Exon Array Analysis System Iterative Data Processing
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Solution Overview
Problem
Current systems for analyzing data from biological probe arrays, such as Affymetrix GeneChip arrays, lack a simplified and flexible architecture, making it difficult to efficiently process and identify biological events from the vast amounts of data generated, particularly for specialized applications like exon array analysis.
Innovation Solution
A method and system that iteratively processes user-selected intensity values from multiple data files, determining and storing parameters to identify biological events, utilizing a scanner for pixel intensity acquisition and a computer with applications for data processing and analysis, enabling flexible and efficient analysis of probe arrays on various substrates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional data analysis systems are used for probe arrays, then basic data processing can be performed, but the architecture lacks flexibility and simplicity for specialized applications like exon array analysis
Solution Approach 1:
The system is divided into distinct modular components: a data acquisition module that receives pixel intensity values from scanners, a data processing module that generates data files with probe intensity values, and an analysis module that processes user selections. This segmentation allows each module to be optimized independently, providing flexibility for specialized applications while maintaining manageable complexity through clear separation of concerns.
Solution Approach 2:
The analysis system is designed with universal capabilities to handle multiple types of probe arrays (GeneChip arrays, spotted probe arrays, exon arrays, SNP arrays) through a common architecture. The system can process different data formats and apply various analysis methods, making it adaptable to specialized applications without requiring completely separate systems for each application type.
2Measurement precision
If all intensity values from probe arrays are processed, then complete data analysis is achieved, but processing efficiency decreases due to vast amounts of data
Solution Approach 1:
The system performs preliminary actions by receiving user selections of specific data files and subsets of intensity values before conducting the full analysis. This allows the analysis process to focus only on relevant data portions from the beginning, improving processing efficiency while maintaining complete analysis of the selected subsets. The iterative opening and closing of data files also represents a preliminary action strategy to manage memory resources efficiently.
Solution Approach 2:
The system extracts and processes only the selected subsets of intensity values that are relevant to the user's analysis needs, rather than processing all intensity values from the probe arrays. This extraction approach maintains measurement precision for the events of interest while significantly improving productivity by avoiding unnecessary processing of irrelevant data.
3Adaptability or versatility
If multiple data files are processed simultaneously, then comprehensive analysis is possible, but memory management becomes complex and resource-intensive
Solution Approach 1:
The system employs periodic action by iteratively opening one data file at a time, processing it, and then closing it before moving to the next file. This periodic opening and closing of data files allows comprehensive analysis of multiple data files while maintaining simple memory management, as only one file needs to be held in memory at any given moment rather than loading all files simultaneously.
Data Source
AI summary
In one embodiment, a method for analyzing data generated by probe arrays is described that comprises receiving user selections of two or more data files and an identification of one or more subsets of intensity values acquired from a biological probe array. The method includes iteratively opening each data file, identifying the selected subset of intensity values associated with each open data file, determining parameters for processing, storing the parameters and the identified intensity values, and closing the open data file prior to the subsequent iteration. The method then includes processing the stored intensity values using the parameters to identify one or more biological events.