IGRA status and microbiome composition

Adonis test

There are seemingly innumerable ways to query for differences in microbial community composition. Given the zero-inflated type of microbiome data, non-parametric analyses are most appropriate to ask if a statistical difference exists between two groups. In the case of IGRA status, we concluded that LTBI (being IGRA+) has no detectable effect on intestinal microbiome composition. What we formally did was a non-parametric multivariate ANOVA on the microbiome features between IGRA- and IGRA+ people.

The following analysis compares 46 IGRA- and 55 IGRA+ people (data in the phy_NoTB_LTBI Phyloseq object).

# Calculate bray curtis distance matrix
bray <- phyloseq::distance(phy_NoTB_LTBI, method = "bray")

# make a data frame from the sample_data
sampledf <- data.frame(sample_data(phy_NoTB_LTBI))

# Adonis test
library(vegan)
adonis(bray ~ IGRA + age + sex, data = sampledf) 

	Df	SumsOfSqs	MeanSqs	F.Model	R2	Pr(>F)
IGRA	1	0.353	0.35288	1.0630	0.01054	0.319
age	1	0.532	0.53233	1.6036	0.01590	0.044 *
sex	1	0.386	0.38622	1.1635	0.01154	0.225
Residuals	97	32.201	0.33196	-	0.96201
Total	100	33.472	-	-	1.00000

Signif. codes: 0 ‘’ 0.001 ‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1

We can see that if anything, age is the major factor that contributes to variation between IGRA- and IGRA+ individuals. In fact, age has been described as a major contributer to microbiome variation (for a variety of reasons), thus, we made sure to control for age in our study.

beta <- betadisper(treat_bray, sampledf$IGRA)
permutest(beta)

	Df	Sum Sq	Mean Sq	F	N.Perm	Pr(>F)
Groups	1	0.02456	0.0245641	3.8261	999	0.05 *
Residuals	99	0.63560	0.0064202