PAMR was almost as effective, reducing batch effects by 98%.
Each normalization could remove a portion of batch effects and their effectiveness differed depending on the severity of batch effects in a dataset.
However, when a dataset had more obvious batch effects such as Dataset 2 and 3, lumi normalization became less effective, leaving a larger portion of batch effects intact.
ComBat treats batch effects as additive and multiplicative effects.
Ignoring batch effects (our assays were designed to minimize batch effects), potential common environmental effects include stress, diet, and exposure to toxins, pathogens, radiation and chemicals.
Gene expression levels were not adjusted for possible batch effects as algorithms that attempt to adjust for batch effects also alter the gene expression distribution.
Patient and healthy control samples were processed concurrently to prevent batch effects.
Due to batch effects, these were later excluded from statistical analysis to avoid any bias.
p-values were generated in 2-way ANOVA, blocking for organ culture date batch effects.
We first measured how much each program reduces the variation caused by batch effects.
Also, Ratio_G performed worst in removing batch effects from the SMRI data.
