In PLN-team/PLNmodels: Poisson Lognormal Models

knitr::opts_chunk$set(
  screenshot.force = FALSE, 
  echo = TRUE,
  rows.print = 5,
  fig.width = 10, 
  fig.height = 10,
  message = FALSE, 
  warning = FALSE)

Preliminaries

The packages required for the analysis are PLNmodels plus some others for data manipulation and representation:

library(PLNmodels)
library(tidyverse)
library(ggplot2)
nb_cores <- 4

Here are a collection of packages implementing various JS widgets:

library(DT)
library(scatterD3)
library(plotly)
library(edgebundleR)
library(threejs)
library(networkD3)

The oaks amplicon data set at play gives the abundance of 114 taxa (66 bacterial OTU, 48 fungal OTUs) in 116 samples. For each sample, 11 additional covariates are known

data(oaks)
datatable(oaks$Abundance[, 1:5] )

PLNmodels Analyses

PCA analysis

my_PCAs <- PLNPCA(Abundance ~ 1 + offset(log(Offset)), data = oaks, ranks = 25:30, control_main = list(cores = nb_cores))

Sparse precision matrix (Network)

my_networks <- PLNnetwork(Abundance ~ 0 + tree + offset(log(Offset)), data = oaks, control_main = list(trace = 2))

Outputs and Vizualisation

Principal Components Map

We explore various scatterplot solutions to represent the individual factor map of the PLN PCA.

coord <- my_PCAs$getBestModel()$scores[, 1:3] %>% 
  as.data.frame() %>%   
  setNames(c("PC1", "PC2", "PC3")) %>% 
  add_column(tree = oaks$tree, names = rownames(oaks$Abundance))

Direct "plotlyfication"

my_PCA <- my_PCAs$getBestModel()
t(tcrossprod(my_PCA$model_par$B, my_PCA$var_par$M)) %>%
  prcomp(center = FALSE, scale. = FALSE) %>%
  factoextra::fviz_pca_biplot(select.var = list(contrib = 10), col.ind  = oaks$tree,
                              title = "Biplot after correction (10 most contributing species, samples colored by distance to ground)") +
  labs(col = "distance (cm)") + scale_color_viridis_d()

ggplotly()

ScatterD3 (Another fancy scatterplot)

scatterD3(data = coord, x = PC1, y = PC2, lab = names,
          col_var = tree, symbol_var = tree,
          xlab = "PC1", ylab = "PC2", col_lab = "tree",
          symbol_lab = "tree", lasso = TRUE)

Native plotly (3D scatterplot)

fig <- plot_ly(
  coord, x = ~PC1, y = ~PC2, z = ~PC3, color = ~tree, size = .35,
  text = ~paste('status:', tree), type = "scatter3d") %>% 
  layout(title = "Individual Factor Map of the Oaks powdery Mildew data set",
         scene = list(xaxis = list(title = 'PC1'),
                      yaxis = list(title = 'PC2'),
                      zaxis = list(title = 'PC3'))
  )
fig

threejs (3D scatterplot)

group <- rainbow(3)[as.numeric(oaks$tree)]
coord %>% select(1:3) %>% as.matrix() %>% 
scatterplot3js(col = group, size = 0.25, pch = ".", grid = FALSE, bg = "black")

Networks

my_net <- my_networks$getBestModel('BIC')

EdgebundleR

g <- my_net$plot_network(output = "igraph", plot = FALSE)
edgebundle(g)

networkD3

# Convert to object suitable for networkD3
d3 <- igraph_to_networkD3(g, group = sapply(strsplit(colnames(oaks$Abundance), "_"), function(x) x[[1]]))

# Create force directed network plot
forceNetwork(Links = d3$links, Nodes = d3$nodes, 
             Source = 'source', Target = 'target', 
             NodeID = 'name', Group = 'group', opacity = 1)

PLN-team/PLNmodels documentation built on Oct. 13, 2024, 4:01 a.m.