Single cell lineage reconstruction using distance-based algorithms and the R package, DCLEAR

Wuming Gong, Hyunwoo J. Kim, Daniel J. Garry, Il Youp Kwak

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

Background: DCLEAR is an R package used for single cell lineage reconstruction. The advances of CRISPR-based gene editing technologies have enabled the prediction of cell lineage trees based on observed edited barcodes from each cell. However, the performance of existing reconstruction methods of cell lineage trees was not accessed until recently. In response to this problem, the Allen Institute hosted the Cell Lineage Reconstruction Dream Challenge in 2020 to crowdsource relevant knowledge from across the world. Our team won sub-challenges 2 and 3 in the challenge competition. Results: The DCLEAR package contained the R codes, which was submitted in response to sub-challenges 2 and 3. Our method consists of two steps: (1) distance matrix estimation and (2) the tree reconstruction from the distance matrix. We proposed two novel methods for distance matrix estimation as outlined in the DCLEAR package. Using our method, we find that two of the more sophisticated distance methods display a substantially improved level of performance compared to the traditional Hamming distance method. DCLEAR is open source and freely available from R CRAN and from under the GNU General Public License, version 3. Conclusions: DCLEAR is a powerful resource for single cell lineage reconstruction.

Original languageEnglish
Article number103
JournalBMC Bioinformatics
Volume23
Issue number1
DOIs
Publication statusPublished - 2022 Dec

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. NRF-2020R1C1C1A01013020).

Publisher Copyright:
© 2022, The Author(s).

Keywords

  • Cell lineage tracing
  • Lineage reconstruction
  • Machine learning
  • Simulation

ASJC Scopus subject areas

  • Structural Biology
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics

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