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Topic: Statistics clarification (Read 4308 times) previous topic - next topic

Statistics clarification

Hi,

I just started using XCMS online. The package has identified numerous features that are up or down regulated in my different lc-ms data sets. However, I am not sure which statistical output is most important for prioritizing identified features.

How would you compare p-value vs. q value, vs. t-statistics vs. feature score?

Separately, does does the statistics take into account the total intensity of the peaks to come up with the p-score?

Thank you in advance,

Yevgeniy

Re: Statistics clarification

Reply #1 –
The most important outputs for prioritizing features are p-value and fold change.
Typically, you would order by p-value, and filter the feature table based on thresholds like p-value <= 0.001, fold change >= 3.

The p-value is calculated from the t-statistics (Welch t-test, unequal variances).

The q-value tries to give you an estimate of the false discovery rate
and is calculated based on the distribution of the p-values.

The feature score takes all statistics and the intensity distribution of the feature into account.
However, it is still experimental.