Cell-Associated Barcode Filtering for Aggregate Detection
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Solution Overview
Problem
Accurate detection of cell-associated barcodes is challenging, especially in single-cell datasets with protein aggregates, leading to false molecular counts due to complications from aggregates.
Innovation Solution
A method and system for detecting aggregates by identifying suspect barcode sequences with high UMI counts, determining aggregate candidates based on detected feature thresholds, and removing aggregates from the dataset.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If aggregate sequences are included in barcode data, then the barcode can capture comprehensive genomic information, but the data quality and accuracy of single-cell sequencing results deteriorate due to sequencing bias and false positives
Solution Approach 1:
The patent segments barcode sequences into two distinct types: cell-associated barcodes (shorter, 8-12 bp) and aggregate-associated barcodes (longer, 14-20 bp). By length-segmenting the barcode data, the system can identify and separate true single-cell barcode signals from aggregate artifacts, thereby maintaining data quality while preserving comprehensive genomic information capture.
Solution Approach 2:
The patent extracts and removes aggregate-associated barcode sequences from the sequencing data through computational filtering. By identifying barcodes with abnormal length characteristics and removing them, the system eliminates the harmful effect of aggregates on data quality while retaining valid single-cell sequencing data.
2Productivity
If standard barcode sequencing methods are used, then the sequencing process is simple and fast, but aggregate sequences cause sequencing bias and reduce reliability
Solution Approach 1:
The patent performs preliminary identification of aggregate-associated barcodes based on their characteristic longer length (14-20 bp) before the main sequencing analysis. This preliminary filtering action is performed on the barcode data itself, allowing rapid identification and removal of aggregate sequences without requiring complex additional sequencing steps, thus maintaining high sequencing speed while improving reliability.
3Difficulty of detecting and measuring
If longer barcode sequences are used to represent aggregates, then aggregate detection becomes possible, but the complexity of data processing increases
Solution Approach 1:
The patent applies local quality differentiation by assigning different length characteristics to different types of barcodes: shorter lengths (8-12 bp) for cell-associated barcodes and longer lengths (14-20 bp) for aggregate-associated barcodes. This local differentiation in barcode length creates an easily distinguishable feature that simplifies aggregate detection and processing complexity.
Data Source
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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.