network | R Documentation |
Microbial related network
network(
otu = NULL,
tax = NULL,
map = NULL,
ps = NULL,
N = 0,
big = FALSE,
select_layout = FALSE,
layout_net = "model_maptree",
r.threshold = 0.6,
p.threshold = 0.05,
method = "spearman",
label = FALSE,
lab = "elements",
group = "Group",
path = "./",
fill = "Phylum",
size = "igraph.degree",
scale = TRUE,
zipi = FALSE,
clu_method = "cluster_fast_greedy",
step = 100,
yourmem = theme_void(),
ncol = 3,
nrow = 1,
R = 10,
ncpus = 1,
a = 1.5
)
ps |
phyloseq Object, contains OTU tables, tax table and map table, represented sequences,phylogenetic tree. |
N |
filter OTU tables by abundance.The defult, N=0, extract the top N number relative abundance of OTU. |
big |
TRUE or FALSE the number of micro data was so many (> 300),you can chose TREU |
select_layout |
TURE or FALSE |
layout_net |
select layout from ggClusterNet |
r.threshold |
The defult, r.threshold=0.6, it represents the correlation that the absolute value of the correlation threshold is greater than 0.6. the value range of correlation threshold from 0 to 1. |
p.threshold |
The defult, p.threshold=0.05, it represents significance threshold below 0.05. |
method |
method for Correlation calculation,method="pearson" is the default value. The alternatives to be passed to cor are "spearman" and "kendall". |
label |
Whether to add node label. |
group |
Separate Group. |
path |
save path of all of network analyse. |
fill |
fill coulor of node |
size |
node size |
zipi |
zipi Calculation |
step |
Random network sampling times |
R |
repeat number of p value calculate |
ncpus |
number of cpus used for sparcc |
lay |
layout which network show |
list which contains OTU correlation matrix
Contact: Tao Wen taowen@njau.edu.cn Penghao Xie 2019103106@njau.edu.cn yongxin liu yxliu@genetics.ac.cn Jun Yuan junyuan@njau.edu.cn
Tao Wen#, Penghao Xie#, Shengdie Yang, Guoqing Niu, Xiaoyu Liu, Zhexu Ding, Chao Xue, Yong-Xin Liu *, Qirong Shen, Jun Yuan* ggClusterNet: an R package for microbiome network analysis and modularity-based multiple network layouts iMeta 2022,DOI: doi: 10.1002/imt2.32
data(ps)
result = network (ps = ps,N = 100,r.threshold=0.6,p.threshold=0.05,label = FALSE,path = path ,zipi = TRUE)
result[[1]]
result[[2]]
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