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I want to compare the copy numbers of a mouse pup bam to the parents.
Example:
CopynumsNormed has 3 samples in order: parent1, parent2, pup
pupVparent1 <- compareToReference( CopynumsNormed, c( NA, FALSE, 1 ) )
gives me the pup copy numbers scaled by parent 1, and parent 1 unchanged.
All the mice have deletions compared to the mm10 reference. Where the parent copy number is 0, the scaled value for the pup is also 0, regardless of the pup copy number.
This is quite misleading output. It makes bins which are missing in both look like a new deletion in the pup. If a bin is empty in a parent but present in the pup, it still looks like a deletion when it is actually more similar to an amplification
I think it would be better to add a small value to zero scores before scaling.
Then
0/0 -> 1
small-value / 0 -> 1 + delta
moderate-value / 0 -> very-large-value
Jocelyn
The text was updated successfully, but these errors were encountered:
Hi,
I want to compare the copy numbers of a mouse pup bam to the parents.
Example:
CopynumsNormed has 3 samples in order: parent1, parent2, pup
pupVparent1 <- compareToReference( CopynumsNormed, c( NA, FALSE, 1 ) )
gives me the pup copy numbers scaled by parent 1, and parent 1 unchanged.
All the mice have deletions compared to the mm10 reference. Where the parent copy number is 0, the scaled value for the pup is also 0, regardless of the pup copy number.
This is quite misleading output. It makes bins which are missing in both look like a new deletion in the pup. If a bin is empty in a parent but present in the pup, it still looks like a deletion when it is actually more similar to an amplification
I think it would be better to add a small value to zero scores before scaling.
Then
0/0 -> 1
small-value / 0 -> 1 + delta
moderate-value / 0 -> very-large-value
Jocelyn
The text was updated successfully, but these errors were encountered: