Introduction To Igraph and Shiny

An Introduction to Graphs
Chris Hammill
2015-04-01
Chris Hammill An Introduction to Graphs 2015-04-01 1 / 47

About Me
Graduate Student in Biology
Bioinformatics Research Assistant
R Aﬃcianado
Data Analysis/Visualization Contractor
Alumnus of this course

Why I’m Here
Talk about my research

Why I’m Here
Teach you a bit about graphs

Why I’m Here
Introduce you to some useful packages

Why I’m Here
Introduce you to some useful packages
Get you excited about interactive analysis

Outline
Introduce graphs
Introduce igraph
Introduce Interactivity with Shiny
Introduce the diabetes project
Demo the diabetes project app
Oﬀer resources

This presentation was written in R Markdown!
The slides and code will be made available via D2L

Outline
Introduce graphs
Introduce igraph
Oﬀer resources

So What Are Graphs?
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This?

So What Are Graphs?
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Nope!

So What Are Graphs
Graphs are a formal system for representing connections between things
Graphs are composed of nodes (or vertices) and edges (connections)
Edges can be weighted or unweighted, directed or not
Graphs have recently been rebranded as networks

So What Are Graphs?
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So This?

So What Are Graphs
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Yup!

Graphs in Math
Graphs were ﬁrst described by Euler (of e fame)
-
The bridges of Konigsberg
The name graph is due Sylvester (1878) which is widely considered
frustrating

Graphs For the Rest of Us
Graphs were brought out of the math domain primarily by social
scientists
For example Sampson (1968) did a social network analysis on monks in
a monastery identifying social dynamics

But More Importantly

And

So
Graphs are everywhere
Social Networks? Graphs
Internet? Graph
Metabolic pathways? Graphs
Due to this amazing generality, graph based representations
and algorithms can be incredibly useful for both exploration and
inference

What Can We Learn From Graphs?
Disclaimer: I’m still learning plenty about what can be done using graphs, so
this section will be necessarily over simpliﬁed.
Typically graphs are used to answer questions about the nature of its
connections (although graph representations can be used to carry out
immensely complex calculations as well; as you might have noticed
when you learned about artiﬁcial neural networks)
Typical questions include:
1 Where are the hubs (highly connected nodes)?
2 Can the graph be subdivided into clusters or communities?
3 Are there unexpected connections?
But as with any data representation you’re usually limited by your ability to
ask interesting questions, not the representations ability to answer them

Graph Properties
Degree Distribution
Degree is the number of edges a node has
The distribution of degrees in a graph is interesting and can hint at the
process generating the graph
Diameter
What is the longest direct path between two nodes
Average Path
What is the average path length between two nodes

Outline
Introduce graphs
Introduce igraph
Oﬀer resources

Creating and Using Graphs
Manipulating graphs with R is typically done with the igraph package,
so let’s try it out:
First Oﬀ, install igraph and attach it with the usual code
install.packages("igraph")
library(igraph)

Create a Random Graph
For exploration sake, lets generate a random graph (An Erdos-Renyi
random graph)
randomGraph <- erdos.renyi.game(20, 0.2)
plot(randomGraph)
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Summary Statistics
Degree
hist(degree(randomGraph))
Histogram of degree(randomGraph)
degree(randomGraph)
Frequency
2 4 6 8
012345

Summary Statistics
Diameter
diameter(randomGraph)
## [1] 4
Path Length
average.path.length(randomGraph)
## [1] 2.052632

Other Useful Commands
# Pull out all the Vertices
V(graph)
# Pull out all the Edges
E(graph)
#Change a component of the edges (or vertices)
E(graph)$weight <- newWeights
#Get all node pairs
get.edgelist(graph)
#Compute the adjacency matrix
get.adjacency(graph)

Outline
Introduce graphs
Introduce igraph
Oﬀer resources

Switching gears
Lets talk about exploratory analysis

Interactivity
A typical ﬁrst pass of data analysis involves:
1 Visualizing your data
2 Searching for hypotheses to test
3 Tuning parameters and repeating steps 1 and 2
You will waste untold hours (if you pursue science) doing
guess-and-check plot parameter tuning
You will grow weary in your search and likely settle for less than
optimal choices
Why not take the guess work out and make it faster to
explore parameter space

Enter Shiny
Shiny is a framework developed by the people at R Studio to bring
interactivity to R
Provides a tool to bring your analyses into the modern age
Not to mention the beneﬁt in presenting your analyses to non-experts
when they can see for themselves how parameters aﬀect the results.
Slightly frustrating interface, but very little new needs to be learned

