Barcode Binning for Single-Cell Aggregate Removal
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Solution Overview
Problem
Accurate detection of cell-associated barcodes in single-cell molecular datasets is challenging, especially when dealing with protein aggregates, which lead to false molecular counts and complicate the analysis.
Innovation Solution
A method is provided to remove aggregates from a dataset by grouping sequence reads into bins based on common barcode sequences, identifying subsets with a high percentage of correction events, and removing those subsets to generate a cleaner dataset for analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If protein aggregates are present in single-cell datasets, then molecular counting becomes complicated and false counts increase, but removing aggregates requires additional processing steps and computational resources
Solution Approach 1:
The patent segments the dataset processing into distinct stages: initial data reception, binning of sequence reads by barcode, identification of correction events within each bin, and selective removal of bins exceeding the correction threshold. This segmentation allows systematic handling of aggregate removal while maintaining processing efficiency
Solution Approach 2:
The patent performs preliminary binning of sequence reads by barcode sequence before identifying correction events. By organizing data into bins in advance and calculating correction event percentages beforehand, the system prepares the dataset for efficient filtering without requiring complex real-time processing during analysis
2Reliability
If aggregate removal processing is performed on all sequence reads, then data quality improves, but processing time and computational resources increase
Solution Approach 1:
The patent applies quality control locally by evaluating correction events within individual bins rather than processing the entire dataset uniformly. Each bin is assessed independently based on its correction event percentage, allowing selective removal only of affected bins while preserving the rest of the data for quick analysis
Solution Approach 2:
The patent changes the parameter threshold for aggregate identification by using a predetermined correction event percentage threshold. Bins are removed only when their correction event percentage exceeds this threshold, enabling flexible control over the balance between data quality and processing efficiency based on the chosen parameter
Data Source
AI summary
Methods and systems for removing aggregates from a dataset can be provided. For example, method for removing aggregates can comprise: receiving a first dataset comprising a plurality of sequence reads; grouping the plurality of sequence reads into bins, wherein each bin comprises sequence reads that share a common barcode sequence; identifying a subset of barcode sequences from the bins as aggregates by tracking correction events of sequence reads; removing the subset of barcode sequences from the first dataset to obtain a second dataset of sequence reads.