Entering edit mode
Hi, Dear List:
I am still looking for some explanation or diagnosis about the
following potential issue that I am not sure what I did is wrong or
fine (I apologize if my previous post is not quite clear to the list)
I am using limma to do the paired test on the following setting, my
tar object looks like as below:
> tar[1:5,]
AccNum Patient_Type_Comb Type RACE ER
67 S10184 S10184_N_W_NEG N W NEG
66 S10184 S10184_T_W_NEG T W NEG
68 S10330 S10330_N_B_NEG N B NEG
69 S10330 S10330_T_B_NEG T B NEG
74 S10601 S10601_N_W_POS N W POS
AccNum is the patient ID and the same patient have two types of
samples: "N" for normal, "T" for tumor,
Two Races in the sample population: W for "White", B for "Black"
ER is for ER status: NEG for negative, POS for positive
Patient_Type_Comb column is for showing the sample phenotype in one
string
The goal of the analysis is looking for the differential gene lists
for each of the contrasts including ER positive tumor vs ER positive
normal for matched same patient of only Black population (Africa
America population), ER negative tumor vs ER negative normal for
matched same patients of only White population (Caucasian population)
etc as you can see more details in my design and contrast matrix
setting (the key is need to consider the paired samples for the same
patient with both tumor and normal samples (normal is surrounding
normal tissue of the tumor tissue for the same patient), which is well
controlled study.
My data matrix (Partial, Array data) looks like the following:
> mydata[1:5,1:4]
S10184_N_W_NEG S10184_T_W_NEG S10330_N_B_NEG S10330_T_B_NEG
7936596 10.079810 10.810695 10.733401 11.369506
8037331 10.076718 10.217359 10.921994 10.389894
8023672 8.503989 8.786565 8.936260 9.384205
8128282 5.423744 4.826185 5.872070 4.486140
8063634 5.909231 6.773356 6.653584 6.408861
Here is how I set up my design and contrast matrix:
>group1<-paste(tar$RACE,tar$Type,tar$ER, sep=".");
> unique(group1)
[1] "W.N.NEG" "W.T.NEG" "B.N.NEG" "B.T.NEG" "W.N.POS" "W.T.POS"
"B.N.POS" "B.T.POS"
> group<-factor(group1, levels=c( "W.N.NEG","W.T.NEG", "B.N.NEG",
>"B.T.NEG", "W.N.POS", "W.T.POS", "B.N.POS", "B.T.POS"))
>Samples<-factor(tar$AccNum);
design<-model.matrix(~-1+group+Samples);
> colnames(design)<-sub("group","",colnames(design));
> colnames(design)<-sub("Samples","",colnames(design));
>
con.matrix<-makeContrasts(T.POS_N.POS=B.T.POS+W.T.POS-B.N.POS-W.N.POS,
+ B.T.POS_B.N.POS=B.T.POS-B.N.POS,W.T.POS_W.N.POS=W.T.POS-W.N.POS,
+ T.NEG_N.NEG=B.T.NEG+W.T.NEG-B.N.NEG-W.N.NEG,
+ B.T.NEG_B.N.NEG=B.T.NEG-B.N.NEG, W.T.NEG_W.N.NEG=W.T.NEG-W.N.NEG,
+ levels=design)
Here is the partical contrast matrix:
> con.matrix[,]
Contrasts
Levels T.POS_N.POS B.T.POS_B.N.POS W.T.POS_W.N.POS T.NEG_N.NEG
B.T.NEG_B.N.NEG W.T.NEG_W.N.NEG
W.N.NEG 0 0 0 -1
0 -1
W.T.NEG 0 0 0 1
0 1
B.N.NEG 0 0 0 -1
-1 0
B.T.NEG 0 0 0 1
1 0
W.N.POS -1 0 -1 0
0 0
W.T.POS 1 0 1 0
0 0
B.N.POS -1 -1 0 0
0 0
B.T.POS 1 1 0 0
0 0
S10330 0 0 0 0
0 0
S10601 0 0 0 0
0 0
S10618 0 0 0 0
0 0
S10929 0 0 0 0
0 0
S10940 0 0 0 0
0 0
However, when I tried to fir the data into the limma model, I run into
the following warnings, which is what I am trying asking about:
> lmFit(mydata,design)->fit1;
Coefficients not estimable: S14697 S14730 S14810 Warning message:
Partial NA coefficients for 26804 probe(s)
This warning seems not bothering the subsequent steps as shown below,
but I am not sure why I get warning here, could the list provide some
insights or clues for me? that would be highly appreciated!
>contrasts.fit(fit1, con.matrix)->fit2
> eBayes(fit2)->fit3
> allContrast<-colnames(fit3);
> allContrast
[1] "T.POS_N.POS" "B.T.POS_B.N.POS" "W.T.POS_W.N.POS"
"T.NEG_N.NEG" "B.T.NEG_B.N.NEG" "W.T.NEG_W.N.NEG"
I also did check specifically for the samples listed in the warning
message
> tar[tar$AccNum %in% c("S14697", "S14730", "S14810"),]
AccNum Patient_Type_Comb Type RACE ER
57 S14697 S14697_N_W_POS N W POS
58 S14697 S14697_T_W_POS T W POS
55 S14730 S14730_N_B_NEG N B NEG
56 S14730 S14730_T_B_NEG T B NEG
59 S14810 S14810_N_B_POS N B POS
60 S14810 S14810_T_B_POS T B POS
They appear to be common, which of all have paired samples (T vs N)
and some of which are white/black and some are ER Negative and
positive, seems not fall into any of the special category of the
phenotype.
I also check specifically for their data as below:
> mydata[1:5,c("S14697_N_W_POS", "S14697_T_W_POS", "S14730_N_B_NEG",
"S14730_T_B_NEG", "S14810_N_B_POS", "S14810_T_B_POS")]
S14697_N_W_POS S14697_T_W_POS S14730_N_B_NEG S14730_T_B_NEG
S14810_N_B_POS S14810_T_B_POS
7936596 11.024855 10.954703 10.832579 10.917364
10.631019 10.842098
8037331 9.807050 10.366058 10.285187 9.955208
10.410920 10.620751
8023672 8.734080 8.359230 8.559288 8.245623
8.613978 8.614790
8128282 5.489218 5.703427 5.026220 4.738774
5.362589 5.193500
8063634 6.562237 6.784427 6.632752 6.757525
6.887120 7.095357
Which also look normal to me.
Thanks a lot in advance for your advice and suggestion!
Best
Myi
