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simultaneous use of robust and weighting methods in limma.
limma arrayweights robust
updated 8.1 years ago by 47k • written 9.1 years ago by ▴ 80
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simultaneous use of robust and weighting methods in limma
limma robust arrayWeights
9.1 years ago • updated 6.7 years ago Gordon Smyth 47k
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Comment: Error in .order_seqlevels(chrom_sizes[, "chrom"]) : !anyNA(m32) is not TRUE by Hervé Pagès 16k
See https://github.com/Bioconductor/GenomeInfoDb/issues/82#issuecomment-1409482932
Comment: Error in .order_seqlevels(chrom_sizes[, "chrom"]) : !anyNA(m32) is not TRUE by Hervé Pagès 16k
See https://github.com/Bioconductor/GenomeInfoDb/issues/82#issuecomment-1409482932
Comment: Sva and batch effect by Gordon Smyth 47k
(+1). I find that ComBat tends to introduce spurious differential expression so, you think there's a batch effect affecting the variances a…
Comment: Advice for the analysis of microarray data with limma by Gordon Smyth 47k
You mean references on orthogonal polynomials? Polynomial approximation is a classic topic in mathematics and statistics and there are many…
Comment: Limma-voom with inverse probability weights (similar to sample weights) by Gordon Smyth 47k
The potential problem isn't with empirical Bayes with the fact that the variance estimate from your method A linear model might not be mean…
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Answer: Sva and batch effect
Answer: AnnotationForge::makeOrgPackage "argument is missing, with no default" Errors
Comment: Error in .order_seqlevels(chrom_sizes[, "chrom"]) : !anyNA(m32) is not TRUE
Comment: Error in .order_seqlevels(chrom_sizes[, "chrom"]) : !anyNA(m32) is not TRUE
Answer: Limma-voom with inverse probability weights
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