Differential Expression Analysis of Single-Cell RNA-Seq Data: Current Statistical Approaches and Outstanding Challenges
With the advent of single-cell RNA-sequencing (scRNA-seq), it is possible to measure the expression dynamics of genes at the single-cell level. Through scRNA-seq, a huge amount of expression data for several thousand(s) of genes over million(s) of cells are generated in a single experiment. Differen...
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| Format: | Article |
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MDPI AG
2022-07-01
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| Online Access: | https://www.mdpi.com/1099-4300/24/7/995 |
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| author | Samarendra Das Anil Rai Shesh N. Rai |
| author_facet | Samarendra Das Anil Rai Shesh N. Rai |
| author_sort | Samarendra Das |
| collection | DOAJ |
| description | With the advent of single-cell RNA-sequencing (scRNA-seq), it is possible to measure the expression dynamics of genes at the single-cell level. Through scRNA-seq, a huge amount of expression data for several thousand(s) of genes over million(s) of cells are generated in a single experiment. Differential expression analysis is the primary downstream analysis of such data to identify gene markers for cell type detection and also provide inputs to other secondary analyses. Many statistical approaches for differential expression analysis have been reported in the literature. Therefore, we critically discuss the underlying statistical principles of the approaches and distinctly divide them into six major classes, i.e., generalized linear, generalized additive, Hurdle, mixture models, two-class parametric, and non-parametric approaches. We also succinctly discuss the limitations that are specific to each class of approaches, and how they are addressed by other subsequent classes of approach. A number of challenges are identified in this study that must be addressed to develop the next class of innovative approaches. Furthermore, we also emphasize the methodological challenges involved in differential expression analysis of scRNA-seq data that researchers must address to draw maximum benefit from this recent single-cell technology. This study will serve as a guide to genome researchers and experimental biologists to objectively select options for their analysis. |
| first_indexed | 2024-03-09T10:19:48Z |
| format | Article |
| id | doaj.art-f918ea51cd4f4bf3910e2688a1b180b1 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-09T10:19:48Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
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| spelling | doaj.art-f918ea51cd4f4bf3910e2688a1b180b12023-12-01T22:07:54ZengMDPI AGEntropy1099-43002022-07-0124799510.3390/e24070995Differential Expression Analysis of Single-Cell RNA-Seq Data: Current Statistical Approaches and Outstanding ChallengesSamarendra Das0Anil Rai1Shesh N. Rai2ICAR-Directorate of Foot and Mouth Disease, Arugul, Bhubaneswar 752050, IndiaICAR-Indian Agricultural Statistics Research Institute, PUSA, New Delhi 110012, IndiaSchool of Interdisciplinary and Graduate Studies, University of Louisville, Louisville, KY 40292, USAWith the advent of single-cell RNA-sequencing (scRNA-seq), it is possible to measure the expression dynamics of genes at the single-cell level. Through scRNA-seq, a huge amount of expression data for several thousand(s) of genes over million(s) of cells are generated in a single experiment. Differential expression analysis is the primary downstream analysis of such data to identify gene markers for cell type detection and also provide inputs to other secondary analyses. Many statistical approaches for differential expression analysis have been reported in the literature. Therefore, we critically discuss the underlying statistical principles of the approaches and distinctly divide them into six major classes, i.e., generalized linear, generalized additive, Hurdle, mixture models, two-class parametric, and non-parametric approaches. We also succinctly discuss the limitations that are specific to each class of approaches, and how they are addressed by other subsequent classes of approach. A number of challenges are identified in this study that must be addressed to develop the next class of innovative approaches. Furthermore, we also emphasize the methodological challenges involved in differential expression analysis of scRNA-seq data that researchers must address to draw maximum benefit from this recent single-cell technology. This study will serve as a guide to genome researchers and experimental biologists to objectively select options for their analysis.https://www.mdpi.com/1099-4300/24/7/995scRNA-seqdifferential expression analysisclassificationstatistical approacheschallenges |
| spellingShingle | Samarendra Das Anil Rai Shesh N. Rai Differential Expression Analysis of Single-Cell RNA-Seq Data: Current Statistical Approaches and Outstanding Challenges Entropy scRNA-seq differential expression analysis classification statistical approaches challenges |
| title | Differential Expression Analysis of Single-Cell RNA-Seq Data: Current Statistical Approaches and Outstanding Challenges |
| title_full | Differential Expression Analysis of Single-Cell RNA-Seq Data: Current Statistical Approaches and Outstanding Challenges |
| title_fullStr | Differential Expression Analysis of Single-Cell RNA-Seq Data: Current Statistical Approaches and Outstanding Challenges |
| title_full_unstemmed | Differential Expression Analysis of Single-Cell RNA-Seq Data: Current Statistical Approaches and Outstanding Challenges |
| title_short | Differential Expression Analysis of Single-Cell RNA-Seq Data: Current Statistical Approaches and Outstanding Challenges |
| title_sort | differential expression analysis of single cell rna seq data current statistical approaches and outstanding challenges |
| topic | scRNA-seq differential expression analysis classification statistical approaches challenges |
| url | https://www.mdpi.com/1099-4300/24/7/995 |
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