Methods and apparatus for identifying alternative splicing events

A method of identifying alternative splicing events, the method comprising:

• receiving a dataset of percent spliced in (PSI) values for each of a plurality of biological samples, wherein the plurality of biological samples includes a first population of samples having a first characteristic and a second population of samples having a second characteristic different from the first characteristic;
• fitting to the dataset, by at least one computer processor, a probabilistic model to identify clusters of samples in the dataset;
• calculating cluster characteristics for each of the clusters;
• filtering the clusters based, at least in part, on the cluster characteristics to identify a subset of clusters, each of which is associated with an alternative splicing event;
• and
• storing on at least one storage device, information associated with the identified alternative splicing events.

A non-transitory computer readable medium encoded with a plurality of instructions that, when executed by at least one computer processor perform a method, the method comprising:

• receiving a dataset of percent spliced in (PSI) values for each of a plurality of biological samples, wherein the plurality of biological samples includes a first population of samples having a first characteristic and a second population of samples having a second characteristic different from the first characteristic;
• fitting to the dataset, a probabilistic model to identify clusters of samples in the dataset;
• calculating cluster characteristics for each of the clusters;
• filtering the clusters based, at least in part, on the cluster characteristics to identify a subset of clusters, each of which is associated with an alternative splicing event;
• and
• storing on at least one storage device, information associated with the identified alternative splicing events.

A method of treating a patient, the method comprising:

• receiving a sample from the patient;
• assaying the sample to determine whether the sample includes one or more of a plurality of alternative splicing events included in an alternative splicing event panel;
• and
• providing a treatment to the patient based on whether the sample includes one or more of the alternative splicing events included in the panel,
• wherein the plurality of alternative splicing events included in the panel has been determined by:
• fitting a probabilistic model to a dataset of percent spliced in (PSI) values for each of a plurality of biological samples to identify clusters of samples in the dataset, wherein the plurality of biological samples includes a first population of samples having a first characteristic and a second population of samples having a second characteristic different from the first characteristic;
• calculating cluster characteristics for each of the clusters;
• and
• filtering the clusters based, at least in part, on the cluster characteristics to identify a subset of clusters, each of which is associated with an alternative splicing event in the alternative splicing event panel.