Stable Gene Identification via Rank Normalization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Analyzing large-scale transcriptome sequencing data to identify stable genes across different assays is complex due to the need for accurate comparison of mRNA expression levels, and existing methods struggle to distinguish between differentially expressed and stable genes without meeting confidence thresholds.
Innovation Solution
A computer-implemented method and system for stable gene analysis that performs rank normalization and identifies longest increasing subsequences of ranks across datasets to determine stable genes, using a processor to generate rank normalized input datasets and identify stable genes based on these subsequences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to compare mRNA expression levels across datasets, then differential expression analysis can be performed, but it is difficult to distinguish stable genes without meeting confidence thresholds
Solution Approach 1:
The patent transforms the analysis from using absolute expression levels to using rank positions of genes within datasets. By changing the parameter from expression level magnitude to relative ranking, the method becomes scale-invariant and can identify stable genes without requiring confidence thresholds on expression level changes
Solution Approach 2:
The patent introduces rank normalization as an intermediary step between raw expression data and stability analysis. This intermediary transformation converts expression levels into ranks, which then serve as the basis for identifying stable genes through longest increasing subsequence analysis
2Adaptability or versatility
If scaling methods are used to normalize expression levels, then comparisons across datasets become possible, but scale-invariance is lost
Solution Approach 1:
Instead of applying scaling transformations to expression levels, the patent changes the parameter to rank position. Ranks are inherently scale-invariant because they depend only on the relative ordering of genes within each dataset, not on the absolute expression values or scaling factors applied to them
3Measurement precision
If comprehensive analysis of all genes across multiple datasets is performed, then accurate stable gene identification is achieved, but computational complexity increases
Solution Approach 1:
By transforming expression levels to ranks, the patent simplifies the data structure while preserving the information needed for stability analysis. This parameter transformation reduces computational complexity by eliminating the need to handle large expression value ranges and scaling calculations
Solution Approach 2:
The patent extracts the essential information for stability analysis (the relative ordering of genes) while discarding the complex expression level magnitudes. This extraction of rank information from raw expression data simplifies subsequent computational steps
Data Source
AI summary
Various embodiments perform stable gene analysis of transcriptome sequencing data. In one embodiment, a plurality of datasets each including transcriptome sequencing data are received by a processor. Each of the plurality of datasets includes a plurality of genes and a respective ranking value for each of the plurality of genes. A plurality of rank normalized input datasets is generated based on assigning, for each of the plurality of datasets, a rank to each of the plurality of genes. One or more longest increasing subsequence (LIS) of ranks are identified between each pair of the plurality of rank normalized input datasets. A set of stable genes from the plurality of genes is identified based on each of the one or more LIS of ranks across the plurality of rank normalized input datasets.