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Differential Expression Analysis of Single-Cell RNA-Seq Data: Current Statistical Approaches and Outstanding Challenges

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Title Differential Expression Analysis of Single-Cell RNA-Seq Data: Current Statistical Approaches and Outstanding Challenges
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Creator Samarendra Das
Anil Rai
Shesh N. Rai
 
Subject scRNA-seq; differential expression analysis; classification; statistical approaches; challenges
 
Description Not Available
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.
Not Available
 
Date 2022-12-16T15:23:37Z
2022-12-16T15:23:37Z
2022-07-18
 
Type Research Paper
 
Identifier Das, S.; Rai, A.; Rai, S.N. Differential Expression Analysis of Single-Cell RNA-Seq Data: Current Statistical Approaches and Outstanding Challenges. Entropy 2022, 24, 995. https://doi.org/ 10.3390/e24070995
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http://krishi.icar.gov.in/jspui/handle/123456789/75216
 
Language English
 
Relation Not Available;
 
Publisher MDPI