Hey all, I’m using the Statistical Analysis [metadata table] option for our multi-factor analyses. I performed linear model with covariant analysis. I did successfully obtained features after covariant removal. However, there is no option that allows the user to obtain the feature table with covariants adjusted. Only thing I could get was the list of features with p values and adjusted p values after covariant removal. Is it possible to add this option so I can download the feature table with peak intensities/concentrations adjusted after covariant analyses? thanks.
I think you mix two different approaches. I copy the text from our Nature Protocol (2022). The data you request is defined for removal approach.
Box 4 Covariate adjustment
Batch effect adjustment versus removal for omics data
In large-scale omics studies, systematic differences are commonly observed across samples measured at different time periods or locations, which are often called batch effects. Computational strategies for dealing with batch effects can be put into two types: removal and adjustment. Removal methods perform batch effect correction before statistical analysis, while adjustment methods include batch variables within the model for statistical analysis, such that differences associated with batch are accounted for when looking for significant differences associated with the primary variable of interest.
Thank you for the reply, Dr. Xia! I meant the adjustment methods using the linear model. In your paper, it says 59 Explore the covariate adjustment results . The pop-up message tells us that, after covariate adjustment, there are 333 metabolites with P < 0.05. As shown in Fig. 9, adjusting for the covariates improved the significance of the TCE coefficient for many features, ultimately increasing the number past the threshold from 216 to 333. While we can visualize each feature in the program, how do I obtain the csv files of the feature table after the adjustment being applied? Assume the feature table value will change after the covariate adjustment.
The p values will change, not feature values themselves. You can click the table icon or go to the download page to view the feature detail table
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