# Logistic Regression Splicing Filter

This is an R based filter so will only be available if you have configured R in the SeqMonk preferences and installed the pre-requisite packages

The logistic regression splicing filter is useful for detecting certain classes of alternate splicing events in RNA-Seq data. There are some pre-requisites for using this filter.

1. You must be analysing RNA-Seq data which has been imported setting the option to "Import Introns Rather than Exons" so that the data you see are the introns from splicing events
2. You must have generated probes such that the probes lie directly over introns. You would normally to this using the Read Position probe generator, but you could also theoretically use the Feature probe generator to specifically define introns
3. Your quantitation must be a raw count of how many times that intron was observed in each sample. This would normally be achieved using the Exact Overlap Count quantitation method with all of the normalisation options turned off.
4. Your datasets must be assembled into replicate sets with at least 3 replicates per set

What the filter does is to identify putative splicing events by finding pairs of probes whose start or end positions are exactly the same. It then uses a logistic regression test to test whether the ratio of the counts in these two introns changes significantly between the two replicate sets you're testing. A change in the ratio would imply a change in the splicing decisions being made by that transcript.

This filter will therefore find differential splicing events where the location of the splice donor or splice acceptor changes, but will not find events such as novel splice introductions, or changes from a splice to reading through an intron. For these types of change you would need to use a DESeq2 comparison on the splice junction count data

## Options

- You need to two Replicate Sets to compare from the list on the left.
- You need to select a p-value cutoff for the filter
- You can choose to pre-filter your data by the minimum count an intron has across all the samples in a condition
- You can choose whether to apply multiple testing correction (you pretty much always want to leave this turned on)