Single-Cell Transcriptome Profiling Simulation Reveals the Impact of Sequencing Parameters and Algorithms on Clustering
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...
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| Format: | Article |
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MDPI AG
2021-07-01
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| Series: | Life |
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| Online Access: | https://www.mdpi.com/2075-1729/11/7/716 |
| _version_ | 1797526733325336576 |
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| author | Yunhe Liu Aoshen Wu Xueqing Peng Xiaona Liu Gang Liu Lei Liu |
| author_facet | Yunhe Liu Aoshen Wu Xueqing Peng Xiaona Liu Gang Liu Lei Liu |
| author_sort | Yunhe Liu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T09:34:30Z |
| format | Article |
| id | doaj.art-999755b613dd49509e4ea656c1ee6ee4 |
| institution | Directory Open Access Journal |
| issn | 2075-1729 |
| language | English |
| last_indexed | 2024-03-10T09:34:30Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Life |
| spelling | doaj.art-999755b613dd49509e4ea656c1ee6ee42023-11-22T04:13:38ZengMDPI AGLife2075-17292021-07-0111771610.3390/life11070716Single-Cell Transcriptome Profiling Simulation Reveals the Impact of Sequencing Parameters and Algorithms on ClusteringYunhe Liu0Aoshen Wu1Xueqing Peng2Xiaona Liu3Gang Liu4Lei Liu5Institute of Biomedical Sciences, Fudan University, Shanghai 200000, ChinaInstitute of Biomedical Sciences, Fudan University, Shanghai 200000, ChinaInstitute of Biomedical Sciences, Fudan University, Shanghai 200000, ChinaInstitute of Biomedical Sciences, Fudan University, Shanghai 200000, ChinaInstitute of Biomedical Sciences, Fudan University, Shanghai 200000, ChinaInstitute of Biomedical Sciences, Fudan University, Shanghai 200000, ChinaDespite 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.https://www.mdpi.com/2075-1729/11/7/716single cellbioinformaticssimulationclusteringcell type annotation |
| spellingShingle | Yunhe Liu Aoshen Wu Xueqing Peng Xiaona Liu Gang Liu Lei Liu Single-Cell Transcriptome Profiling Simulation Reveals the Impact of Sequencing Parameters and Algorithms on Clustering Life single cell bioinformatics simulation clustering cell type annotation |
| title | Single-Cell Transcriptome Profiling Simulation Reveals the Impact of Sequencing Parameters and Algorithms on Clustering |
| title_full | Single-Cell Transcriptome Profiling Simulation Reveals the Impact of Sequencing Parameters and Algorithms on Clustering |
| title_fullStr | Single-Cell Transcriptome Profiling Simulation Reveals the Impact of Sequencing Parameters and Algorithms on Clustering |
| title_full_unstemmed | Single-Cell Transcriptome Profiling Simulation Reveals the Impact of Sequencing Parameters and Algorithms on Clustering |
| title_short | Single-Cell Transcriptome Profiling Simulation Reveals the Impact of Sequencing Parameters and Algorithms on Clustering |
| title_sort | single cell transcriptome profiling simulation reveals the impact of sequencing parameters and algorithms on clustering |
| topic | single cell bioinformatics simulation clustering cell type annotation |
| url | https://www.mdpi.com/2075-1729/11/7/716 |
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