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Table 2 Type I error control and power on simulated data based on Anaerovorax’s normalized abundance

From: Powerful and robust non-parametric association testing for microbiome data via a zero-inflated quantile approach (ZINQ)

Sample size = 600

     
 

Type I error

Power

 

Null

Setting 1

Setting 2

Setting 3

α-level

0.05

0.01

0.05

0.05

0.05

Rarefaction

     

Linear regression

0.0547

0.0084

0.9949

0.6928

0.1247

ZIP

0.7387

0.6622

1.0000 ∗

0.9742 ∗

0.7720 ∗

ZINB

0.2019

0.0771

0.9812 ∗

0.7321 ∗

0.2398 ∗

ZINQ-MinP

0.0526

0.0106

0.9994

0.8557

0.1508

ZINQ-Cauchy

0.0580

0.0110

0.9991

0.8346

0.1493

TSS

     

Linear regression

0.0536

0.0088

0.9970

0.7425

0.1320

ZIB

0.0110

0.0017

0.9964 +

0.6255 +

0.0305 +

Tobit

0.0543

0.0099

0.9989

0.8041

0.1467

ZIlogN

0.6992

0.6872

1.0000 ∗

1.0000 ∗

0.9999 ∗

ZIG

0.0548

0.0102

0.9961

0.7264

0.1196

ZINQ-MinP

0.0501

0.0101

0.9995

0.9096

0.1669

ZINQ-Cauchy

0.0503

0.0103

0.9994

0.8981

0.1555

CSS

     

Linear regression

0.0527

0.0113

0.9995

0.8934

0.1733

Tobit

0.0526

0.0110

0.9985

0.8597

0.1628

ZIlogN

0.0475

0.0095

0.9996

0.8794

0.1464

ZIG

0.0494

0.0096

0.9998

0.8850

0.1474

ZINQ-MinP

0.0501

0.0103

0.9993

0.8852

0.1520

ZINQ-Cauchy

0.0505

0.0095

0.9991

0.8735

0.1524

  1. Setting 1: 100% from HBP edf for HBP samples;
  2. Setting 2: 80% from HBP edf and 20% from non-HBP edf for HBP samples;
  3. Setting 3: 60% from HBP edf and 40% from non-HBP edf for HBP samples.
  4. ∗: power of a method that inflates type I error
  5. +: power of a method that deflates type I error
  6. Results by the various methods on 10000 simulated datasets by generating samples from the edf of Anaerovorax’s normalized abundance, including type I error control and power under different settings with significance cutoffs 0.05 and 0.01
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Microbiome

ISSN: 2049-2618

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