Preface
1
Introduction
2
How bias affects abundance measurements
2.1
Model of MGS measurement
2.2
Relative abundance
2.3
Absolute abundance
2.3.1
Leveraging information about total-community abundance
2.3.2
Leveraging information about a reference species
3
How bias affects DA results
3.1
Fold change between a pair of samples
3.2
Regression analysis of many samples
3.3
Rank-based analyses
4
Case studies
4.1
Foliar fungi experiment
4.2
Vaginal microbiomes of pregnant women
4.3
Human gut microbiomes
4.4
Microbial growth in marine sediments
4.5
Summary and discussion
5
Potential solutions
5.1
Ratio-based relative DA analysis
5.2
Calibration using community controls
5.3
Choose absolute-abundance methods with more stable FEs
5.3.1
Use complementary MGS and total-abundance measurements
5.3.2
Normalize to a reference species
5.4
Bias sensitivity analysis
5.5
Bias-aware meta-analysis
6
Conclusion
Appendix
A
MGS measurement details
A.1
Alternative method for measuring species absolute abundances from a spike-in
A.2
Missing and multiple reference species
B
Linear regression
B.1
Simple linear regression
B.1.1
Review of simple linear regression
B.1.2
Measurement error in the response
B.1.3
Specific application to taxonomic bias
B.2
Gamma-Poisson regression
B.2.1
Background
B.2.2
Inferring LFCs in proportions with and without bias correction
C
Alpha diversity and variation in the mean efficiency
D
Supplemental figures
References
The effect of taxonomic bias on differential-abundance analysis in microbiome studies
A
MGS measurement details
A.1
Alternative method for measuring species absolute abundances from a spike-in
(stub)
A.2
Missing and multiple reference species
(stub)