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The UMI within each UMI group with the highest number of reads is reported and other UMIs within the group are filtered out. If two UMIs within the group have the same number of reads, the UMI with the lowest ascii sequence ASCII value is used. 

This method is similar to the default method in the Drop-seq cookbook, which collapses UMI barcodes with a Hamming distance of 1. 

This method may output more UMIs than the default behavior as only UMIs within an edit distance of 1 are summarized, whereas UMIs with a greater distance can be linked in the UMI-tools method. For a comparison of the performance of the two approaches, please see the Adjacency (CellRanger) and Directional (UMI-tools) methods described in Smith et al. 2017. 

References

Smith T, Hegar A, Sudbery I. UMI-tools: modeling sequencing errors in Unique Molecular Identifiers to improve quantification accuracy. Genome Research 2017; 27(3): 491-499. https://doi.org/10.1101/gr.209601.116 

Drop-seq Alignment Cookbook v1.2 Jan 2016, James Nemesh, Steve McCarroll’s lab, Harvard Medical School  http://mccarrolllab.com/wp-content/uploads/2016/03/Drop-seqAlignmentCookbookv1.2Jan2016.pdf

Cell Ranger Algorithms Overview, 10x Genomics https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/algorithms/overview