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Table 3 Type I error control and power on simulated data based on Saccharibacteria’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

0.0247

0.0032

0.0602 +

0.0424 +

0.0326 +

ZIP

0.8238

0.7766

0.8056 ∗

0.7642 ∗

0.7384 ∗

ZINB

0.4241

0.2916

0.3515 ∗

0.3304 ∗

0.3150 ∗

ZINQ-MinP

0.0471

0.0089

0.9243

0.4867

0.0819

ZINQ-Cauchy

0.0506

0.0100

0.9166

0.5428

0.0954

TSS

     

Linear regression

0.0279

0.0030

0.0372 +

0.0338 +

0.0320 +

ZIB

0.0053

0.0009

0.0649 +

0.0190 +

0.0067 +

Tobit

0.0522

0.0137

0.0837

0.0703

0.0635

ZIlogN

0.9997

0.9997

0.9987 ∗

0.9978 ∗

0.9983 ∗

ZIG

0.0495

0.0073

0.1094

0.0675

0.0498

ZINQ-MinP

0.0428

0.0083

0.6626

0.2480

0.0596

ZINQ-Cauchy

0.0497

0.0099

0.6800

0.2818

0.0700

CSS

     

Linear regression

0.0500

0.0107

0.2021

0.1034

0.0541

Tobit

0.0498

0.0111

0.1677

0.0929

0.0533

ZIlogN

0.0446

0.0071

0.4933

0.2063

0.0621

ZIG

0.0443

0.0076

0.5563

0.2264

0.0643

ZINQ-MinP

0.0456

0.0085

0.8442

0.3766

0.0720

ZINQ-Cauchy

0.0497

0.0099

0.8327

0.3897

0.0773

  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 Saccharibacteria’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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