And the combineScores function will return the max of this vector, even if BMA was selected as combine method.
This will result in the wrong similarity of 1 for any two gene-sets where one has only a single GO term which is also part of the other set's GO terms.
To fix it, you should add a drop=F to the above matrix indexing, to force getting a matrix result:
scores[i,j] <- combineScores(go_matrix[gos1, gos2, drop=F], combine=combine)
GuangchuangYu
commented
4 months ago
Hi, first of all, thank you for the great semantic similarity package!
I discovered an issue in the mclusterSim function: If a gene-set (cluster) has a single GO term, the result of the following matrix indexing returns a vector, instead of the similarity matrix: https://github.com/YuLab-SMU/GOSemSim/blob/26bb3f65cada0f1c9f240e1d84872531ca65f949/R/mclusterSim.R#L56
And the
combineScoresfunction will return themaxof this vector, even ifBMAwas selected as combine method. This will result in the wrong similarity of 1 for any two gene-sets where one has only a single GO term which is also part of the other set's GO terms.To fix it, you should add a
drop=Fto the above matrix indexing, to force getting a matrix result:scores[i,j] <- combineScores(go_matrix[gos1, gos2, drop=F], combine=combine)kind regards, Ulrich