To explore correlations among the different phyla and genera present in the samples, and their relationships to microbial community structure in PTSD participants and TE controls, compositional biplot analyses were performed. The full set of reads mapped to Greengenes, identifying a total of 7,556 OTUs, was used for this analysis. A multiplicative zero replacement technique was used to determine pseudo counts for zeros (124). A centre log ratio transformation (125) was conducted to transform the data and a singular value decomposition was performed to obtain a 2 rank approximation of the phyla and genera and the samples. Weighted gene co-expression network analysis (WGCNA) Network-based analysis was conducted using weighted gene co-expression network analysis (WGCNA). The full set of reads mapped to Greengenes, identifying a total of 7,556 OTUs, was used for this analysis. Modules were tested for association with variables of interest (age, Childhood Trauma Questionnaire (CTQ) score, time since index trauma (in months), plasma protein (CRP) concentration (mg/L)) using general linear models. All statistical analyses were performed in R via the R Studio platform and used the WGCNA statistical package (126, 127). Initial analyses to check for potential outliers and excessive missingness in our data set were performed using the WGCNA package. In addition, hierarchical average linkage cluster analysis was performed to identify outlying samples. Neither of these resulted in the removal of any participants. We used WGCNA to identify clusters, or modules, of OTUs that are highly co-expressed as well as a hub OTU that is the most highly connected. The nodes were the relative abundance values of OTUs, and we used this analysis to build modules of these nodes. To build the modules we used a for assigning module membership, as suggested and thoroughly reviewed by “soft threshold” Zhang and Horvath (128). A topological overlap matrix (TOM) was built and used to develop
To explore correlations among the different phyla and genera present in the samples, and their relationships to microbial community structure in PTSD participants and TE controls, compositional biplot analyses were performed. The full set of reads mapped to Greengenes, identifying a total of 7,556 OTUs, was used for this analysis. A multiplicative zero replacement technique was used to determine pseudo counts for zeros (124). A centre log ratio transformation (125) was conducted to transform the data and a singular value decomposition was performed to obtain a 2 rank approximation of the phyla and genera and the samples. Weighted gene co-expression network analysis (WGCNA) Network-based analysis was conducted using weighted gene co-expression network analysis (WGCNA). The full set of reads mapped to Greengenes, identifying a total of 7,556 OTUs, was used for this analysis. Modules were tested for association with variables of interest (age, Childhood Trauma Questionnaire (CTQ) score, time since index trauma (in months), plasma protein (CRP) concentration (mg/L)) using general linear models. All statistical analyses were performed in R via the R Studio platform and used the WGCNA statistical package (126, 127). Initial analyses to check for potential outliers and excessive missingness in our data set were performed using the WGCNA package. In addition, hierarchical average linkage cluster analysis was performed to identify outlying samples. Neither of these resulted in the removal of any participants. We used WGCNA to identify clusters, or modules, of OTUs that are highly co-expressed as well as a hub OTU that is the most highly connected. The nodes were the relative abundance values of OTUs, and we used this analysis to build modules of these nodes. To build the modules we used a “soft threshold” for assigning module membership, as suggested and thoroughly reviewed by Zhang and Horvath (128). A topological overlap matrix (TOM) was built and used to develop
PSYMED_00_00_2017_06_30_LOWRY_PSY16-223_SDC1.docx