Dear all,

I have qPCR data obtained with Fluidigm biomark array 96*96 with HTqPCR package (it's not digital PCR). I have n.features=78 (including 17 duplicated genes) and n.data=84 (30 duplicated samples, 5 duplicated points (standard curve for efficiency), 1 duplicated calibrator sample, and 4 single samples).

I want to analyze my data by taking into account duplicates but failed. I tried several things including changeCtLayout without success. Any help will be greatly appreciate.

Here are my code

Regards

# Analyses with HTqPCR fluidigm array 96*96 (78*84) ####

m(list=ls())
library("HTqPCR")
workDir <- "C:/Analyses_HTqPCR"
setwd(workDir)
temp<-read.csv("qcg.csv",as.is=TRUE,skip=11, sep=";")
raw1 <- readCtData(files="qcg.csv", format="BioMark", n.features=78, n.data=84 ,
                    sep=";" ,
                    column.info = list(flag="Call", feature="Name.1", position="ID", Ct="Value"),
                    samples=temp$Name[seq(1,nrow(temp),78)])
plotCtArray(raw1)
datacg<-read.csv(file = "pData_cg.csv", sep=";")
dim(datacg)
[1] 84  4
## adding sample info to pData###
pData(raw1)<-data.frame(sample=datacg$sample,woman= datacg$woman,group= datacg$group, replicat =datacg$replicat)
head(pData(raw1))
  sample woman group replicat
1    84b    84   CTRL        2
2     84    84   CTRL        1
3   Pp80  Pp80   TREAT        1
4    28b    28   CTRL        2
5   Pp40  Pp40   TREAT        1
6     28    28   CTRL        1

#define order sample #
order<-(c("84","84","Pp80","28","Pp40","28","Pp20","27","Pp10","27","105","105","89","89","85","85"))
raw_o<-changeCtLayout(raw1,sample.order = order)
Error in data.frame(..., check.names = FALSE) :
  arguments imply differing number of rows: 2, 0, 1

 sessionInfo()
R version 3.3.3 (2017-03-06)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows >= 8 x64 (build 9200)
attached base packages:
[1] parallel  stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] HTqPCR_1.28.0       limma_3.30.13       RColorBrewer_1.1-2  Biobase_2.34.0      BiocGenerics_0.20.0

loaded via a namespace (and not attached):
[1] gtools_3.5.0          bitops_1.0-6          affy_1.52.0           stats4_3.3.3        
[5] KernSmooth_2.23-15    BiocInstaller_1.24.0  gplots_3.0.1          zlibbioc_1.20.0     
[9] gdata_2.17.0          affyio_1.44.0         preprocessCore_1.36.0 tools_3.3.3         
[13] rsconnect_0.7         caTools_1.17.1