Probabilistic Clustering for Alternative Splicing Identification
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Solution Overview
Problem
Conventional methods for analyzing cancer tissue samples assume uniform biological properties within each distribution, failing to accurately identify alternative splicing events that can occur in both cancerous and non-cancerous samples, leading to inadequate differentiation between populations.
Innovation Solution
A molecular profiling platform that uses probabilistic models, such as Gaussian Mixture Models, to analyze datasets of PSI values from biological samples, identifying clusters and characterizing alternative splicing events by fitting a probabilistic model to datasets of samples with different characteristics, allowing for the detection of differential splicing events in subpopulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional methods assume uniform biological properties within each distribution, then analysis is simplified, but identification accuracy of alternative splicing events deteriorates
Solution Approach 1:
The patent segments the sample population into distinct subpopulations using probabilistic clustering (e.g., Gaussian Mixture Models) rather than treating all samples within a disease category as uniform. This segmentation allows identification of alternative splicing events that are specific to particular subpopulations (e.g., cancer-specific subpopulations) while maintaining analytical tractability through automated clustering algorithms.
Solution Approach 2:
The patent changes the analytical parameter from assuming uniform distribution to modeling heterogeneous distributions using probabilistic models. By fitting multiple Gaussian components to represent different subpopulations, the method captures biological heterogeneity in alternative splicing patterns without requiring manual intervention to define subpopulations.
2Measurement precision
If probabilistic models are used to identify subpopulations, then identification accuracy of alternative splicing events improves, but computational complexity increases
Solution Approach 1:
The probabilistic clustering model is self-service in that it automatically identifies subpopulations and alternative splicing events without requiring manual specification of cluster numbers or parameters. The Gaussian Mixture Model self-adjusts to fit the data distribution and automatically determines the optimal number of subpopulations based on the statistical properties of the PSI values across samples.
Solution Approach 2:
The patent transforms a complex biological problem into a standardized statistical modeling problem by changing parameters to fit known probability distributions. This allows use of well-established computational algorithms for fitting Gaussian Mixture Models, which are more computationally efficient than de novo clustering methods, thereby reducing computational complexity while maintaining accuracy.
3Quantity of substance
If analysis focuses on overall population distributions, then sample size requirements are reduced, but detection of subpopulation-specific events deteriorates
Solution Approach 1:
By segmenting the overall population into subpopulations through probabilistic clustering, the method increases statistical power for detecting alternative splicing events specific to smaller subgroups. Each cluster represents a biologically coherent subpopulation with shared splicing patterns, allowing detection of events that would be diluted or missed in aggregate population analysis.
Solution Approach 2:
The probabilistic model acts as a counterweight to the diluting effect of population heterogeneity. By explicitly modeling and separating subpopulations, the method counteracts the loss of signal that occurs when mixing samples with different biological properties, thereby maintaining detection sensitivity even with moderate sample sizes.
Data Source
AI summary
Methods and apparatus for identifying alternative splicing events. The method comprises 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 the at least one storage device, information associated with the identified alternative splicing events.