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## ----setup, include=FALSE------------------------------------------------
library(knitr)
library(RevEcoR)
opts_chunk$set(fig.width=8, fig.height=5)
set.seed(60823316)
## ----eval=FALSE----------------------------------------------------------
# install.packages("RevEcoR")
## ----eval=FALSE----------------------------------------------------------
# if (!require(devtools)
# install.packages("devtools")
# devtools::install_github("yiluheihei/RevEcoR")
## ----eval=TRUE-----------------------------------------------------------
library(RevEcoR)
## ----eval=FALSE----------------------------------------------------------
# ## download sample metabolic data from remote KEGG database
# buc <- getOrgMetabolicData("buc")
# data(kegg_buc)
# head(buc)
## ----eval=TRUE, htmlcap="Figure 1 Reconstruction metabolic network of *Buchnera aphidicola APS*", fig.lp="Figure 1", fig.width=8, fig.height=8----
## species in KEGG
buc.net <- reconstructGsMN(kegg_buc, RefData = NULL)
igraph::print.igraph(buc.net)
igraph::plot.igraph(buc.net, vertex.label=NA, vertex.size=5, edge.arrow.size=0.1)
## ko annotation profile species detected in a human microbiome in IMG (not in KEGG)
annodir <- system.file("extdata/koanno.tab",package = "RevEcoR")
metabolic.data <- read.delim(annodir,stringsAsFactors=FALSE)
##load the reference metabolic data
data(RefDbcache)
g2 <- reconstructGsMN(metabolic.data, RefData = RefDbcache)
## ----eval=TRUE, htmlcap="Figure 2The node colored with red represents the species' seed set",fig.lp="Figure 2", fig.width=8, fig.height=8----
## seed set prediction
seed.set <- getSeedSets(buc.net, 0.2)
show(seed.set)
head(seed.set@seeds)
## The node colored with red represents the species' seed set
nodes <- igraph::V(buc.net)$name
seeds <- unlist(seed.set@seeds)
seed.index <- match(seeds,nodes)
node.color <- rep("SkyBlue2",length(nodes))
node.color[seed.index] <- "red"
igraph::plot.igraph(buc.net,
vertex.label=NA, vertex.size=5, edge.arrow.size=0.1,
vertex.color = node.color)
## ------------------------------------------------------------------------
# ptr metabolic network
data(kegg_ptr)
##ptr.net <- reconstructGsMN(getOrgMetabolicData("ptr"))
ptr.net <- reconstructGsMN(kegg_ptr)
# cooperation analysis between buc and ptr
cooperation.index <- calculateCooperationIndex(buc.net,ptr.net)
cooperation.index
## ---- eval = FALSE, echo=TRUE--------------------------------------------
# ##metabolic network reconstruction of these seven species
# net <- lapply(anno.species, reconstructGsMN)
## ---- eval=FALSE, echo=TRUE----------------------------------------------
# ## caculate interactions among vious species
# interactions <- calculateCooperationIndex(net, p = TRUE)
# ## competition index
# $competition.index
# Aa Ao Fn Pg Sg So Va
# Aa 1.0000000 0.4736842 0.3157895 0.2280702 0.4210526 0.4385965 0.2456140
# Ao 0.4736842 1.0000000 0.3684211 0.3333333 0.4736842 0.4736842 0.2456140
# Fn 0.5000000 0.5833333 1.0000000 0.4166667 0.5833333 0.5555556 0.4166667
# Pg 0.4193548 0.6129032 0.4838710 1.0000000 0.6129032 0.5161290 0.3870968
# Sg 0.5454545 0.6136364 0.4772727 0.4318182 1.0000000 0.9090909 0.3863636
# So 0.5813953 0.6046512 0.4651163 0.3720930 0.9302326 1.0000000 0.3953488
# Va 0.4827586 0.4827586 0.5172414 0.4137931 0.5862069 0.5862069 1.0000000
# ## p value of competition index
# $competition.index.p
# Aa Ao Fn Pg Sg So Va
# Aa 0.000 0.001 0.001 0.001 0.001 0.001 0.001
# Ao 0.001 0.000 0.001 0.001 0.001 0.001 0.001
# Fn 0.001 0.001 0.000 0.001 0.001 0.001 0.001
# Pg 0.001 0.001 0.001 