17 samples(3 normal samples, 14 NPC tumor samples from different patients)were used in my Affymetrix microarray experiments. The small  size microarrays were recommmended to be analyzed using LIMMA. After moderated t statistic, I found the results were not so nice. please see attachment.
 
quality assessment was recommended in the first steps of data analysis. In some cases poor quality arrays will have to be dropped, but an alternative is to downweight the lower quality arrays using the arrayWeights() function in limma or array level standard errors from affyPLM. 
 
My Questions:
1. my RAM is 512M(windows xp), can the RAM size be used for 17 affymetrix chips?
2. If poor quality arrays checked, how to downweight the lower quality arrays using the arrayWeights() function in limma or array level atandard errors from affyPLM? All chips downweighted  or only poor quality arrays downweighted?
3. The downweight will change the original expression values or not?
 
Any advice and suggestions will be much appreciated.
 
dir()
>  [1] "G05.CEL"    "G09.CEL"    "G10.CEL"    "G12.CEL"    "G15.CEL"
>  [6] "G19.CEL"    "GF.CEL"     "GM.CEL"     "H044.CEL"   "H05.CEL"
>[11] "H07.CEL"    "H10.CEL"    "H11.CEL"    "H14.CEL"    "hgu133acdf"
>[16] "N01.CEL"    "N02.CEL"    "N03.CEL"
> > library(limma)
> > library(affy)
>Loading required package: Biobase
>Loading required package: tools
>Welcome to Bioconductor
>          Vignettes contain introductory material.  To view,
>          simply type: openVignette()
>          For details on reading vignettes, see
>          the openVignette help page.
>Loading required package: reposTools
> > Data <- ReadAffy()
> > eset <- rma(Data)
>Background correcting
>Normalizing
>Calculating Expression
> > pData(eset)
>          sample
>G05.CEL       1
>G09.CEL       2
>G10.CEL       3
>G12.CEL       4
>G15.CEL       5
>G19.CEL       6
>GF.CEL        7
>GM.CEL        8
>H044.CEL      9
>H05.CEL      10
>H07.CEL      11
>H10.CEL      12
>H11.CEL      13
>H14.CEL      14
>N01.CEL      15
>N02.CEL      16
>N03.CEL      17
> > tissue <- 
> 
c("C","C","C","C","C","C","C","C","C","C","C","C","C","C","N","N","N")
> > design <- model.matrix(~factor(tissue))
> > colnames(design) <- c("C", "CvsN")
> > design
>    C CvsN
>1  1    0
>2  1    0
>3  1    0
>4  1    0
>5  1    0
>6  1    0
>7  1    0
>8  1    0
>9  1    0
>10 1    0
>11 1    0
>12 1    0
>13 1    0
>14 1    0
>15 1    1
>16 1    1
>17 1    1
>attr(,"assign")
>[1] 0 1
>attr(,"contrasts")
>attr(,"contrasts")$"factor(tissue)"
>[1] "contr.treatment"
>
>
> > fit <-lmFit(eset,design)
> > fit <-eBayes(fit)
> > options(digits=2)
> > topTable(fit,coef=2,n=50,adjust="fdr")
>                ID     M   A    t P.Value    B
>22193  78047_s_at  0.60 7.3  5.3    0.82 -3.4
>2594  203065_s_at -1.26 6.7 -5.0    0.82 -3.5
>10680 211245_x_at  0.58 4.9  4.7    1.00 -3.6
>17919 218554_s_at  0.59 4.7  4.5    1.00 -3.6
>9431  209945_s_at -0.67 6.1 -4.5    1.00 -3.6
>4556  205029_s_at  3.09 3.6  4.4    1.00 -3.6
>4557    205030_at  3.58 4.6  4.3    1.00 -3.6
>5845  206319_s_at  0.82 4.0  4.3    1.00 -3.7
>21838    36019_at  0.67 6.7  4.2    1.00 -3.7
>5209  205682_x_at  0.61 4.8  4.2    1.00 -3.7
>6791  207266_x_at -0.95 7.8 -4.0    1.00 -3.7
>21916    38447_at  0.66 7.3  4.0    1.00 -3.7
>21914    38340_at  0.59 6.3  3.9    1.00 -3.8
>16241   216871_at  0.59 3.4  3.9    1.00 -3.8
>982   201454_s_at -0.65 6.2 -3.9    1.00 -3.8
>22024    46256_at  0.62 7.2  3.9    1.00 -3.8
>7489  207978_s_at  0.47 4.3  3.8    1.00 -3.8
>4452    204925_at  0.48 5.0  3.8    1.00 -3.8
>7121    207600_at  0.48 5.5  3.7    1.00 -3.8
>12443 213060_s_at  1.41 6.0  3.7    1.00 -3.8
>1619    202091_at  0.51 3.3  3.7    1.00 -3.8
>9890    210412_at  0.53 3.5  3.6    1.00 -3.8
>21922  38707_r_at  0.45 7.8  3.6    1.00 -3.9
>2715    203187_at  0.59 5.8  3.6    1.00 -3.9
>3354    203827_at -0.99 5.5 -3.6    1.00 -3.9
>5340  205813_s_at  0.52 5.8  3.5    1.00 -3.9
>2445  202916_s_at -0.61 6.1 -3.5    1.00 -3.9
>18810   219446_at -0.68 5.9 -3.5    1.00 -3.9
>14010   214632_at -0.54 4.2 -3.4    1.00 -3.9
>2915    203388_at  0.46 6.2  3.4    1.00 -3.9
>21936    396_f_at  0.70 7.7  3.4    1.00 -3.9
>16292 216922_x_at  0.61 3.8  3.4    1.00 -3.9
>13378   213999_at  0.44 4.5  3.4    1.00 -3.9
>9642    210158_at  0.58 4.4  3.4    1.00 -3.9
>19117   219753_at  0.65 5.6  3.4    1.00 -3.9
>10820 211405_x_at  0.53 5.3  3.4    1.00 -3.9
>19242 219878_s_at -0.58 4.5 -3.4    1.00 -3.9
>3275  203748_x_at -0.90 7.9 -3.4    1.00 -3.9
>16554   217187_at  0.58 5.7  3.4    1.00 -3.9
>8627  209133_s_at  0.54 4.7  3.3    1.00 -3.9
>17983 218618_s_at -1.15 8.0 -3.3    1.00 -3.9
>20977   221615_at  0.50 3.7  3.3    1.00 -3.9
>18562   219198_at  0.54 5.7  3.3    1.00 -3.9
>19513   220149_at  0.58 4.8  3.3    1.00 -3.9
>1770    202242_at  1.04 5.4  3.3    1.00 -3.9
>10081 210616_s_at -0.56 8.4 -3.3    1.00 -3.9
>17995   218630_at  0.37 5.4  3.3    1.00 -3.9
>3018  203491_s_at -0.67 5.1 -3.3    1.00 -3.9
>10823 211410_x_at  0.56 5.3  3.3    1.00 -3.9
>16351 216981_x_at  0.57 6.3  3.3    1.00 -3.9


Best Regards
 
Han Weinong  
hanweinong@yahoo.com

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