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May I know if it's possible to designate the base_level to multiple values or only one? If have multiple groups/comparisons, need to run separately (separate the units file and the sample file)?
Thank you very much!
diffexp:
# samples to exclude (e.g. outliers due to technical problems)
exclude:
# model for sleuth differential expression analysis
models:
model_X:
full: ~condition + batch
reduced: ~batch
# Binary valued covariate that shall be used for fold change/effect size
# based downstream analyses.
primary_variable: condition
# base level of the primary variable (will be considered as denominator
# in the fold change/effect size estimation).
**base_level**: untreated
# significance level to use for volcano, ma- and qq-plots
The text was updated successfully, but these errors were encountered:
The base_level: is only meant for calculating fold changes in expression. And this usually only makes sense against some control condition, like a set of untreated samples in the example. Thus, you can only have one base_level per model that you compare against. You could hack around this, by specifying the same model twice, only differing in the base_level that you specify. But you then have to double-check that your comparisons both make sense.
I hope that helps?
That said, maybe you want to do something else, e.g. do multiple group comparisons. For that, you can have any number of levels in the primary_variable: column (condition in the example). So you could for example have values genotype_X, genotype_Y, and genotype_Z in the respective column of your samples.tsv.
Dear. author,
Thanks a lot for the great tools!
May I know if it's possible to designate the base_level to multiple values or only one? If have multiple groups/comparisons, need to run separately (separate the units file and the sample file)?
Thank you very much!
The text was updated successfully, but these errors were encountered: