Thanks for the help! But when I tried it, it does not work the same way I
want. :(
after combining the two matrices, they look like this:
V1 V2 X TEL.AML1.C41
Hyperdip.50.C23
1 TEL.AML1.C41 1 TEL.AML1.C41 1.0000000 0.00000000
2 Hyperdip.50.C23 1 Hyperdip.50.C23 0.0000000 1.00000000
3 BCR.AB.LC1 1 BCR.AB.LC1 0.1212121 0.78125000
4 Hyperdip.50.C13 1 Hyperdip.50.C13 0.0000000 1.00000000
6 TEL.AML1.9 1 T.ALL.C5 0.0000000 0.03225807
7 TEL.AML1.8 1 TEL.AML1.9 1.0000000 0.00000000
8 Hyperdip.50.C7 1 TEL.AML1.8 1.0000000 0.00000000
9 TEL.AML1.C37 1 Hyperdip.50.C7 0.0000000 1.00000000
11 TEL.AML1.C47 1 TEL.AML1.C37 1.0000000 0.00000000
13 Hyperdip.50.11 1 MLL.6 0.0000000 0.03225807
when i do : orddata1 <- df2_a[order(df2_a[,1],decreasing=T),]
I get the result:
V1 V2 X TEL.AML1.C41
Hyperdip.50.C23
22 TEL.AML1.C49 1 TEL.AML1.2M.1 1 0.00000000
11 TEL.AML1.C47 1 TEL.AML1.C37 1 0.00000000
1 TEL.AML1.C41 1 TEL.AML1.C41 1 0.00000000
9 TEL.AML1.C37 1 Hyperdip.50.C7 0 1.00000000
6 TEL.AML1.9 1 T.ALL.C5 0
0.03225807
7 TEL.AML1.8 1 TEL.AML1.9 1
0.00000000
16 TEL.AML1.2M.4 1 Hyperdip.50.11 0 1.00000000
19 TEL.AML1.2M.1 1 TEL.AML1.2M.4 1 0.00000000
15 Hyperdip.50.R2 1 T.ALL.C10 0
0.00000000
20 Hyperdip.50.C9 1 BCR.ABL.Hyperdip.R5 0 1.00000000
The results are not right! I want it to look for the gene TEL.AML1.C49 in
the second matrix and group it accordingly.
Aparna
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