[BioC] What wrong with my data using LIMMA

Naomi Altman naomi at stat.psu.edu
Mon Sep 5 03:57:23 CEST 2005


Dear Han Weinong,

What makes you think there is something wrong with your data?  You appear 
to have no statistically significant results.  How to deal with this has 
come up previously on this list.  e.g. Toptable sorts the genes in order of 
most likely to be differentially expressed - you could use real time PCR on 
some of the genes at the top of the list.

--Naomi Altman

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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