Defined random effects aster model:
reaster01 <- reaster(resp ~ varb + frts:(cont*site), list(block = ~ 0 + frts:subplot), pred, fam, famlist = famlist, varb, id, root, data = redata_silene)
Runs fine, but when I add a
pop random effect:
reaster02 <- reaster(resp ~ varb + frts:(cont*site), list(block = ~ 0 + frts:subplot, pop = ~ 0 + frts:cont/pop), pred, fam, famlist = famlist, varb, id, root, data = redata_silene)
I get an error:
Error in reaster.default(resp ~ varb + frts:(cont * site), list(block = ~0 + : step 1 part 1 (optim Nelder-Mead with pickle) fail
I emailed Charlie to ask about this - apparently an issue with Nelder-Mead so may not be ‘fixable’ via reaster.
Leveraging the power of git, checked out a new branch to use my full Trinity transcriptome assembly before cleaning. Re-ran expression quantification using
Sailfish and ran R script through identification of thermally-responsive transcripts. Confirmed previous results:
assembly <- rep(c("clean", "contaminated"), each = 11) colony <- rep("A22", times = 22) unmapped_ratio_clean <- c(0.8305, 0.7926, 0.8241, 0.8114, 0.8111, 0.8253, 0.8065, 0.8108, 0.8122, 0.8171, 0.8215) unmapped_ratio_contaminated <- c(0.8304, 0.7926, 0.8242, 0.8114, 0.8111, 0.8253, 0.8065, 0.8108, 0.8122, 0.8171, 0.8215) (mean.diff.unmapped <- mean(unmapped_ratio_clean - unmapped_ratio_contaminated))
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Removing ‘contaminant’ sequences from the transcriptome has significant effects on expression quantification and thus downstream analyses. How to deal with mapping reads to known contaminants?
Map reads to the complete transcriptome, including contaminants, and then remove the known contaminants. In this case, it seems that I risk incorrectly mapping reads to the contaminants and losing information (false negatives), analogous to a Type I (producer’s) error.
Map reads to the “cleaned” transcriptome. This seems analogous to a Type II (consumer’s) error where I risk finding significant changes in expression that are due to incorrectly mapping ‘contaminant’ reads to true transcripts (false positives).
Asked this question to Sailfish user’s group and posted on Biostars…
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