Last updated: 2022-04-12

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Knit directory: Cystic-Fibrosis-and-Gut-Microbiome/

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C:/Users/Jie Zhou/Documents/paper02052022/longitudinalOutcome/Cystic-Fibrosis-and-Gut-Microbiome/data/ddata.csv	data/ddata.csv
C:/Users/Jie Zhou/Documents/paper02052022/longitudinalOutcome/Cystic-Fibrosis-and-Gut-Microbiome/data/analyst_pulmonary_exacerbation_202110061618.csv	data/analyst_pulmonary_exacerbation_202110061618.csv

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Input the microbiome data: 38 subjects, 82 microbes. These data have undergone log-ratio transfomation and so can be deemed as normal data. As for the Outcome data also is input in a separate file.

taxa_raw = read.csv("C:/Users/Jie Zhou/Documents/paper02052022/longitudinalOutcome/Cystic-Fibrosis-and-Gut-Microbiome/data/ddata.csv")
# averaging across time points
taxa_avg = taxa_raw %>% 
  group_by(subject) %>% 
  summarize_all(list(mean))

taxa = taxa_avg[,4:ncol(taxa_avg)]
pulm = read.delim("C:/Users/Jie Zhou/Documents/paper02052022/longitudinalOutcome/Cystic-Fibrosis-and-Gut-Microbiome/data/analyst_pulmonary_exacerbation_202110061618.csv")

Selecting the microbial interaction network using glasso. Here the tuning parameter is recommended based on previous study. Given the selected network, leadeigen algorithm is employed to find the communities in the network.

set.seed(2)
# lambda_glasso = seq(0.05, 6, 0.01)
# cv_glasso_taxa = CVglasso(taxa, K = 5, lam = lambda_glasso)
#   lam_pick = cv_glasso_taxa$Tuning[2]
lam_pick=0.75
glasso_taxa = glasso(cov(taxa), rho = lam_pick)
pcov_taxa = glasso_taxa$wi
taxa_nwk = as.matrix(ifelse(pcov_taxa != 0, 1, 0))
# graph theory

taxa_graph = graph.adjacency(taxa_nwk, mode = "undirected")
taxa_louvain = cluster_louvain(taxa_graph)
taxa_leadeigen = cluster_leading_eigen(taxa_graph) # This one produces fewer modules (clusters)

For each of the communities, compute its principal components.

colnames(taxa[which(taxa_leadeigen$membership == 3)])

 [1] "Erysipelatoclostridium" "Bacteroides"            "Parabacteroides"       
 [4] "Tyzzerella"             "Mediterraneibacter"     "Sellimonas"            
 [7] "Flavonifractor"         "Ruminococcus2"          "Ruthenibacterium"      
[10] "Alistipes"              "Clostridium_XVIII"      "Monoglobus"            
[13] "Eggerthella"            "Turicibacter"           "Eisenbergiella"        
[16] "Flintibacter"           "Agathobaculum"          "Anaerotignum"          
[19] "Dysosmobacter"          "Bilophila"              "Clostridium_IV"        
[22] "Faecalicatena"

taxa_le_cluster1 = taxa[which(taxa_leadeigen$membership == 1)]
taxa_le_pca1 = prcomp(taxa_le_cluster1)
taxa_le_eigen1 = as.matrix(taxa_le_cluster1) %*% taxa_le_pca1$rotation
plot(taxa_le_pca1$sdev^2/sum(taxa_le_pca1$sdev^2), type="l", main = "Leading Eigen Cluster 1 PCs", 
     xlab="principle components", ylab="variance explained")