Cell Label Classification Using Dynamic Molecular Label Thresholds
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Solution Overview
Problem
Existing methods for cell analysis, such as stochastic barcoding, introduce errors that lead to overestimated cell counts due to amplification bias and inaccurate gene expression measurements.
Innovation Solution
A method for identifying signal cell labels by stochastically barcoding cells with unique molecular labels, determining the number of distinct sequences associated with each cell label, generating cumulative sum and second derivative plots, and identifying cell labels as signal or noise based on a threshold, followed by removing noise cell label information from sequencing data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If stochastic barcoding is used for cell analysis, then cell count and gene expression can be measured, but errors are introduced leading to overestimated cell counts
Solution Approach 1:
The patent implements a feedback mechanism by using the observed distribution of molecular label counts across cell labels to dynamically determine a threshold for distinguishing signal from noise. The method calculates the second derivative of the cumulative sum plot and identifies the minimum point as the optimal threshold, creating a self-correcting system that adapts to the actual data distribution rather than relying on fixed predetermined values.
Solution Approach 2:
The patent changes the parameter of threshold determination from fixed to dynamic by introducing a mathematical transformation process. The cumulative sum plot and its second derivative transform the raw molecular label count data into a form where the optimal threshold becomes identifiable as a minimum point, allowing the system to adaptively adjust the threshold parameter based on the actual data characteristics.
2Measurement precision
If amplification is performed during barcoding, then gene expression can be detected, but amplification bias is introduced causing inaccurate measurements
Solution Approach 1:
The patent uses feedback by comparing the distribution of molecular label counts across multiple cell labels to identify the natural separation point between true signals and noise. The second derivative calculation provides feedback about the curvature changes in the cumulative distribution, allowing the system to automatically identify the threshold where amplification bias and noise diverge from genuine biological signals.
Solution Approach 2:
The patent replaces the mechanical amplification process with a computational analysis approach. Instead of relying on the physical amplification process to preserve signal integrity, the method substitutes a mathematical transformation process that analyzes the statistical distribution of molecular labels to distinguish true signals from amplification artifacts and noise.
3Productivity
If all cell labels are counted without filtering, then no signal from noise discrimination is performed, but overestimation of cell counts occurs
Solution Approach 1:
The patent extracts and removes noise cell labels from the data set by applying a threshold-based filtering mechanism. The method identifies cell labels with molecular label counts below the dynamically determined threshold and excludes them from the final cell count, separating the true biological signals from technical noise and amplification artifacts.
Solution Approach 2:
The patent changes the parameter of cell inclusion from binary (all or nothing) to continuous and adaptive by introducing a dynamically determined threshold. This threshold is calculated based on the second derivative minimum of the cumulative sum plot, allowing the system to adaptively adjust the inclusion criterion to maximize both throughput and accuracy.
Data Source
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AI summary
Disclosed herein are methods and systems for classifying cell labels, for example identifying a signal cell label. In some embodiments, the method comprises: obtaining sequencing data of barcoded targets created using targets in cells barcoded using barcodes, wherein a barcode comprises a cell label and a molecular label. After ranking the cell labels, a minimum of a second derivative plot of a cumulative sum plot can be determined. Using the methods, a cell label can be classified as a signal cell label or a noise cell label based on the number of molecular labels with distinct sequences associated with the cell label and a cell label threshold.