Dear all (and we thing especially Heidi):

we are using HTqPCR to analyze a set of cards which we casted on this 
format from the original one (being the only one being accepted in input):

2    Run05    41    Passed    sample 41    ABCC5    Target    30
3    Run05    41    Passed    sample 41    ADM    Target    31.3
4    Run05    41    Passed    sample 41    CEBPB    Target 29.8
5    Run05    41    Passed    sample 41    CSF1R    Target 31.2
6    Run05    41    Passed    sample 41    CXCL16    Target 26.9
7    Run05    41    Passed    sample 41    CYC1    Target 25.7
8    Run05    41    Passed    sample 41    DYNLT1    Target 25.8
9    Run05    41    Passed    sample 41    EREG    Target 35.6
10    Run05    41    Passed    sample 41    ERH    Target 25.9
11    Run05    41    Passed    sample 41    FGD4    Target    40
12    Run05    41    Passed    sample 41    GPX1    Target 20.4
[...]

The total number of files and groups is as follows (this is the file 
"Elenco_1.txt" which is used below):

File    Group
41.txt    Sano
39.txt    Sano
37.txt    Sano
35.txt    Sano
43.txt    Sano
34.txt    Sano
44.txt    Sano
38.txt    Sano
48.txt    Sano
40.txt    Sano
47.txt    Sano
6.txt    Non Responder DISEASE
26.txt    Non Responder DISEASE
2.txt    Non Responder DISEASE
69.txt    Non Responder DISEASE
68.txt    Non Responder DISEASE
5.txt    Non Responder DISEASE
71.txt    Responder DISEASE
3.txt    Responder DISEASE
17.txt    Responder DISEASE
1.txt    Responder DISEASE
19.txt    Responder DISEASE

The comparison is DISEASE vs non DISEASE, but what leaves us dubious is 
the normalization part.

Here is the code up to the dump of the normalized values matrices:

library("HTqPCR")
path <- ("C:/Users/BRINIEL/Desktop/new_analisi_card1/analisiAeB/")
files <- read.delim (file.path(path, "Elenco_1.txt"))
files
filelist <- as.character(files$File)
filelist
raw <- readCtData(files = filelist, path = path, n.features=46, type=7, 
flag=NULL, feature=6, Ct=8, header=FALSE, n.data=1)
featureNames (raw)
raw.cat <- setCategory(raw, Ct.max=36, Ct.min=9, replicates=FALSE, 
quantile=0.9, groups =files$Group, verbose=TRUE)

*s.norm <- normalizeCtData(raw.cat, norm="scale.rank")**
**exprs(s.norm)**
**write.table(exprs(s.norm),file="Ct norm scaling.txt")**
**
**g.norm <- normalizeCtData(raw.cat, norm="geometric.mean")**
**exprs(g.norm)**
**write.table(exprs(g.norm),file="Ct norm media geometrica.txt")*

Now if we look at the content of the two expression value files, it 
looks like that the first column (corresponding to the first sample) is 
always unchanged, while all the others have been normalized.

In this case the first dataset is sample 41 so you can check comparing 
between the lines above and below what is happening.

We do not include here all the columns but all the samples *except *the 
first have all their values 'normalized'

*Ct norm scaling:*

     41    39    37    35    43    34    44    38
ABCC5    30    27.37706161    26.47393365    29.7721327 31.20189573    
26.39260664    26.32436019    27.54274882
ADM    31.3    30.36540284    28.51753555    32.31241706 34.40473934    
26.29800948    29.82796209    28.60208531
CEBPB    29.8    28.53383886    26.65971564    27.84151659 
30.06540284    27.3385782    27.36597156    26.29080569
CSF1R    31.2    27.66625592    28.05308057    37.18976303 
36.98767773    31.0278673    34.56255924    29.75772512
CXCL16    26.9    27.56985782    24.15165877    30.28018957 
28.82559242    25.91962085    26.89251185    26.96492891
CYC1    25.7    23.52113744    22.01516588    26.92701422 27.27582938    
22.89251185    22.53668246    23.88322275
DYNLT1    25.8    23.71393365    21.17914692    25.8092891 
26.03601896    22.89251185    22.63137441    23.01649289
EREG    35.6    31.32938389    30.18957346    35.66559242 37.29763033    
29.79810427    32.76341232    30.33554502