So How Does Shiny Work
A shiny app is composed of (at least) two ﬁles
1 server.R
2 UI.R
server.R is responsible for performing the calculations in the app
UI.R is responsible for coordinating input from the user and output
from the server

Minimal Example
server.R
library(shiny)
shinyServer(function(input, output){
output$quadraticPlot <- renderPlot({
x <- seq(-2,2, length.out = 500)
y <- input$a * x^2 + input$b * x + input$c
plot(y ~ x,
xlim = c(-2,2),
ylim = c(-2,4),
type = "l")
})
})

Minimal Example
UI.R
library(shiny)
shinyUI(
fluidPage(
sliderInput("a", "a", min = -2L, max = 2L, value = 1),
sliderInput("b", "b", min = -1L, max = 1L, value = 0),
sliderInput("c", "c", min = -2L, max = 2L, value = 0),
plotOutput("quadraticPlot")
)
)

A Not So Minimal Example
Pedigree
Addisons_Comp
IBD_AI
Thyroid_Disease_AI
CVD_Comp
dyslipidemia_Comp
heart_disease_Comp
blood_pressure_Comp
nerve_damage_Compretinopathy_Comp
DKA_Comp
Hyperglycemia_Comp
diabetes_nurse
diabetes_specialist
dietician
GP
nephrologist_new
opthalmologist
cardiologist
podiatrist
Ace_inhibitor
Statin
addiction
anxiety_MH
depression_MH
Cholesterol_HDL_ratio
Creatinine
Glucose_Fasting
Glucose_Random
Hgb_A1C
M_C_Ratio
TSH
TTG
Gender
Weight
Smoke
Pneumococcal_Vax
Excercise
Health_Rating
Diabetes_Management_Rating
Rating_Of_Health_Care
DKA_ER
Dialysis
DOB
Diagnosis_Date
Insulin_started
DKA_Diagnosis
Ketones_Diagnosis
Weight_Loss_Symptom
bedwetting_Symptom
Breast_Fed
Sister_T1D
Father_T1D
Paunt_T1D
Puncle_T1D
Thyroid_Disease_FH
Hypertension_FH
Retinopathy_Diagnosis
Microalb_DiagnosisNephropathy_Diagnosis
Neuropathy_Diagnosis
Unknown_Hospitalizations
DKA_Hospitalizations_Old
other_hospitalizations
cd1d_rs3754471
cd1d_rs859009
ctla4_rs1863800
ctla4_mh30
ctla4_a49g
ctla4_ct60g_ga
ctla4_jo31g
ctla4_jo27tc
ccr2_v64i_ga
ccr5_a676g
wolf_611ag
dob_ga
sumo4_rs237012
adrb1_ga
ins_67ag
vdr_rs2544038
vdr_rs2408876
pld2_rs3764900
nos2a_rs4796017
nos2a_rs2248814
BCL2_c8687299
ptpns1_rs6075340
ptpns1_rs6111988
ptpns1_rs1884565
amel
amel_new
nos2a
−50
0
50
−log(p)
10
20
30
dataSet
gen
new
old
Pedigree
Number of Observations
40
60
80
100

Outline
Introduce graphs
Introduce igraph
Oﬀer resources

Diabetes Project
Attempting to predict health outcomes for Newfoundlanders suﬀering
from type one diabetes mellitus
Data from a large cohort of diabetes patents gathered ~10 years ago
Heterogenous mix of data sources, types, and completeness
Lots of data cleaning

The Data
three major data sources
1 Diabetes database
contains information about 631 study participants at the time of study
start

The Data
1 Diabetes database
start
2 Genetics Data
contains genotype markers for 591 study participants (and family
members)

The Data
1 Diabetes database
start
2 Genetics Data
members)
3 2014 Checkup Database
contains survey data and chart review for ~100 study participants

The Data
1 Diabetes database
start
2 Genetics Data
members)
This analysis is only concerned with the individuals for whom we have
updated information

The Data
1 Diabetes database
start
2 Genetics Data
members)
This analysis is only concerned with the individuals for whom we have
updated information
After cleaning 300 features exist for the participants

Analysis Approach
Considering each feature how well does it correlate to the rest of the
features

Analysis Approach
features
Pairwise correlation measures can be treated as a distance measure
between features

Analysis Approach
features
between features
Correlations can be ﬁltered by signﬁcance level

Analysis Approach
features
between features
Correlations can be filtered by signficance level
Each significant correlation can be viewed as an edge connecting the
two features

Creating the Graph
Challenge in going from
Spread Sheet Representation
head(bigtable[25:28,c(1,21,23, 41)])
## Pedigree dietician_new nephrologist_new Hgb_A1C_new
## 25 93001 0 0 8.7
## 26 94001 3 0 10.2
## 27 101001 0 0 9.2
## 28 105001 0 0 13.7

