The Poisson distribution model fits UMI-based single-cell RNA-sequencing data
Abstract Background Modeling of single cell RNA-sequencing (scRNA-seq) data remains challenging due to a high percentage of zeros and data heterogeneity, so improved modeling has strong potential to benefit many downstream data analyses. The existing zero-inflated or over-dispersed models are based...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2023-06-01
|
| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12859-023-05349-2 |
| _version_ | 1827922790995460096 |
|---|---|
| author | Yue Pan Justin T. Landis Razia Moorad Di Wu J. S. Marron Dirk P. Dittmer |
| author_facet | Yue Pan Justin T. Landis Razia Moorad Di Wu J. S. Marron Dirk P. Dittmer |
| author_sort | Yue Pan |
| collection | DOAJ |
| description | Abstract Background Modeling of single cell RNA-sequencing (scRNA-seq) data remains challenging due to a high percentage of zeros and data heterogeneity, so improved modeling has strong potential to benefit many downstream data analyses. The existing zero-inflated or over-dispersed models are based on aggregations at either the gene or the cell level. However, they typically lose accuracy due to a too crude aggregation at those two levels. Results We avoid the crude approximations entailed by such aggregation through proposing an independent Poisson distribution (IPD) particularly at each individual entry in the scRNA-seq data matrix. This approach naturally and intuitively models the large number of zeros as matrix entries with a very small Poisson parameter. The critical challenge of cell clustering is approached via a novel data representation as Departures from a simple homogeneous IPD (DIPD) to capture the per-gene-per-cell intrinsic heterogeneity generated by cell clusters. Our experiments using real data and crafted experiments show that using DIPD as a data representation for scRNA-seq data can uncover novel cell subtypes that are missed or can only be found by careful parameter tuning using conventional methods. Conclusions This new method has multiple advantages, including (1) no need for prior feature selection or manual optimization of hyperparameters; (2) flexibility to combine with and improve upon other methods, such as Seurat. Another novel contribution is the use of crafted experiments as part of the validation of our newly developed DIPD-based clustering pipeline. This new clustering pipeline is implemented in the R (CRAN) package scpoisson. |
| first_indexed | 2024-03-13T04:47:48Z |
| format | Article |
| id | doaj.art-c1b4528d7e5549bc82488d53ea1255dc |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-03-13T04:47:48Z |
| publishDate | 2023-06-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-c1b4528d7e5549bc82488d53ea1255dc2023-06-18T11:26:22ZengBMCBMC Bioinformatics1471-21052023-06-0124112710.1186/s12859-023-05349-2The Poisson distribution model fits UMI-based single-cell RNA-sequencing dataYue Pan0Justin T. Landis1Razia Moorad2Di Wu3J. S. Marron4Dirk P. Dittmer5Department of Biostatistics, University of North Carolina at Chapel HillLineberger Comprehensive Cancer Center, University of North Carolina at Chapel HillLineberger Comprehensive Cancer Center, University of North Carolina at Chapel HillDepartment of Biostatistics, University of North Carolina at Chapel HillDepartment of Biostatistics, University of North Carolina at Chapel HillLineberger Comprehensive Cancer Center, University of North Carolina at Chapel HillAbstract Background Modeling of single cell RNA-sequencing (scRNA-seq) data remains challenging due to a high percentage of zeros and data heterogeneity, so improved modeling has strong potential to benefit many downstream data analyses. The existing zero-inflated or over-dispersed models are based on aggregations at either the gene or the cell level. However, they typically lose accuracy due to a too crude aggregation at those two levels. Results We avoid the crude approximations entailed by such aggregation through proposing an independent Poisson distribution (IPD) particularly at each individual entry in the scRNA-seq data matrix. This approach naturally and intuitively models the large number of zeros as matrix entries with a very small Poisson parameter. The critical challenge of cell clustering is approached via a novel data representation as Departures from a simple homogeneous IPD (DIPD) to capture the per-gene-per-cell intrinsic heterogeneity generated by cell clusters. Our experiments using real data and crafted experiments show that using DIPD as a data representation for scRNA-seq data can uncover novel cell subtypes that are missed or can only be found by careful parameter tuning using conventional methods. Conclusions This new method has multiple advantages, including (1) no need for prior feature selection or manual optimization of hyperparameters; (2) flexibility to combine with and improve upon other methods, such as Seurat. Another novel contribution is the use of crafted experiments as part of the validation of our newly developed DIPD-based clustering pipeline. This new clustering pipeline is implemented in the R (CRAN) package scpoisson.https://doi.org/10.1186/s12859-023-05349-2Single cellRNA-seqPoisson distributionData representation |
| spellingShingle | Yue Pan Justin T. Landis Razia Moorad Di Wu J. S. Marron Dirk P. Dittmer The Poisson distribution model fits UMI-based single-cell RNA-sequencing data BMC Bioinformatics Single cell RNA-seq Poisson distribution Data representation |
| title | The Poisson distribution model fits UMI-based single-cell RNA-sequencing data |
| title_full | The Poisson distribution model fits UMI-based single-cell RNA-sequencing data |
| title_fullStr | The Poisson distribution model fits UMI-based single-cell RNA-sequencing data |
| title_full_unstemmed | The Poisson distribution model fits UMI-based single-cell RNA-sequencing data |
| title_short | The Poisson distribution model fits UMI-based single-cell RNA-sequencing data |
| title_sort | poisson distribution model fits umi based single cell rna sequencing data |
| topic | Single cell RNA-seq Poisson distribution Data representation |
| url | https://doi.org/10.1186/s12859-023-05349-2 |
| work_keys_str_mv | AT yuepan thepoissondistributionmodelfitsumibasedsinglecellrnasequencingdata AT justintlandis thepoissondistributionmodelfitsumibasedsinglecellrnasequencingdata AT raziamoorad thepoissondistributionmodelfitsumibasedsinglecellrnasequencingdata AT diwu thepoissondistributionmodelfitsumibasedsinglecellrnasequencingdata AT jsmarron thepoissondistributionmodelfitsumibasedsinglecellrnasequencingdata AT dirkpdittmer thepoissondistributionmodelfitsumibasedsinglecellrnasequencingdata AT yuepan poissondistributionmodelfitsumibasedsinglecellrnasequencingdata AT justintlandis poissondistributionmodelfitsumibasedsinglecellrnasequencingdata AT raziamoorad poissondistributionmodelfitsumibasedsinglecellrnasequencingdata AT diwu poissondistributionmodelfitsumibasedsinglecellrnasequencingdata AT jsmarron poissondistributionmodelfitsumibasedsinglecellrnasequencingdata AT dirkpdittmer poissondistributionmodelfitsumibasedsinglecellrnasequencingdata |