使用存在/不存在数据的共现网络R

Question

我正在尝试为我的细菌种类存在/不存在数据制作一个共现网络图，但不确定如何处理。我希望以这样的enter image description here结尾，如果每个物种都存在于同一患者中，则每个物种都与另一个物种相关联，对于较高频率的物种，其圆圈较大。我最初尝试使用widyr和tidygraph程序包，但是我不确定我的数据集是否与enter image description here兼容，因为它以患者为列，而以单个物种为行。最好是，我想知道我可以使用哪些程序包/代码来处理我的数据集，或者如何更改数据集以用于这些程序包。

Answer 1

您可以使用矩阵叉积获得同现矩阵。然后，使用igraph包将邻接矩阵转换为图很简单。试试这个：

library(igraph)

# Create fake data set
# rows = patients
# cols = species
set.seed(2222)
df <- matrix(sample(c(TRUE, FALSE), 50, replace = TRUE), 5)
colnames(df) <- letters[1:10]

# Generate co-occurrence matrix with crossproduct
co_mat <- t(df) %*% df

# Set diagonal values to 0
diag(co_mat) <- 0

# Assign dim names
dimnames(co_mat) <- list(colnames(df), colnames(df))

# Create graph from adjacency matrix
# ! edge weights are equal to frequency of co-occurrence
g <- graph_from_adjacency_matrix(co_mat, mode = "upper", weighted = TRUE)

# Assign nodes weight equal to species frequency
g <- set.vertex.attribute(g, "v_weight", value = colSums(df))

plot(g, vertex.size = V(g)$v_weight * 5 + 5, edge.width = E(g)$weight * 5)

这是我们的虚假数据

         a     b     c     d     e     f     g     h     i     j
[1,]  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE
[2,]  TRUE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE FALSE
[3,] FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE
[4,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE
[5,] FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE

这是结果：

Answer 2

像Istrel一样，我也会推荐igraph。也许用ggplot可以解决第二个问题。

library(ggnetwork)
library(ggplot2)
library(igraph)

#sample data:
set.seed(1)
mat <- matrix(rbinom(50 * 5, 1, 0.1), ncol = 15, nrow = 100)

# This is not necessary for the example data. But in your case, if you want  species as nodes you have to do a transpose: 
#mat <- t(mat)

#### Optional! But usually there are often "empty cases" which you might want to remove: 
# remove 0-sum-columns
mat <- mat[,apply(mat, 2, function(x) !all(x==0))] 
# remove 0-sum-rows
mat <- mat[apply(mat, 1, function(x) !all(x==0)),]

# transform in term-term adjacency matrix
mat.t <- mat  %*% t(mat)

##### calculate graph 
g <- igraph::graph.adjacency(mat.t,mode="undirected",weighted=T,diag=FALSE)

# calculate coordinates (see https://igraph.org/r/doc/layout_.html for different layouts)
layout <- as.matrix(layout_with_lgl(g))

p<-ggplot(g, layout = layout, aes(x = x, y = y, xend = xend, yend = yend)) +
  geom_edges( color = "grey20", alpha = 0.2, size = 2) + # add e.g. curvature =  0.15 for curved edges
  geom_nodes(size =  (centralization.degree(g)$res +3) , color="darkolivegreen4", alpha = 1) +
  geom_nodes(size =  centralization.degree(g)$res , color="darkolivegreen2", alpha = 1) +
  geom_nodetext(aes(label = vertex.names), size= 5) +
  theme_blank()
p

enter image description here

使用ggplot美学工具：

# calculate degree:
V(g)$Degree <- centralization.degree(g)$res

p<-ggplot(g, layout = layout, aes(x = x, y = y, xend = xend, yend = yend)) +
  geom_edges( color = "grey20", alpha = 0.2, size = 2) + # add e.g. curvature = 0.15 for curved edges
  geom_nodes(aes(size =  Degree) , color="darkolivegreen2", alpha = 1) +
  scale_size_continuous(range = c(5, 16)) +
  geom_nodetext(aes(label = vertex.names), size= 5) +
  theme_blank()
p