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Simulating multiple faceted variability in single cell RNA sequencing

Author

Listed:
  • Xiuwei Zhang

    (Center for Computational Biology, UC Berkeley
    Ragon Institute of Massachusetts General Hospital, MIT and Harvard)

  • Chenling Xu

    (Center for Computational Biology, UC Berkeley)

  • Nir Yosef

    (Center for Computational Biology, UC Berkeley
    Ragon Institute of Massachusetts General Hospital, MIT and Harvard
    Chan-Zuckerberg Biohub)

Abstract

The abundance of new computational methods for processing and interpreting transcriptomes at a single cell level raises the need for in silico platforms for evaluation and validation. Here, we present SymSim, a simulator that explicitly models the processes that give rise to data observed in single cell RNA-Seq experiments. The components of the SymSim pipeline pertain to the three primary sources of variation in single cell RNA-Seq data: noise intrinsic to the process of transcription, extrinsic variation indicative of different cell states (both discrete and continuous), and technical variation due to low sensitivity and measurement noise and bias. We demonstrate how SymSim can be used for benchmarking methods for clustering, differential expression and trajectory inference, and for examining the effects of various parameters on their performance. We also show how SymSim can be used to evaluate the number of cells required to detect a rare population under various scenarios.

Suggested Citation

  • Xiuwei Zhang & Chenling Xu & Nir Yosef, 2019. "Simulating multiple faceted variability in single cell RNA sequencing," Nature Communications, Nature, vol. 10(1), pages 1-16, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-10500-w
    DOI: 10.1038/s41467-019-10500-w
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    Cited by:

    1. Angeles Arzalluz-Luque & Pedro Salguero & Sonia Tarazona & Ana Conesa, 2022. "acorde unravels functionally interpretable networks of isoform co-usage from single cell data," Nature Communications, Nature, vol. 13(1), pages 1-18, December.
    2. Jolene S. Ranek & Wayne Stallaert & J. Justin Milner & Margaret Redick & Samuel C. Wolff & Adriana S. Beltran & Natalie Stanley & Jeremy E. Purvis, 2024. "DELVE: feature selection for preserving biological trajectories in single-cell data," Nature Communications, Nature, vol. 15(1), pages 1-26, December.
    3. Ziqi Zhang & Xinye Zhao & Mehak Bindra & Peng Qiu & Xiuwei Zhang, 2024. "scDisInFact: disentangled learning for integration and prediction of multi-batch multi-condition single-cell RNA-sequencing data," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    4. Xiang Lin & Tian Tian & Zhi Wei & Hakon Hakonarson, 2022. "Clustering of single-cell multi-omics data with a multimodal deep learning method," Nature Communications, Nature, vol. 13(1), pages 1-18, December.
    5. Lingfei Wang, 2021. "Single-cell normalization and association testing unifying CRISPR screen and gene co-expression analyses with Normalisr," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
    6. Ziqi Zhang & Haoran Sun & Ragunathan Mariappan & Xi Chen & Xinyu Chen & Mika S. Jain & Mirjana Efremova & Sarah A. Teichmann & Vaibhav Rajan & Xiuwei Zhang, 2023. "scMoMaT jointly performs single cell mosaic integration and multi-modal bio-marker detection," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    7. Yue Cao & Pengyi Yang & Jean Yee Hwa Yang, 2021. "A benchmark study of simulation methods for single-cell RNA sequencing data," Nature Communications, Nature, vol. 12(1), pages 1-12, December.

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