Single-Cell Transcriptome Profiling Simulation Reveals the Impact of Sequencing Parameters and Algorithms on Clustering

Liu, Yunhe; Wu, Aoshen; Peng, Xueqing; Liu, Xiaona; Liu, Gang; Liu, Lei

doi:10.3390/life11070716

Open AccessArticle

Single-Cell Transcriptome Profiling Simulation Reveals the Impact of Sequencing Parameters and Algorithms on Clustering

by

Yunhe Liu

¹

,

Aoshen Wu

¹,

Xueqing Peng

¹,

Xiaona Liu

¹,

Gang Liu

^1,* and

Lei Liu

^1,2,*

¹

Institute of Biomedical Sciences, Fudan University, Shanghai 200000, China

²

School of Basic Medical Science, Fudan University, Shanghai 200000, China

^*

Authors to whom correspondence should be addressed.

Life 2021, 11(7), 716; https://doi.org/10.3390/life11070716

Submission received: 8 June 2021 / Revised: 9 July 2021 / Accepted: 15 July 2021 / Published: 19 July 2021

(This article belongs to the Special Issue Bioinformatics Methods for Single Cell Sequencing Data Analysis)

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Abstract

Despite the scRNA-seq analytic algorithms developed, their performance for cell clustering cannot be quantified due to the unknown “true” clusters. Referencing the transcriptomic heterogeneity of cell clusters, a “true” mRNA number matrix of cell individuals was defined as ground truth. Based on the matrix and the actual data generation procedure, a simulation program (SSCRNA) for raw data was developed. Subsequently, the consistency between simulated data and real data was evaluated. Furthermore, the impact of sequencing depth and algorithms for analyses on cluster accuracy was quantified. As a result, the simulation result was highly consistent with that of the actual data. Among the clustering algorithms, the Gaussian normalization method was the more recommended. As for the clustering algorithms, the K-means clustering method was more stable than K-means plus Louvain clustering. In conclusion, the scRNA simulation algorithm developed restores the actual data generation process, discovers the impact of parameters on classification, compares the normalization/clustering algorithms, and provides novel insight into scRNA analyses.

Keywords: single cell; bioinformatics; simulation; clustering; cell type annotation

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MDPI and ACS Style

Liu, Y.; Wu, A.; Peng, X.; Liu, X.; Liu, G.; Liu, L. Single-Cell Transcriptome Profiling Simulation Reveals the Impact of Sequencing Parameters and Algorithms on Clustering. Life 2021, 11, 716. https://doi.org/10.3390/life11070716

AMA Style

Liu Y, Wu A, Peng X, Liu X, Liu G, Liu L. Single-Cell Transcriptome Profiling Simulation Reveals the Impact of Sequencing Parameters and Algorithms on Clustering. Life. 2021; 11(7):716. https://doi.org/10.3390/life11070716

Chicago/Turabian Style

Liu, Yunhe, Aoshen Wu, Xueqing Peng, Xiaona Liu, Gang Liu, and Lei Liu. 2021. "Single-Cell Transcriptome Profiling Simulation Reveals the Impact of Sequencing Parameters and Algorithms on Clustering" Life 11, no. 7: 716. https://doi.org/10.3390/life11070716

APA Style

Liu, Y., Wu, A., Peng, X., Liu, X., Liu, G., & Liu, L. (2021). Single-Cell Transcriptome Profiling Simulation Reveals the Impact of Sequencing Parameters and Algorithms on Clustering. Life, 11(7), 716. https://doi.org/10.3390/life11070716

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

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Single-Cell Transcriptome Profiling Simulation Reveals the Impact of Sequencing Parameters and Algorithms on Clustering

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