description of the outputs of PLIER() function about plier HOT 4 CLOSED

Sorry for the inconvenience. You can find the description as follows. I omit some elements which are not frequently used.

• B latent variable matrix with dimension k-by-samples. You can correlate this matrix with the phenotype of interest
• Z loading matrix with dimension genes-by-k. You can check which genes are top ranked with respect to each LV by ordering the loading values (decreasing order) in a column-wise manner
• U coefficient matrix with dimension pathways-by-k. This encodes the combination of pathways for each LV.
• C The exact pathway matrix used in the decomposition which may be different from the input
• withPrior An index vector which indicates the list of LVs that are associated with pathways
• Uauc see Up
• Up Uauc and Up are AUC and p.val matrices with the same dimension as U. They are calculated based on cross-validation to test how reliable pathways are associated with LVs. All entries with non-zero AUC are collected to formulate summary
• summary This is based on Uauc and Up which records pathway-LV pairs that are supported by cross-validation test. For a given LV, if you find the associated pathways are interesting, you may want to double check the top ranked genes for this LV by looking at Z

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Thanks, I am wondering about how I should interpret 'summary'. It looks like not all of the pathways from Up and Uauc are represented in 'summary', it seems to be because they have zero AUC. Does the cross validation test you describe refer to the "Gene-holdout cross-validation" section in your paper? if so,

1. If a pathway is present in Uauc but not 'summary', should we interpret this as 'the pathway is not well represented among the LVs' and/or 'the pathway is not found to be highly expressed in this dataset'?
2. Since I have 386 LVs in Uauc, is it correct to say that this was the number of LVs found after convergence, and is preserved throughout cross validation? However number of unique LV indexes in 'summary' is 262. Is it useful to perhaps try functional enrichment on LVs that were not assigned to any pathway?
3. Do you think it is necessary to do any kind of multiple testing correction on the p-values?

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Yes, it refers to the "Gene-holdout cross-validation" section.

1. It means there are no LVs that are associated with this pathway based on cross validation test. You may double check this by performing enrichment analysis, and/or check top ranked genes. (check the next bullet)
2. Cross validation was conducted after the decomposition finished, so these 386 LVs were just the result of convergence. There is a parameter frac=0.7 which controls the fraction of LVs that have at least 1 prior information association. That's why the number is 262. Performing pathway/function enrichment on loading (Z in the output) is recommended as a double check on all LVs. Some LVs don't have pathway association based on cross-validation, but they may be a valid pathway surrogate by looking at top-ranked genes.
3. The FDR values are already included in the summary as one column.

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Okay I see that there are FDR assigned to each pathway. Thanks very much Wayne.

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