Removing this variation had a large impact on the smaller J and NJ datasets; it resulted in removing all interleukin-related pathways from significance.
Removing this variation is clearly desirable, but the close proximity (∼0.03 ppm) and similar patterns and lineshapes of the P. pacificus glucose and P. redivivus trehalose crosspeaks raise the possibility of inappropriate alignment of the glucose to the trehalose crosspeaks.
Thus, to correctly evaluate the statistical significance of the remaining components of variation, the normalised data matrix needs to be adjusted by removing this background variation, by subtracting out the top principal component.
To remove this biologically relevant variation component from the GAPDH and B2M expression, we identified a set of transcripts that correlated with the geometric mean of B2M and GAPDH expression at a 5% FDR (false-discovery rate).
To remove this source of variation from the database, regression analysis was used to establish the relationship between the proteasome expression level and the expression of individual probes.
To remove this source of variation we made an adenoviral vector carrying all three genes in order that all transduced cells would receive all three.
This removes any variation introduced by the protein determination, and hence provides the best comparison between sampling protocols for metabolome analysis.
Removing this component of the variation in methylation and expression would have likely precluded identification of the autoimmune component that has not been thoroughly explored by prior studies.
Removing the variation of IL23R had a larger impact on the J and NJ datasets than WTCCC.
