aa$row<-substr(aa$well,start = 2,stop = 3)


kable(head(aa,24))
Bead.ID well count bead.pos bead.neg bead.fraction col row
12 Files//A1 70 0 70 0.0000000 F il
12 Files//A10 161 0 161 0.0000000 F il
12 Files//A11 139 0 139 0.0000000 F il
12 Files//A12 134 0 134 0.0000000 F il
12 Files//A2 119 0 119 0.0000000 F il
12 Files//A3 139 0 139 0.0000000 F il
12 Files//A4 119 0 119 0.0000000 F il
12 Files//A5 135 0 135 0.0000000 F il
12 Files//A6 151 0 151 0.0000000 F il
12 Files//A7 117 0 117 0.0000000 F il
12 Files//A8 141 0 141 0.0000000 F il
12 Files//A9 129 0 129 0.0000000 F il
13 Files//A1 57 25 32 0.4385965 F il
13 Files//A10 119 0 119 0.0000000 F il
13 Files//A11 88 4 84 0.0454545 F il
13 Files//A12 114 3 111 0.0263158 F il
13 Files//A2 102 35 67 0.3431373 F il
13 Files//A3 106 3 103 0.0283019 F il
13 Files//A4 88 6 82 0.0681818 F il
13 Files//A5 87 4 83 0.0459770 F il
13 Files//A6 107 8 99 0.0747664 F il
13 Files//A7 88 4 84 0.0454545 F il
13 Files//A8 114 76 38 0.6666667 F il
13 Files//A9 113 5 108 0.0442478 F il

21.5 Interpretation

The bead.fraction is the fraction of all beads in the well which were above the n SD threshold for positivity. This is a digital count converted in to a fraction, which allows a continuous measure to be gained for each

ggplot(aa,aes(well,bead.fraction))+
  geom_bar(stat="identity")+
  facet_wrap(.~Bead.ID)+
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))