[Big data analysis for complex biological systems, 2/3] DCLEAR: Reconstructing Single Cell Lineage Trees from CRISPR recorders by Distance-based Methods

Abstract: A fundamental challenge in biology is the reconstruction of developmental trajectories as they divide and progress through different stages. The recent advance of CRISPR-based molecular tools, such as intMEMOIR and scGESTALT, have produced a new generation of techniques that enable the reconstruction of cell lineages of complex organisms at single-cell resolution. However, there are significant challenges for computationally inferring the lineage trees upon the noisy experimental readout at the single cell level. The recent Allen Institute lineage reconstruction DREAM challenge was the first attempt to rigorously examine the performance and robustness of lineage reconstruction algorithms by using benchmark experimental and in silico data. We proposed two novel methods weighted hamming distance and k-mer replacement distance approaches of estimating the cell distances from the CRISPR/Cas9-enabled recorders and outperform existing methods by several metrics and under a wide variety of parameters regimes. Our methods won subchallenge 2 and 3 of the competition. Our new algorithms should enable the accurate large-scale lineage tracing efforts. The weighted hamming distance and k-mer replacement distance methods were implemented as an R package DCLEAR (https://github.com/ikwak2/DCLEAR).