Reconstructing complex lineage trees from scRNA-seq data using MERLoT

被引:15
|
作者
Parra, R. Gonzalo [1 ,2 ]
Papadopoulos, Nikolaos [1 ]
Ahumada-Arranz, Laura [1 ]
El Kholtei, Jakob [1 ]
Mottelson, Noah [1 ]
Horokhovsky, Yehor [1 ]
Treutlein, Barbara [3 ,4 ]
Soeding, Johannes [1 ]
机构
[1] Max Planck Inst Biophys Chem, Quantitat & Computat Biol Grp, Fassberg 11, D-37077 Gottingen, Germany
[2] European Mol Biol Lab, Genome Biol Unit, Meyerhofstr 1, D-69117 Heidelberg, Germany
[3] Max Planck Inst Evolutionary Anthropol, Dept Evolutionary Genet, Deutsch Pl 6, D-04103 Leipzig, Germany
[4] Swiss Fed Inst Technol, Dept Biosyst Sci & Engn, Zurich, Switzerland
关键词
CELL FATE DECISIONS; COMMITMENT;
D O I
10.1093/nar/gkz706
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Advances in single-cell transcriptomics techniques are revolutionizing studies of cellular differentiation and heterogeneity. It has become possible to track the trajectory of thousands of genes across the cellular lineage trees that represent the temporal emergence of cell types during dynamic processes. However, reconstruction of cellular lineage trees with more than a few cell fates has proved challenging. We present MERLoT (https://github.com/soedinglab/ merlot), a flexible and user-friendly tool to reconstruct complex lineage trees from single-cell transcriptomics data. It can impute temporal gene expression profiles along the reconstructed tree. We show MERLoT's capabilities on various real cases and hundreds of simulated datasets.
引用
收藏
页码:8961 / 8974
页数:14
相关论文
共 50 条
  • [41] CellDepot: A Unified Repository for scRNA-seq Data and Visual Exploration
    Lin, Dongdong
    Chen, Yirui
    Negi, Soumya
    Cheng, Derrick
    Ouyang, Zhengyu
    Sexton, David
    Li, Kejie
    Zhang, Baohong
    JOURNAL OF MOLECULAR BIOLOGY, 2022, 434 (11)
  • [42] Detection of differentially abundant cell subpopulations in scRNA-seq data
    Zhao, Jun
    Jaffe, Ariel
    Li, Henry
    Lindenbaum, Ofir
    Sefik, Esen
    Jackson, Ruaidhri
    Cheng, Xiuyuan
    Flavell, Richard A.
    Kluger, Yuval
    PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2021, 118 (22)
  • [43] Interpretable Factors in scRNA-seq Data with Disentangled Generative Models
    Mao, Haiyi
    Broerman, Matthew J.
    Benos, Panayiotis, V
    2020 IEEE 20TH INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOENGINEERING (BIBE 2020), 2020, : 85 - 88
  • [44] scRNA-seq data analysis method to improve analysis performance
    Lu, Junru
    Sheng, Yuqi
    Qian, Weiheng
    Pan, Min
    Zhao, Xiangwei
    Ge, Qinyu
    IET NANOBIOTECHNOLOGY, 2023, 17 (03) : 246 - 256
  • [45] Identification of Marker Genes in Infectious Diseases from ScRNA-seq Data Using Interpretable Machine Learning
    Martinez, Gustavo Sganzerla
    Garduno, Alexis
    Ostadgavahi, Ali Toloue
    Hewins, Benjamin
    Dutt, Mansi
    Kumar, Anuj
    Martin-Loeches, Ignacio
    Kelvin, David J.
    INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES, 2024, 25 (11)
  • [46] An Experiment on Ab Initio Discovery of Biological Knowledge from scRNA-Seq Data Using Machine Learning
    Shah, Najeebullah
    Li, Jiaqi
    Li, Fanhong
    Chen, Wenchang
    Gao, Haoxiang
    Chen, Sijie
    Hua, Kui
    Zhang, Xuegong
    PATTERNS, 2020, 1 (05):
  • [47] Visualizing hierarchies in scRNA-seq data using a density tree-biased autoencoder
    Garrido, Quentin
    Damrich, Sebastian
    Jager, Alexander
    Cerletti, Dario
    Claassen, Manfred
    Najman, Laurent
    Hamprecht, Fred A.
    BIOINFORMATICS, 2022, 38 (SUPPL 1) : 316 - 324
  • [48] scNCL: transferring labels from scRNA-seq to scATAC-seq data with neighborhood contrastive regularization
    Yan, Xuhua
    Zheng, Ruiqing
    Chen, Jinmiao
    Li, Min
    BIOINFORMATICS, 2023, 39 (08)
  • [49] scAlign: a tool for alignment, integration, and rare cell identification from scRNA-seq data
    Nelson Johansen
    Gerald Quon
    Genome Biology, 20
  • [50] Immunopipe: A comprehensive and flexible scRNA-seq and scTCR-seq data analysis pipeline
    Wang, Panwen
    Dong, Haidong
    Yu, Yue
    Zhang, Shuwen
    Sun, Zhifu
    Kocher, Jean-Pierre A.
    Wang, Junwen
    Yi, Lin
    Li, Ying
    CANCER RESEARCH, 2024, 84 (06)