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RUVseq vs SVA/CombatSeq vs other -- if we are going to have a standardized option for finding and removing sources of unknown variability, we may want to first determine which of these method is best
○ Along those lines, there are QC steps that can/should be done which should be included in determining which unknown source of variation might correspond to known sources of variation in the data; this is important to avoid overfitting the model, if confounding variables are included (known or not)
§ For example, correlating SVs (or equivalent in RUVSeq) with principal components and aspects of metadata
The text was updated successfully, but these errors were encountered:
RUVseq vs SVA/CombatSeq vs other -- if we are going to have a standardized option for finding and removing sources of unknown variability, we may want to first determine which of these method is best
○ Along those lines, there are QC steps that can/should be done which should be included in determining which unknown source of variation might correspond to known sources of variation in the data; this is important to avoid overfitting the model, if confounding variables are included (known or not)
§ For example, correlating SVs (or equivalent in RUVSeq) with principal components and aspects of metadata
The text was updated successfully, but these errors were encountered: