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Table 1 Performance of rPCA for outlier detection on simulated data with 3 biological replicates in each treatment group using rlog transformation

From: Robust principal component analysis for accurate outlier sample detection in RNA-Seq data

ID of sample being added

outlier model

Error rate

Sample replicate

Outlier detected by PcaHubert

Number FP outlier called by PcaHubert

outlier detected by PcaGrid

Number FP outlier called by PcaGrid

SEN (%) by PcaGrid

SP (%) by PcaGrid

None (baseline)

NA

0.005

 

NA

NA

NA

NA

NA

NA

N-1

NA

0.01

1

NA

0

NA

0

NA

NA

N-2

0.05

1

NA

1

NA

0

NA

NA

N-3

0.1

1

NA

1

NA

0

NA

NA

N-4

0.2

1

NA

1

NA

0

NA

NA

L-1

outlierL

0.01

1

Yes

1

Yes

0

100

100

L-2

0.01

2

Yes

1

Yes

0

100

100

L-3

0.01

3

Yes

1

Yes

0

100

100

L-4

0.05

1

Yes

1

Yes

0

100

100

L-5

0.05

2

Yes

1

Yes

0

100

100

L-6

0.05

3

Yes

1

Yes

0

100

100

L-7

0.1

1

Yes

1

Yes

0

100

100

L-8

0.1

2

Yes

1

Yes

0

100

100

L-9

0.1

3

Yes

1

Yes

0

100

100

L-10

0.2

1

Yes

1

Yes

0

100

100

L-11

0.2

2

Yes

1

Yes

0

100

100

L-12

0.2

3

Yes

1

Yes

0

100

100

H-1

outlierH

0.01

1

Yes

1

Yes

0

100

100

H-2

0.01

2

Yes

1

Yes

0

100

100

H-3

0.01

3

Yes

1

Yes

0

100

100

H-4

0.05

1

Yes

1

Yes

0

100

100

H-5

0.05

2

Yes

1

Yes

0

100

100

H-6

0.05

3

Yes

1

Yes

0

100

100

H-7

0.1

1

Yes

1

Yes

0

100

100

H-8

0.1

2

Yes

1

Yes

0

100

100

H-9

0.1

3

Yes

1

Yes

0

100

100

H-10

0.2

1

Yes

1

Yes

0

100

100

H-11

0.2

2

Yes

1

Yes

0

100

100

H-12

0.2

3

Yes

1

Yes

0

100

100

  1. SEN Sensitivity, SP Specificity, rlog Regularized log transformation, vst Variance Stabilizing Transformation, outlierL Outlier with low “outlierness”, outlierH Outlier with high “outlierness”