# Intensity Difference Filter

The intensity difference filter is a simple filter which can be used to compare two or more data stores. It can assign a p-value to each individual probe without the need for replicates. It is designed to work with datasets which are largely similar, but which show an intensity dependent effect on the variability within the data, normally such that higher values show lower variability and vice versa.

The filter works by constructing a local distribution of differences from probes with similar average intensity to the probe being examined. The set of differences is then modelled with a normal distribution and this model is used to assign a probability of the probe being examined falling within that distribution. At low intensities the differences distribution will tend to show high variability, and at high intensities the variability will be lower, so the p-values calculated take this change in variability into account.

The number of probes in the local distribution is either 1% of the total probes examined, or 100 probes, whichever is the larger. Where multiple data stores are selected then all possible pairwise combinations of stores are examined, and the lowest p-value from that set is assigned to the probe.

## Options

- You can set the p-value threshold below which probes will be kept.
- You can choose whether to apply Benjamini and Hochberg multiple testing correction. The use of multiple testing correction is required when this test is providing a primary p-value for any analysis, but can be omitted in some cases where this is used as a secondary filter akin to a dynamic fold change filter, and where the p-value will not be reported.
- You can pick the number of points which will be sampled to make up the distribution used for testing. This value will initially be calculated as 1% of your total probes, or a minimum of 100 probes, but you can change this to a different value if you think the default is inappropriate for some reason