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Table 2 Differential expression methods tested in this paper. The “Data sets” column lists the data sets that each method is tested on in this work. The top block contains the methods that are presented in this work; implementations of these methods can be found in the repository linked in the data availability disclosure. The second block of methods consists of general statistical tests. The third block consists of methods that were designed specifically for scRNA-seq data. The fourth block consists of standard machine learning methods; Log. Reg. stands for logistic regression. We also consider selecting markers randomly without replacement. See the marker selection methods description in the Methods for more information

From: A rank-based marker selection method for high throughput scRNA-seq data

Method

Data sets

Package

Version

Ref

RankCorr

All

custom

  

Spa

Zeisel, Paul

implementation

 

[14]

t-test

All

scanpy

1.3.7; see text

 

Wilcoxon

All

 

1.3.7; see text

 

edgeR

Zeisel, Paul, ZhengFilt

edgeR, rpy2 v2.9.4

3.24.1

[27]

MAST

Zeisel, Paul, ZhengFilt

MAST, rpy2 v2.9.4

1.8.1

[28]

scVI

Zeisel, Paul

Source from GitHub

0.2.4

[29]

Elastic Nets

Zeisel, Paul

scikit-learn [30]

0.20.0

[12]

Log. Reg.

All

scanpy

1.3.7; see text

[31]

Random selection

Zeisel, Paul, ZhengFilt,

   
 

ZhengFull