0.000 0.001 0.001 0.001
# Sg 0.001 0.001 0.001 0.001 0.000 0.001 0.001
# So 0.001 0.001 0.001 0.001 0.001 0.000 0.001
# Va 0.001 0.001 0.001 0.001 0.001 0.001 0.000
# ## complementarity index
# $complementarity.index
# Aa Ao Fn Pg Sg So Va
# Aa 0.0000000 0.1052632 0.1228070 0.07017544 0.0877193 0.08771930 0.1228070
# Ao 0.1403509 0.0000000 0.1403509 0.07017544 0.1228070 0.12280702 0.1403509
# Fn 0.1944444 0.1666667 0.0000000 0.16666667 0.1111111 0.11111111 0.1388889
# Pg 0.2258065 0.2258065 0.1612903 0.00000000 0.1612903 0.19354839 0.2258065
# Sg 0.2272727 0.1818182 0.1590909 0.09090909 0.0000000 0.04545455 0.1590909
# So 0.1860465 0.1395349 0.1860465 0.09302326 0.0000000 0.00000000 0.1395349
# Va 0.2068966 0.1724138 0.1379310 0.17241379 0.1379310 0.13793103 0.0000000
# ## p value of complementarity index
# $complementarity.index.p
# Aa Ao Fn Pg Sg So Va
# Aa 0.000 0.001 0.001 0.001 0.001 0.001 0.001
# Ao 0.001 0.000 0.001 0.001 0.001 0.001 0.001
# Fn 0.001 0.001 0.000 0.001 0.001 0.001 0.001
# Pg 0.001 0.001 0.001 0.000 0.001 0.001 0.001
# Sg 0.001 0.001 0.001 0.001 0.000 0.001 0.001
# So 0.001 0.001 0.001 0.001 0.001 0.000 0.001
# Va 0.001 0.001 0.001 0.001 0.001 0.001 0.000
## ------------------------------------------------------------------------
data(gut_microbiome)
## summary(gut_microbiome)
## ---- eval = FALSE, echo = TRUE------------------------------------------
# gut.nets <- lapply(gut_microbiome,reconstructGsMN)
# seed.sets <- lapply(gut.nets,getSeedSets)
# ## Since calculation is on large scale, species interactions prediction may take several hours
# gut.interactions <- calculateCooperationIndex(gut.nets)
# competition.index <- gut.interactions$competition.index
# complementarity.index <- gut.interactions$complementarity.index
## ---- eval = TRUE, echo = TRUE-------------------------------------------
occurrence.score <- read.delim(system.file("extdata/occurrence.tab",
package = "RevEcoR"),stringsAsFactors = FALSE, quote = "")
## ---- eval=FALSE,echo=TRUE-----------------------------------------------
# competition.index <- (competition.index + t(competition.index))/2
# complementarity.index <- (complementarity.index + t(complementarity.index))/2
## ---- eval=FALSE,echo=TRUE-----------------------------------------------
# ## upper triangles, which is used to calculate the correlation
# competition.upper <- competition.index[upper.tri(competition.index)]
# occurrence.upper <- occurrence.score[upper.tri(occurrence.score)]
# complementarity.upper <- complementarity.index[upper.tri(complementarity.index)]
#
# ## calculate the spearman correlation betwwen co-occurrence scores and two
# ## interactions indices
# competition.cor <- cor(competition.upper,occurrence.upper,method="spearman")
# complementarity.cor <- cor(complementarity.upper,occurrence.upper,method="spearman")
#
# ## permutation-based mantel test. Random permutation the co-occurance score
# ## 10000 times, P value is the fraction of correlations as high as or higher
# ## than the original
# if (require(magrittr)){
# null.stat <- replicate(10000,
# sample(1:116) %>% occurrence.score[.,.] %>%
# .[upper.tri(.)]
# )
# competition.null <- cor(competition.upper,null.stat)
# complementarity.null <- cor(complementarity.upper,null.stat)
# length(which(competition.null >= competition.cor)) ## 0 p.competition < 0.00001
# length(which(complementarity.null <= complementarity.cor)) ## 0 p.complementarity< 0.00001
# }
## ---- eval=TRUE----------------------------------------------------------
sessionInfo()
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