Hello Group,
I am trying t analyze my affymetrix (HuGene-1_0-st-v1) data using BiC.
Previously i was using different softwares for this. And this is my first
try with Bioconductor for big experiment. So thought to get some advice in
the beginning.
I have Three groups of patient: (In-vivo)
 A-Acute reaction. Patient taking a drug X develops reaction.
 R-recovered (6 weeks after acute reaction-not longer taking drug X).
 T-Tolerant. Patient on X and tolerating treatment.

Now in in-vitro study we used another constant Y
   RXY recovered and challenged with X+Y
   RY recovered challenged with only Y. RXY vs RY are to exclude effects by
Y.
  TXY tolerant and challenged with X+Y,
  TY tolerant challenged with only Y. TXY vs TY are to exclude effects by Y.

No I want to check the cross relation and effects A vs R, RvsT and Avs T
and  differentially expressed genes for each comparison. And the same in
invitro. There are not same patients in different groups, thus I think I
want to apply unpaired-t test.

This is what I tried:
> sessionInfo()
R version 2.15.1 (2012-06-22)
Platform: i386-apple-darwin9.8.0/i386 (32-bit)

locale:
[1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base

other attached packages:
 [1] statmod_1.4.15              limma_3.12.1
 annotate_1.34.1             hugene10stprobeset.db_8.0.1 org.Hs.eg.db_2.7.1

 [6] BiocInstaller_1.4.7         affycoretools_1.28.0        KEGG.db_2.7.1
              GO.db_2.7.1                 AnnotationDbi_1.18.1
[11] affy_1.34.0                 Biobase_2.16.0
 BiocGenerics_0.2.0          pd.hugene.1.0.st.v1_3.6.0   RSQLite_0.11.1

[16] DBI_0.2-5                   oligo_1.20.4
 oligoClasses_1.18.0


 rmaOligoinvivo = oligo::rma(InVivodat1)
Background correcting
Normalizing
Calculating Expression

> rmaOligoinvitro = oligo::rma(InVitrodat1)
Background correcting
Normalizing
Calculating Expression

> maplot(rmaOligoinvivo)
> maplot(rmaOligoinvitro)
> InVivoTargets
 FileName Treatment
1   MC1       A
2   MC2       A
3   MC3       A
4   MC4       A
5   MC5       A
6   MC6       A
7   MC7       R
8   MC8        R
9   MC9        R
10 MC10        R
11 MC11        R
12 MC12        R
13 MC13       T
14 MC14        T
15 MC15        T
16 MC16        T
17 MC17        T
18 MC18        T
>
InVitroTargets=readTargets("~/Desktop/Recent/Liverpool-work-related/Micro_RawData/InVitroTargets.txt")
> InVitroTargets
   FileName Treatment Batch  CD4
1  MC19       RY     1 High
2  MC20        TY     1  Low
3  MC21        RY     2 High
4  MC22        TY     2 High
5  MC23        TY     2  Low
6  MC24        RY     2 High
7  MC25        TXY     1  Low
8  MC26       RXY     1 High
9  MC27       RXY    2  Low
10 MC28        TXY    2 High
11 MC29        RXY     2 High
12 MC30      TXY    2 High

f.invivo <- factor(InVivoTargets$Treatment, levels = c("A", "R", "T"))

design.invivo <- model.matrix(~0 + f.invivo)

>

> colnames(design.invivo) <- c("A", "R", "T")

> fit.invivo <- lmFit(rmaOligoinvivo, design.invivo)

> contrast.matrix.invivo <- makeContrasts(R-A, T-R, T-A,levels =
design.invivo)

> fit2.invivo <- contrasts.fit(fit.invivo, contrast.matrix.invivo)

> fit2.invivo <-eBayes(fit2.invivo)

> topTable(fit2.invivo, coef = 1, adjust = "fdr")

           ID      logFC   AveExpr         t      P.Value adj.P.Val
B

8819  7943047 -0.3640702  4.177681 -5.395110 3.942713e-05 0.3282013
-2.023533

9675  7950951 -0.3640702  4.177681 -5.395110 3.942713e-05 0.3282013
-2.023533

18889 8043581 -0.3640702  4.177681 -5.395110 3.942713e-05 0.3282013
-2.023533

19899 8053785 -0.3640702  4.177681 -5.395110 3.942713e-05 0.3282013
-2.023533

3713  7896238  0.7731154  2.999029  4.796490 1.434510e-04 0.9552974
-2.323922

19926 8054075 -0.3816217  4.062936 -4.557543 2.424324e-04 0.9998796
-2.454618

18660 8041642 -1.0007299  4.220083 -4.290346 4.379518e-04 0.9998796
-2.607991

3759  7896284 -0.7555604  5.727302 -4.159251 5.861601e-04 0.9998796
-2.685960

6238  7917530  0.5596335 11.170012  4.117421 6.433789e-04 0.9998796
-2.711203

15545 8010622 -0.3324189  3.771856 -3.971869 8.899739e-04 0.9998796
-2.800385
I am progressing in a right way? Further I want to perform unpaired t test
for comparing AvsT and so on. Any help will be really great.
Thanks a lot ,
 Mitra.






-- 
Dr. Suparna Mitra
Wolfson Centre for Personalised Medicine
Department of Molecular and Clinical Pharmacology
Institute of Translational Medicine University of Liverpool
Block A: Waterhouse Buildings,  L69 3GL Liverpool

Tel.  +44 (0)151 795 5394, Internal ext: 55394
M: +44 (0) 7511387895
Email id: smitra@liverpool.ac.uk
Alternative Email id: suparna.mitra.sm@gmail.com

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