This Title All WIREs
How to cite this WIREs title:
WIREs Comp Stat

SAREV: A review on statistical analytics of single‐cell RNA sequencing data

Full article on Wiley Online Library:   HTML PDF

Can't access this content? Tell your librarian.

Abstract Due to the development of next‐generation RNA sequencing technologies, there has been tremendous progress in research involving determining the role of genomics, transcriptomics, and epigenomics in complex biological systems. However, scientists have realized that information obtained using earlier technology, frequently called “bulk RNA‐seq” data, provides information averaged across all the cells present in a tissue. Relatively newly developed single‐cell (single‐cell RNA sequencing [scRNA‐seq]) technology allows us to provide transcriptomic information at a single‐cell resolution. Nevertheless, these high‐resolution data have their own complex natures and demand novel statistical data analysis methods to provide effective and highly accurate results on complex biological systems. In this review, we cover many such recently developed statistical methods for researchers wanting to pursue scRNA‐seq statistical and computational research as well as scientific research about these existing methods and free software tools available for their generated data. This review is certainly not exhaustive due to page limitations. We have tried to cover the popular methods starting from quality control to the downstream analysis of finding differentially expressed genes and concluding with a brief description of network analysis. This article is categorized under: Statistical and Graphical Methods of Data Analysis > Analysis of High Dimensional Data Statistical Models > Generalized Linear Models Software for Computational Statistics > Software/Statistical Software
Workflow of single‐cell RNA sequencing (scRNA‐seq) analysis
[ Normal View | Magnified View ]
UpSet plots of differentially expressed (DE) genes detected by six different methods for six cell types. Dots below vertical bars represent intersections of detected DE genes among different methods. Colored horizontal bars represent the total number of DE genes for each method
[ Normal View | Magnified View ]
Receiver operating characteristic (ROC) curves for several differential expression analysis methods using simulated data. Adapted from T. Wang and Nabavi (2018)
[ Normal View | Magnified View ]
t‐SNE plots for the cluster labels for (clockwise from top left) the ground truth, SC3, SIMLR, SOUP, RaceID, and Seurat. For the true cell types, Group 1 is dark blue, Group 2 is orange, Group 3 is green, Group 4 is red, Group 5 is purple, Group 6 is brown, Group 7 is pink, Group 8 is gray, Group 9 is yellow, and Group 10 is teal
[ Normal View | Magnified View ]

Browse by Topic

Software for Computational Statistics > Software/Statistical Software
Statistical Models > Generalized Linear Models
Statistical and Graphical Methods of Data Analysis > Analysis of High Dimensional Data

Access to this WIREs title is by subscription only.

Recommend to Your
Librarian Now!

The latest WIREs articles in your inbox

Sign Up for Article Alerts