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