**10**wrote:

**I want to use the output values of voomWithQualityWeights as an input for further analysis (e.g., input for network).**

**The outputs of voomWithQualityWeights are:**

1. The $E should actually be: logCpm+0.5 for each gene regardless of voom normalization.

That does not include the voom normalization.

2. weights- are "numeric matrix of inverse variance weights" .

These are probably only the weights for each observation (observation= gene X sample).

3. $sample.weights- weight normalization per sample

**How do I extract the voom normalized value for each observation? Is it $E*weight of each observation? (just multiplication?)? ****How should I combine the sample weight?**

**Thank you.**

**38k**• written 22 months ago by harelarik •

**10**

The '

`weights`

' output from running`voomWithQualityWeights()`

already combines both the observational and sample-specific weights.These weights are relative though and only make sense in the context of a linear model (i.e. in a per gene analysis).

I would not recommend just multiplying the logCPM values by them and using them in your network analysis.

780Thank you very much.I. Are the $E values are "logCpm+0.5" transformation of raw counts or the of TMM values (made by calcNormFactors(y))?II.Considering one found Differentially expressed genes, based on voom values, and would like to continue to downstream analysis (e.g., network analysis).Which values would you think would be most consistent to use:* This question was answered by Gordon below.a.The $E (logCpm+0.5) values?b.Some other form of variance stabilization values.For example the out put of the

`varianceStabilizingTransformatio() function from`

DESeq2?c.Other option?10