Comments (2)
Both are very good questions and ones that we are addressing extensively in our paper revision. Briefly, yes, the 3' bias will limit the number of SNPs detectable and thus limit the power to identify smaller CNVs using the allele-based approach hb$calcAlleleCnvBoundaries
. My benchmarks so far suggests that the method will still be useful for whole chromosome or chromosome arm level alterations. The expression-based approach hb$calcGexpCnvBoundaries
would provide comparable resolution under the assumption that your 10X data contains primarily one cell type and thus an appropriate reference expression dataset for expression normalization can be identified. Otherwise, you may need to cluster your data by cell type first, and then normalize to the appropriate reference expression dataset. I am also currently benchmarking speed, which is tricky for 10X data because you have more cells but fewer SNPs and fewer genes. Speed is also highly dependent on the complexity of the subclonal structure.
I would always recommend assessing your data visually using hb$plotGexpProfile
and hb$plotAlleleProfile
as sanity checks.
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Thanks for the useful and prompt comment. Last question, do you have a favourite normalization for 10X data?
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Related Issues (20)
- lt$setGexpDev HOT 1
- Warning in setGeneFactors and error in retestIdentifiedCnvs
- Allele-mode for selecting normal cells HOT 2
- 10X + Honeybadger question HOT 2
- Error in summarizeResults HOT 3
- Error in calcGexpCnvBoundaries when running with numeric chromosome names HOT 1
- Error: subscript contains invalid names HOT 9
- read bam files HOT 4
- Filtering of identified CNVs HOT 2
- speed of running setAlleleMats step
- What is the reason for only including snps in HoneyBADGER? HOT 2
- gene filtering different in HoneyBADGER object HOT 3
- Error: Failed to install 'HoneyBADGER' from GitHub HOT 2
- Showing error when trying Getting_Started.Rmd
- no method for coercing this S4 class to a vector HOT 3
- showing NULL in the step of calcGexpCnvBoundaries - getting started tutorial HOT 12
- Applying bayesian hierarchical model in integrating analyses tutorial
- error in hb$summarizeResults
- Error in summarizeResults for the allele-based approach
- Error in calcCombCnvProb HOT 1
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