# Percentile Normalisation Quantitation

Although there are various normalisation options available in the inital quantitaion methods it will sometimes be the case that there are differences in the distribution of your values which mean that the normalised data are not directly comparable. For example you might have a small number of probes in which a huge number of reads suddenly appear, and this may make it appear that all other probes are decreasing where actually you'd like them to stay the same.

The Percentile Normalisation Quantitation allows you to pick a point on the distribution of values in your data and it will apply a scaling to all data stores so that they share the same value at that point. The value used at the common point will be the highest value seen in any of the data stores being normalised.

## Options

- What type of correction to apply (add or multiply). If you choose to add then a constant factor will be added to each probe value. For multiply a scalaing factor is caluculated using the difference between the current value and the minimum value in each set. Each value then has the minimum value subtracted, is multipled by the scaling factor, and then the minimum value is re-added. This has the effect of "stretching" your distribution, keeping the start value the same, but increasing everything else such that the distributions cross at the selected percetile
- Which percentile of your distribtion you would like to use to calculate the scaling factor. You should avoid using 0 or 100 since these are likely to contain extreme outliers in your set. Generally values from 50-90% are ideal, but you should look at your distributions to see if these are appropriate for your data.