[BioC] What wrong with my data using LIMMA
Naomi Altman
naomi at stat.psu.edu
Mon Sep 5 03:59:49 CEST 2005
See this thread: Re: [BioC] adjusted p-values for large number of genes...
At 08:53 PM 9/4/2005, weinong han wrote:
>Hi. List,
>
>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.
> >
> >What is wrong with my data? How to do next?
> >
> >Any advice and suggestions will be much appreciated.
> >
> >I am looking forward to your response
>
>
>
>
>
>
>Best Regards
>
>Han Weinong
>hanweinong at yahoo.com
>
>__________________________________________________
>
>
>
>
> > 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
> >
>_______________________________________________
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Naomi S. Altman 814-865-3791 (voice)
Associate Professor
Dept. of Statistics 814-863-7114 (fax)
Penn State University 814-865-1348 (Statistics)
University Park, PA 16802-2111
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