2.4 Data analysis
Read counts from all samples were of the same order of magnitude (18,834
to 44,787 for bacterial dataset and 9726 to 62,613 for fungal dataset).
Singletons, and doubletons were first filtered out from both bacterial
and fungal datasets. To decrease the noise, taxa with the sum of
relative abundance less than 0.001 were removed. This resulted in a core
dataset of 693 taxa by 40 samples in bacterial dataset and 364 taxa by
40 samples in fungal dataset. The raw taxa counts were normalized to
abundance using Hellinger transformation.
Statistical analyses were performed using R version 3.6.1 (R Development
Core Team, 2016). Graphs were plotted with R packages “ggplot2”
(Wickham, 2016), “grid” (Murrell, 2005), and “gridExtra” (Auguie,
2017). Two-way analysis of variance (two-way ANOVA) was carried out to
test the effect of plant tissue type or radiation level on the richness
and diversity of bacterial and fungal microbiota with functionaov in “stats” package (R Development Core Team, 2016). Type 1
error rates had a Benjamini-Hochberg (FDR) p value correction
performed for ANOVA models with function p.adjust in “stats”
package (Benjamini & Hochberg, 1995; R Development Core Team, 2016;
Veach et al., 2019).
Significant
differences between the microbial populations were further compared
using Tukey’s honestly significant difference (HSD) test with functionHSD.test in “agricolae” package (Mendiburu, 2019).
The distance matrices of community composition (Hellinger-transformed
OTU read data) of endophytic fungi were constructed by calculating
dissimilarities using
Bray-Curtis
method (Faith, Minchin, & Belbin, 1987). Non-metric multidimensional
scaling (NMDS) was used to visualize the community composition
dissimilarity of endophytic bacteria or fungi among the different plant
tissues or radiation levels usingmetaMDSfunction in “vegan” package (Oksanen et al., 2016). Analysis of
similarities (ANOSIM) was applied to statistically test the significant
differences in microbial composition between plant tissues or among
radiation levels. Permutational multivariate analysis of variance
(PerMANOVA)
with 999 permutations was implemented with adonis in “vegan”
package to investigate the environmental influence on microbiota
composition.
The effect of different environmental factors (explanatory variables) on
endophyte abundance or richness (genus level for bacteria and species
level for fungi) was tested using Poisson generalized linear models
(GLM) with stepwise selection by AIC. This analysis was performed using
function glm in “stats” package and function stepAIC in
“MASS” package (R Development Core Team, 2016; Veach et al., 2019;
Venables & Ripley, 2002). The data distribution was tested with
function shapiro.test in “stats” package. All data were
calculated with Poisson distribution and overdispersion in data was
tested with function qcc.overdispersion.test in “qcc” package
(Scrucca, 2004). Type 1 error rates were FDR-corrected with the method
mentioned above.
Co-occurrence analysis was applied on bacterial and fungal datasets
separately and collectively with function cor.test in “stats”
package (R Development Core Team, 2016). The co-occurrence networks were
visualized with “igraph” package (Csardi & Nepusz, 2006). Network
characteristics were determined using functions in “bipartite” package
(Dormann, Fründ, Blüthgen, & Gruber, 2009).
Intra-genus genetic diversity of bacteria and fungi from control and
three treatment levels were evaluated by computing the
pairwise
distances of DNA sequences within the groups. Only the ASVs assigned
taxonomy at the genus level for bacteria and fungi were included in the
analysis. Pairwise distance was calculated among all ASVs available in a
certain genus from one treatment level using “K80” model withdist.dna in “ape” package (Paradis & Schliep, 2018). One-way
ANOVA was applied to test the difference significance of intra-genus
genetic diversity, as well as all sequence distances regardless of
genera, among four treatments.