Pedigree
Addisons_Comp
IBD_AI
Thyroid_Disease_AI
CVD_Comp
dyslipidemia_Comp
heart_disease_Comp
blood_pressure_Comp
nerve_damage_Compretinopathy_Comp
DKA_Comp
Hyperglycemia_Comp
Hypoglycemia_Comp
diabetes_nurse
diabetes_specialist
dietician
GP
nephrologist_new
opthalmologist
cardiologist
podiatrist
Ace_inhibitor
Statin
addiction
anxiety_MH
depression_MH
Cholesterol_HDL_ratio
Creatinine
Glucose_Fasting
Glucose_Random
Hgb_A1C
M_C_Ratio
TSH
TTG
Gender
Weight
Smoke
Pneumococcal_Vax
Excercise
Health_Rating
Diabetes_Management_Rating
Rating_Of_Health_Care
DKA_ER
Dialysis
DOB
Diagnosis_Date
Insulin_started
DKA_Diagnosis
Ketones_Diagnosis
Weight_Loss_Symptom
bedwetting_Symptom
Breast_Fed
Sister_T1D
Father_T1D
Paunt_T1D
Puncle_T1D
Thyroid_Disease_FH
Hypertension_FH
Retinopathy_Diagnosis
Microalb_DiagnosisNephropathy_Diagnosis
Neuropathy_Diagnosis
Unknown_Hospitalizations
DKA_Hospitalizations_Old
other_hospitalizations
cd1d_rs3754471
cd1d_rs859009
ctla4_rs1863800
ctla4_mh30
ctla4_a49g
ctla4_ct60g_ga
ctla4_jo31g
ctla4_jo27tc
ccr2_v64i_ga
ccr5_a676g
wolf_611ag
dob_ga
sumo4_rs237025
sumo4_rs237012
adrb1_ga
ins_67ag
vdr_rs2544038
vdr_rs2408876
pld2_rs3764900
nos2a_rs4796017
nos2a_rs2248814
BCL2_c8687299
ptpns1_rs6075340
ptpns1_rs6111988
ptpns1_rs1884565
ptpns1_rs2267916
amel
amel_new
mit_nt7028
nos2a
−50
0
50
−log(p)
10
20
30
dataSet
gen
new
old
Pedigree
Number of Observations
40
60
80
100

Producing the Base Graph
Convert to a distance matrix
bt <- pCorrelationMatrix(bigtable)
Convert To Adjacency Matrix
adjacencyMat <- bt < threshold
Create an Igraph Object
network <- igraph.adjacency(adjacencyMat)

Converting the Igraph to a data.frame
Create a data.frame of vectices
getVertices <- function(graph, vertexNames = NULL){
vertices <- as.data.frame(layout.fruchterman.reingold(graph))
names(vertices) <- c("x","y")
vertices$vertexName <- 1:nrow(vertices)
if(!is.null(vertexNames)) vertices$vertexName <- vertexNames
vertices$size <- get.vertex.attribute(graph, "weight")
vertices
}

Converting the Igraph to a data.frame
Create a data.frame of edges
getEdges <- function(graph, vertices){
edgeLocations <- get.edgelist(graph)
edgeCoords <- mapply(function(v1,v2){
c(vertices[v1,], vertices[v2,])
}, edgeLocations[,1], edgeLocations[,2])
edgeFrame <- as.data.frame(t(edgeCoords))[,c(1,2,5,6)]
edgeFrame[,1:4] <- lapply(edgeFrame[,1:4], as.numeric)
edgeFrame$weight <- get.edge.attribute(graph, "weight")
edgeFrame$npo <- get.edge.attribute(graph, "npo")
names(edgeFrame) <- c("x0", "y0", "x1", "y1", "weight", "npo")
return(edgeFrame)
}

Do Both and Smoosh ’em Together
graph2frame <- function(graph, vertexNames = NULL){
vertices <- getVertices(graph, vertexNames)
edges <- getEdges(graph, vertices)
names(vertices) <- c("x0","y0", "vertexName", "size")
vertices$x1 <- NA
vertices$y1 <- NA
vertices$weight <- NA
vertices$npo <- NA
vertices$use <- "vertex"
edges$vertexName <- NA
edges$use <- "edge"
edges$size <- NA
rbind(vertices, edges)
}

Outline
Introduce graphs
Introduce igraph
Oﬀer resources

The App

Resources
Igraph
Ggplot
Shiny
R Markdown
Knitr
Datatables for R
My Blog!

Thanks For Having Me
Any questions?

Introduction To Igraph and Shiny

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