Analyte Imaging Data Preparation With Clustered Pixel Storage
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Solution Overview
Problem
Existing methods for identifying nucleic acid sequences and analytes using multiple coloring rounds generate large data volumes that require significant computing effort and memory, leading to high acquisition and maintenance costs, and limit sample throughput.
Innovation Solution
A method that reduces data volume by eliminating color information of non-candidate pixels and using machine learning models to assess and store only relevant data points, allowing efficient and rapid processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all color information from multiple coloring rounds is stored for evaluating analytes, then measurement precision is improved, but data storage requirements and computing effort increase significantly
Solution Approach 1:
The patent extracts and stores only the relevant color information data points that are necessary for analyte identification, eliminating redundant color information from non-candidate pixels. This selective extraction maintains measurement precision while significantly reducing data storage requirements by storing only essential data points for evaluation.
Solution Approach 2:
The patent applies different quality standards to different regions of the image data by identifying candidate pixels versus non-candidate pixels. Only candidate pixels (those likely to contain analytes) are stored with full color information, while non-candidate pixels are eliminated, creating a locally optimized data storage strategy that preserves precision where needed and reduces storage elsewhere.
2Measurement precision
If large data volumes from multiple coloring rounds are processed, then analyte identification accuracy is improved, but processing time increases
Solution Approach 1:
The patent extracts only the essential color information from candidate pixels for storage and processing, eliminating redundant data from non-candidate pixels. This extraction reduces the total data volume requiring processing while maintaining all necessary information for accurate analyte identification, thereby reducing processing time without sacrificing precision.
Solution Approach 2:
The patent performs preliminary assessment and identification of candidate pixels before full data processing. By pre-identifying which pixels contain relevant analyte information, the system prepares a reduced dataset in advance, eliminating the need to process all pixels equally and significantly reducing subsequent processing time while maintaining identification accuracy.
3Speed
If SSD hard drives are used for storing large data volumes, then data access speed is improved, but write cycle limitations are reached quickly
Solution Approach 1:
The patent extracts and stores only the essential color information from candidate pixels, dramatically reducing the total data storage volume. This reduced data volume decreases the number of write cycles required on SSD hard drives while maintaining fast data access speeds, as the smaller dataset can be stored more efficiently and accessed more quickly.
4Loss of information
If all pixels in images are evaluated and stored, then completeness of data is improved, but device complexity increases
Solution Approach 1:
The patent extracts and identifies candidate pixels that contain relevant analyte information, storing only this essential subset of data. This selective extraction maintains data completeness for analyte identification purposes while significantly reducing system complexity by eliminating the need to process, store, and manage all pixel data uniformly.
Data Source
AI summary
A method for preparing data for identifying analytes by coloring one or more analytes with markers in multiple coloring rounds, the markers in each case being specific for a certain set of analytes, detecting multiple markers using a camera, which for each coloring round generates at least one image that includes multiple pixels and that may contain color information of one or more markers, and storing the images of the particular coloring rounds stored for evaluating the color information,whereinthe color values determined in the individual coloring rounds are clustered, according to their intensity values, in local or global clusters with similar intensity values, and only the clustered data are stored.