useR! 2024

Cells originate from cell, tracing their lineage from a common ancestor (lineage tracing) is provial for the exploration of development, tumors, and stem cell biology. This is particularly important in answering questions such as stem cell potency, cancer cell plasticity. Within scATACSeq or single-cell multiomics sequencing, mitochondria DNA is enriched due to their histone-free nature with somatic mutations in mitochondria acting as endogeneous marker to following cell lineage in single cell level while profiling open chromatin. We introduce scMitoMut (available in bioconductor), an R package that leverages the statistical model to accurately identify mitochondrial mutations at the single-cell level. scMitoMut is designed to enable users to analysis large datasets on personal computers. In the implementation phase, we have addressed the challenge of handling large scATACSeq datasets on personal computers by creating an HDF5-based object to store raw data and intermediate results. To speed up statistical model fitting for both binomial-mixture and beta-binomial distributions, we have utilized Rcpp for efficient computation and implemented parallel processing techniques.