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

被引:11
作者
Gong, Wuming [1 ]
Kim, Hyunwoo J. [2 ]
Garry, Daniel J. [1 ]
Kwak, Il-Youp [3 ]
机构
[1] Univ Minnesota, Lillehei Heart Inst, Minneapolis, MN USA
[2] Korea Univ, Dept Comp Sci & Engn, Seoul, South Korea
[3] Chung Ang Univ, Dept Appl Stat, Seoul, South Korea
基金
新加坡国家研究基金会;
关键词
Cell lineage tracing; Lineage reconstruction; Machine learning; Simulation;
D O I
10.1186/s12859-022-04633-x
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
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.
引用
收藏
页数:14
相关论文
共 16 条
[1]   Whole-organism clone tracing using single-cell sequencing [J].
Alemany, Anna ;
Florescu, Maria ;
Baron, Chloe S. ;
Peterson-Maduro, Josi ;
van Oudenaarden, Alexander .
NATURE, 2018, 556 (7699) :108-+
[2]   Fast and accurate phylogeny reconstruction algorithms based on the minimum-evolution principle [J].
Desper, R ;
Gascuel, O .
JOURNAL OF COMPUTATIONAL BIOLOGY, 2002, 9 (05) :687-705
[3]  
Dobson A. J., 1975, LECT NOTES MATH, V452, P95, DOI DOI 10.1007/BFB0069548
[4]   Synthetic recording and in situ readout of lineage information in single cells [J].
Frieda, Kirsten L. ;
Linton, James M. ;
Hormoz, Sahand ;
Choi, Joonhyuk ;
Chow, Ke-Huan K. ;
Singer, Zakary S. ;
Budde, Mark W. ;
Elowitz, Michael B. ;
Cai, Long .
NATURE, 2017, 541 (7635) :107-+
[5]   Benchmarked approaches for reconstruction of in vitro cell lineages and in silico models of C. elegans and M. musculus developmental trees [J].
Gong, Wuming ;
Granados, Alejandro A. ;
Hu, Jingyuan ;
Jones, Matthew G. ;
Raz, Ofir ;
Salvador-Martinez, Irepan ;
Zhang, Hanrui ;
Chow, Ke-Huan K. ;
Kwak, Il-Youp ;
Retkute, Renata ;
Prusokiene, Alisa ;
Prusokas, Augustinas ;
Khodaverdian, Alex ;
Zhang, Richard ;
Rao, Suhas ;
Wang, Robert ;
Rennert, Phil ;
Saipradeep, Vangala G. ;
Sivadasan, Naveen ;
Rao, Aditya ;
Joseph, Thomas ;
Srinivasan, Rajgopal ;
Peng, Jiajie ;
Han, Lu ;
Shang, Xuequn ;
Garry, Daniel J. ;
Yu, Thomas ;
Chung, Verena ;
Mason, Michael ;
Liu, Zhandong ;
Guan, Yuanfang ;
Yosef, Nir ;
Shendure, Jay ;
Telford, Maximilian J. ;
Shapiro, Ehud ;
Elowitz, Michael B. ;
Meyer, Pablo .
CELL SYSTEMS, 2021, 12 (08) :810-+
[6]  
Grabocka J, 2019, ABS190510108 CORR
[7]   Inference of single-cell phylogenies from lineage tracing data using Cassiopeia [J].
Jones, Matthew G. ;
Khodaverdian, Alex ;
Quinn, Jeffrey J. ;
Chan, Michelle M. ;
Hussmann, Jeffrey A. ;
Wang, Robert ;
Xu, Chenling ;
Weissman, Jonathan S. ;
Yosef, Nir .
GENOME BIOLOGY, 2020, 21 (01)
[8]   Whole-organism lineage tracing by combinatorial and cumulative genome editing [J].
McKenna, Aaron ;
Findlay, Gregory M. ;
Gagnon, James A. ;
Horwitz, Marshall S. ;
Schier, Alexander F. ;
Shendure, Jay .
SCIENCE, 2016, 353 (6298)
[9]  
Paradis E, 2004, BIOINFORMATICS, V20, P289, DOI [10.1093/bioinformatics/btg412, 10.1093/bioinformatics/bty633]
[10]  
R Development Core Team, 2017, R: A Language and Environment for Statistical Computing