I have a set of seven files from runs on 48 low-throughput qpcr system. So, our runs have 48 max samples. In fact, we have 30 reactions per run. Our file is like this:

File1.csv

1   10-A1-C C9  1   IDH Endogenous Control  12.6929092407226
2   10-A1-C C10 1   IDH Endogenous Control  12.4841232299805
3   10-A1-S C11 2   IDH Endogenous Control  18.4506340026855
...
30  9-A1-S  F2  15  IDH Endogenous Control  17.3977642059326

File2.csv

1   10-A1-C A1  1   UBQ Endogenous Control  11.66
2   10-A1-C A2  1   UBQ Endogenous Control  11.7372970581054
3   10-A1-S A3  2   UBQ Endogenous Control  16.82
...
30  9-A1-S  C6  15  UBQ Endogenous Control  17.3474025726318

File3.csv

1   10-A1-C C9  1   CDPK26  Target  16.9320430755615
2   10-A1-C C10 1   CDPK26  Target  17.0587520599365
3   10-A1-S C11 2   CDPK26  Target  16.5248744964599
...
30  9-A1-S  F2  15  CDPK26  Target  16.9789012908935

...

File7.csv

1   10-A1-C C9  1   MYB Target  12.5751647949219
2   10-A1-C C10 1   MYB Target  12.959545135498
3   10-A1-S C11 2   MYB Target  10.3745765686035
...
30  9-A1-S  F2  15  MYB Target  12.228588104248

First column -> id (1 until 30)

Second column -> sample identification (10 = genotype; A1 = region 1; C = symptoms). We have three genotypes (10, 3 and 9); two regions (A1 and A2) and with symptoms and without. So, we have theses RNA samples: 10-A1-C, 10-A1-S, 10-A2-S, 3-A1-C and 9-A1-S.

third column -> well plate

fourth column -> technical replicates 1 or 2

fifth column -> gene name

sixth column -> feature type (endogenous or target)

seventh column -> Ct

I read my files like this:

files <- read.delim("files.txt")
raw  <- readCtData(files = files$File, n.features = 30,
                   column.info=list(feature = 5, type = 6, position = 3,
                                    Ct = 7), n.data = 1)

Now, I would like to do the appropriate phenoData file for, on downstream analysis do a proper statistical contrasts set up.

So, could you help me?

Thank you so much!

Marcelo