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Table 2 Power to detect DD genes in simulated data

From: A statistical approach for identifying differential distributions in single-cell RNA-seq experiments

  

True gene category

 

Sample size

Method

DE

DP

DM

DB

Overall (FDR)

50

scDD

0.893

0.418*

0.898*

0.572*

0.695* (0.029)

 

SCDE

0.872

0.026

0.817

0.260

0.494 (0.004)

 

MAST

0.908*

0.400

0.871

0.019

0.550 (0.026)

75

scDD

0.951

0.590

0.960*

0.668*

0.792* (0.031)

 

SCDE

0.948

0.070

0.903

0.387

0.577 (0.003)

 

MAST

0.956*

0.633*

0.943

0.036

0.642 (0.022)

100

scDD

0.972

0.717

0.982*

0.727*

0.850* (0.033)

 

SCDE

0.975

0.125

0.946

0.478

0.631 (0.003)

 

MAST

0.977*

0.752*

0.970

0.045

0.686 (0.022)

500

scDD

1.000*

0.983

1.000*

0.905*

0.972* (0.035)

 

SCDE

1.000*

0.855

0.998

0.787

0.910 (0.004)

 

MAST

1.000*

0.993*

1.000*

0.170

0.791 (0.022)

  1. Average power to detect simulated DD genes by true category. Averages are calculated over 20 replications. Standard errors were <0.025 (not shown)
  2. DB both differential modality and different component means, DD differential distribution, DE differential expression, DM differential modality, DP differential proportion, FDR false discovery rate. Values followed by * designate which method(s) achieved the highest power to detect DD genes from each particular gene category (as well as overall) for each sample sample size setting
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Genome Biology

ISSN: 1474-760X

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