Tackling the Widespread and Critical Impact of Batch Effects in High-Throughput Data.¶
Leek, J. T., Scharpf, R. B., Bravo, H. C., Simcha, D., Langmead, B., Johnson, W. E., Geman, D., et al. (2010). Tackling the Widespread and Critical Impact of Batch Effects in High-Throughput Data. Nature Reviews Genetics, 733-739.
Cited by¶
1 citation across 1 artifact.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Cross-Dimensional Leakage
- Machine learning: a confounding feature correlated with the target inflates the apparent importance of other features that co-vary with it; importance diagnostics partially reveal but cannot fully resolve this without experimental decoupling. Genomics: batch effects — assay-batch variance contaminates apparent biological signal across many genes at once; correction methods were built specifically to partition this leakage.
This sourceDocuments assay-batch variance contaminating apparent biological signal across many genes at once and the need for correction.
Supported in partVerified against the work's full text
Supports the genomics half (batch effects touch many genes and correction/SVA methods exist); the machine-learning half of the sentence is not in this paper.
“This suggests that batch effects influence a large percentage of the measurements from genomic technologies.”
- Machine learning: a confounding feature correlated with the target inflates the apparent importance of other features that co-vary with it; importance diagnostics partially reveal but cannot fully resolve this without experimental decoupling. Genomics: batch effects — assay-batch variance contaminates apparent biological signal across many genes at once; correction methods were built specifically to partition this leakage.
Verification¶
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Registry ID ref:f7b84f7614c5 · see in the full table