*Ct norm geometric*

     41    39    37    35    43    34    44    38
ABCC5    30    27.73443878    26.93934246    29.88113261 30.76352197    
26.51166676    26.8989347    27.49219508
ADM    31.3    30.76178949    29.01887064    32.4307173 33.92136694    
26.41664286    30.47900874    28.5495872
CEBPB    29.8    28.90631647    27.12839047    27.94344824 
29.64299633    27.46190571    27.96328103    26.24254985
CSF1R    31.2    28.0274082    28.5462506    37.32591991 36.46801611    
31.16783762    35.31694663    29.70310587
CXCL16    26.9    27.92975172    24.57624224    30.39104955 
28.42060473    26.03654728    27.47948724    26.91543574
CYC1    25.7    23.82817979    22.40219004    27.02559775 26.89261523    
22.99578263    23.02858438    23.83938594
DYNLT1    25.8    24.02349274    21.55147396    25.90378049 
25.67022363    22.99578263    23.12534314    22.97424694
EREG    35.6    31.73835423    30.7203028    35.7961691 36.77361401    
29.93252698    33.47853023    30.27986521

This looks a bit odd - why the first sample seems to be taken as a 
'reference' for both normalization methods and hence is left unchanged ?

Another (related ?) oddity is that in the final differential analysis 
result the same sample ID is always reported in the feature.pos field, 
as you can see below:

     genes    feature.pos    t.test    p.value    adj.p.value
22    NUCB1    41    -1.998838921    0.077900837    0.251381346
8    ERH    41    -1.958143348    0.091329532    0.251381346
16    MAFB    41    -1.887142703    0.09421993    0.251381346
28    RNF130    41    -1.904866754    0.099644523    0.251381346
3    CEBPB    41    -1.853176708    0.103563968    0.251381346
18    MSR1    41    -1.80887129    0.10432619    0.251381346

Are we doing something wrong in the data input or subsequent elaboration 
here? can we actually trust these normalizations?

Many thanks in advance - kind regards

Alessandro & Elena

---


> sessionInfo()

R version 3.0.1 (2013-05-16)

Platform: x86_64-w64-mingw32/x64 (64-bit)

locale:

[1] LC_COLLATE=Italian_Italy.1252LC_CTYPE=Italian_Italy.1252

[3] LC_MONETARY=Italian_Italy.1252 LC_NUMERIC=C

[5] LC_TIME=Italian_Italy.1252

attached base packages:

[1] parallelstatsgraphicsgrDevices utilsdatasetsmethods

[8] base

other attached packages:

[1] HTqPCR_1.14.0limma_3.16.5RColorBrewer_1.0-5 Biobase_2.20.0

[5] BiocGenerics_0.6.0

loaded via a namespace (and not attached):

[1] affy_1.38.1affyio_1.28.0BiocInstaller_1.10.2

[4] gdata_2.12.0.2gplots_2.11.0.1gtools_2.7.1

[7] preprocessCore_1.22.0 stats4_3.0.1zlibbioc_1.6.0






-- 
Alessandro Guffanti

Alessandro Guffanti

Head, Bioinformatics

*Genomnia srl*

Via Nerviano, 31/B -- 20020 Lainate (MI)

Tel. +39-0293305.702 / Fax +39-0293305.777

www.genomnia.com <http://www.genomnia.com>

alessandro.guffanti@genomnia.com <mailto:alessandro.guffanti@genomnia.com>

*P* *Per cortesia, prima di stampare questa e-mail pensate all'ambiente.*

*           Please consider the environment before printing this mail 
note.*



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