Question: MSnset combine error message
0
gravatar for kamal.fartiyal84
8 months ago by
Cancer Research UK Cambridge Institute
kamal.fartiyal8410 wrote:

Hi,

I get the following error message on combining this dataset I have using the following command:

MSnset_comb_norr <- MSnbase::combine(MSnset_Set1,MSnset_Set2,MSnset_Set3,MSnset_Set4)

Error in combine(x[[nm]], y[[nm]]) : 
  matrix shared row and column elements differ: Mean relative difference: 0.4045317

The error occurs if I combine MSnsetSet4 as it works fine till MSnsetSet3.

For Reference the object dimensions are as below

dim(exprs(MSnset_Set4))
 12426    10

dim(exprs(MSnset_Set3))
 13260    10

dim(exprs(MSnset_Set2))
 11117    10

dim(exprs(MSnset_Set1))
 11973    10

This is first time I am getting this message as the combine function works fine otherwise. Any suggestions regarding this would be highly appreciated.

Thanks.

Kamal

msnbase • 178 views
ADD COMMENTlink modified 8 months ago • written 8 months ago by kamal.fartiyal8410
Answer: MSnset combine error message
0
gravatar for Laurent Gatto
8 months ago by
Laurent Gatto1.2k
Belgium
Laurent Gatto1.2k wrote:

From the dimensions of your 4 objects, I assume that the 10 sample (column) names are identical and that you want to combine along to features (rows). Could it be that MSnset_Set4 has a feature that exists in a previous object? This would indeed lead to such an error.

ADD COMMENTlink written 8 months ago by Laurent Gatto1.2k
Answer: MSnset combine error message
0
gravatar for kamal.fartiyal84
8 months ago by
Cancer Research UK Cambridge Institute
kamal.fartiyal8410 wrote:

The column names are not identical. I am combining about 40 different samples based on the common peptides shared among them. Features (rows) represents peptides.

ADD COMMENTlink written 8 months ago by kamal.fartiyal8410

Actually you are right, upon checking one of the column name is same across two objects. I think this generated the error. Thanks.

ADD REPLYlink written 8 months ago by kamal.fartiyal8410

Ok, let me know how it goes. The feature variables (in fData) can also lead to errors if they have the same names in different objects. For example if the number of peptides for a protein group in the first object claims that 10 peptides were found, but in the second object only 8 peptides were found. Then, you'll end up with an error because the same protein group/feature variable have conflicting information. This can be solved by removing feature variables that aren't useful/relevant anymore upon combination, or rename them using updateFvarLabels.

ADD REPLYlink written 8 months ago by Laurent Gatto1.2k
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