General TOM Measures in Yeast - University of California ...
Applications of GTOM to Fly Protein-Protein Interaction Networks
Andy Yip, Steve Horvath
# STARTING THE R session:
# Open the R software by double clicking the corresponding icon
#To interact with the R software copy and paste the commands into the R console.
#Text after "#" is a comment and is automatically ignored by R.
# Set the working directory of the R session by using the following command.
setwd(“C:/Documents and Settings/shorvath/My Documents/ADAG/AndyYip/TutoriaGTOMscreeningFly”)
# Note that we use / instead of \ in the path.
# read in the R libraries.
library(sna) # this is needed for closeness
#library(MASS)
#library(class)
library(cluster)
#library(sma) # different from sna! this is needed for plot.mat below
library(impute) # needed for imputing missing value before principal component analysis
#Memory
# check the maximum memory that can be allocated
memory.size(TRUE)/1024
# increase the available memory
memory.limit(size=2048)
# read in the custom network functions
source("C:/Documents and Settings/shorvath/My Documents/RFunctions/NetworkFunctions.txt")
# Load the network data
datAnnotation=read.delim(“FlyProtein_Annotations.csv”,header=T,sep=",")
dim(datAnnotation)
[1] 1371 3
# This vector encodes network essentiality (essential=1 and 0 otherwise)
essentiality = datAnnotation$essentiality - 1;
AdjMat1 = read.csv("FlyProtein_ADJ1.csv", header=F)
AdjMat1 = as.matrix(AdjMat1)
rownames(AdjMat1)=datAnnotation$nodename;
colnames(AdjMat1)=datAnnotation$nodename;
# Note that there are a lot of proteins for which essentiality is not known.
sum(is.na(essentiality))
[1] 466
sum(is.na(AdjMat1))
[1] 0
# ===================================================
# Check Scale Free Topology
# ===================================================
Degree = apply(AdjMat1,2,sum)
# Let’s create a scale free topology plot.
# The black curve corresponds to scale free topology and
# the red curve corresponds to truncated scale free topology.
par(mfrow=c(1,1))
ScaleFreePlot1(Degree, AF1=paste(“Fly Protein Network”),truncated1=TRUE);
[pic]
# ===================================================
# Module Detection/TOMk Plots
# ===================================================
# Restrict the analysis to the genes with high connectivity
DegCut = 1000
DegreeRank = rank(-Degree)
rest1 = DegreeRank signif(wilcox.test(PropEssentialGTOM0- PropEssentialGTOM2)$p.value,2)
[1] 0.034
> signif(wilcox.test(PropEssentialGTOM1-PropEssentialGTOM2)$p.value,2)
[1] 0.015
> signif(wilcox.test(PropEssentialGTOM3-PropEssentialGTOM2)$p.value,2)
[1] 0.02
Discussion
We find that neighborhood analysis with GTOM2 leads to significantly better results than GTOM0 (Wilcoxon p-value=0.034), GTOM1 (p-value= 0.015) and GTOM3 (p-value=0.02).
While this example shows that GTOM2 measure can lead to superior results than the standard measures GTOM0 and GTOM1, there is no doubt that more comprehensive empirical analyses are needed to establish the usefulness and limitations of the GTOM2 measure.
THE END
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