De novo detection of somatic mutations in high-throughput single-cell profiling data sets
To compare the mutations detected using matched genome sequencing and scRNA-seq data, we computed the base counts for all positions in the genome using the WES–WGS data. For this analysis, we focused only on regions with a coverage of at least 50× in the WES–WGS data from the cancer sample and 10× in the matched normal sample.
As we treated the WES–WGS data as the baseline for comparison, we categorized the mutations as: true positives ; true positives with low support in WES–WGS ; false negatives ; and false positives . To compute performance metrics, we estimated the sensitivity, precision and F1 score values for each algorithm using 50 bootstrap resamples generated by sampling with replacement from the set of mutations used for benchmarking. We then compared the performance between callers using the Student’s-test, correcting for multiple hypothesis testing using the false discovery rate method.
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