K-mer counting with low memory consumption enables fast clustering of single-cell sequencing data without read alignment
Shi, C. H.; Yip, K. Y.
Show abstract
K-mer counting has many applications in sequencing data processing and analysis. However, sequencing errors can produce many false k-mers that substantially increase the memory requirement during counting. We propose a fast k-mer counting method, CQF-deNoise, which has a novel component for dynamically identifying and removing false k-mers while preserving counting accuracy. Compared with four state-of-the-art k-mer counting methods, CQF-deNoise consumed 49-76% less memory than the second best method, but still ran competitively fast. The k-mer counts from CQF-deNoise produced cell clusters from single-cell RNA-seq data highly consistent with CellRanger but required only 5% of the running time at the same memory consumption, suggesting that CQF-deNoise can be used for a preview of cell clusters for an early detection of potential data problems, before running a much more time-consuming full analysis pipeline.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.