Transcriptome Profiling via Cluster Centroid Inference
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Solution Overview
Problem
Current transcriptome-analysis technologies are limited by high cost and low throughput, making it impractical to analyze thousands of tissue specimens and cellular states induced by external perturbations for medically relevant connections.
Innovation Solution
A method for efficient and economical generation of full-transcriptome gene-expression profiles by identifying cluster centroid landmark transcripts that predict the expression levels of other transcripts within the same cluster, using a platform that measures sub-transcriptome numbers of transcript measurements and performs computational analysis to create a dependency matrix for inference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If direct measurement of all transcripts is performed, then measurement precision is improved, but cost increases and throughput decreases
Solution Approach 1:
The transcriptome is segmented into multiple clusters based on co-expression patterns, with each cluster represented by a centroid transcript. This segmentation allows measurement of only representative transcripts from each cluster rather than all transcripts, thereby increasing throughput while maintaining measurement precision through the representative nature of centroids.
Solution Approach 2:
The expression levels of non-measured transcripts are inferred by copying the measurement pattern from measured centroid transcripts. The algorithm copies the co-expression relationships observed in training data to predict expression levels of unmeasured transcripts, achieving full transcriptome profiling without direct measurement of all transcripts.
2Measurement precision
If direct measurement of all transcripts is performed, then measurement precision is improved, but cost increases
Solution Approach 1:
The transcriptome is divided into clusters with centroid representatives, reducing the quantity of transcripts that need direct measurement. This segmentation maintains measurement precision by ensuring each cluster's centroid accurately represents the co-expressed group, while reducing the overall cost proportional to the reduction in measured transcripts.
Solution Approach 2:
Instead of directly measuring all transcripts, the system uses copying of expression patterns from measured centroids to infer non-measured transcripts. This copying approach preserves measurement precision through algorithmic inference while reducing cost by eliminating direct measurement of redundant transcripts.
3Productivity
If sub-transcriptome numbers of measurements are performed, then cost decreases and throughput increases, but measurement precision deteriorates
Solution Approach 1:
The transcriptome is segmented into co-expression clusters, and measurements are performed on centroid transcripts that represent each segment. This segmentation strategy maintains measurement precision by ensuring centroids capture the essential expression patterns of their clusters, while reducing the total number of measurements to increase throughput.
Solution Approach 2:
The system uses copying of co-expression relationships from measured centroids to infer expression levels of non-measured transcripts. This copying mechanism preserves measurement precision by leveraging the correlated expression patterns observed in training data, allowing accurate inference from reduced measurements.
Data Source
AI summary
The present invention provides compositions and methods for making and using a transcriptome-wide gene-expression profiling platform that measures the expression levels of only a select subset of the total number of transcripts. Because gene expression is believed to be highly correlated, direct measurement of a small number (for example, 1,000) of appropriately-selected transcripts allows the expression levels of the remainder to be inferred. The present invention, therefore, has the potential to reduce the cost and increase the throughput of full-transcriptome gene-expression profiling relative to the well-known conventional approaches that require all transcripts to be measured.