Inferring differential leukocyte activity
from antibody microarrays using alatent variable model
Joshua Ho1,2
1 School of IT, The University of Sydney
2 NICTA, Australian Technology Park, NSW, Australia

joshua@it.usyd.edu.au
Joint work with
R. Koundinya, T.S. Caetano,
C.G. dos Remedios, and M.A. Charleston

Human Immune System
 Many types of leukocytes (white blood cells),

each have a different immune function:
B-cells
 T-cells
 NK-cells
 Monocytes … and many more Distribution of leukocytes differs in differentdiseases
 Each type of leukocyte is characterized by aset of cell surface molecules called
CD antigens

GIW 2008, Gold CoastCD antigens

GIW 2008, Gold Coast

Antibody Microarray
Antigen does not match
Leukocytes do not bind
Antigens match
Leukocytes are captured

Anti CD Antibodies

Anti CD Antibodies
GIW 2008, Gold Coast

Courtesy of A. Brown

Antibody Microarray

GIW 2008, Gold Coast

Courtesy of A. Brown

Current Analysis Approach Visualization
 Clustering Finding DE genes
 Train classifier But how can we infer changes at theleukocyte level?

GIW 2008, Gold Coast

Courtesy of A. Brown

How difficult is antibody microarrayanalysis?

GIW 2008, Gold CoastHow to infer leukocyte activity?

 Conclusion: T decreased, Mo increased
GIW 2008, Gold Coast

Courtesy of A. Brown

Is GSEA an answer?
 No. Simulation shows GSEA is not satisfactory
 GSEA q-val for enrichment for simulated

T and B cells are 0.7 and 0.69 (not sig.)

GIW 2008, Gold CoastOur approach: latent variable modelC = class (observed) = {normal, disease1,disease2,…} L = leukocyte activity (latent) = {activated, deactivated} G = antigen expression (observed) = Gaussian distribution
GIW 2008, Gold CoastConditional Probability ModelsC P(Li|C) =active 0.3
inactive

LiLi

class1 class2

Li+1

0.7

0.80.2

 P(Gj|Li,Li+1) =LiGj

Li+1si,si+1

activeinactive

activeu1,1, s1,1

u0,1, s0,1

inactive

u1,0, s1,0

u0,0, s0,0

GIW 2008, Gold Coastui,ui+1

Model simplification
 n parents= 2x2n…

Li-2

Li-1

Li

Li+1

Li+2

parametersin the model Expensive
Gjandinaccurate Solution: decomposition
for small P(Gj|Li,Li+1,…Li+m) =sample
P(Gj|Li) P(Gj|Li+1)…P(Gj|Li+m)
dataset Only 2n parameters
GIW 2008, Gold Coast…

Model Simplification
 Problem:

dataduplication!

 Solution:

variance of each Gi
be proportional tothe original numberof parents
GIW 2008, Gold CoastLVM Parameter Learning
Expectation Maximization (EM) algorithm
 E-step: infer expected distribution of the latentvariables M-Step: find the maximum likelihood estimates ofthe model parameters Repeat E- & M- steps until convergenceCCP(L|C)

LE step

P(L|C) M step

LP(G|L)GCP(G|L)

E stepGP(L|C) *LP(G|L) *G
GIW 2008, Gold CoastModel Analysis
class1 class2

P(Li|C) =active 0.3

0.8

0.7

0.2

inactive Question: how to determine differential

leukocyte activity from P(Li|C)?
 Solution: Total correlation

GIW 2008, Gold CoastSimulated data T cells are differentially activated B cells’ activity remains unchanged
GIW 2008, Gold CoastResult: simulated data Very effective in inferring differential

leukocyte activity in simulated data
GIW 2008, Gold CoastReal data – cardiovascular diseases
 Brown et al. – coronary artery diseases

Control: 19 healthy donor Stable angina pectoris (SAP): 15 patients
 Unstable angina pectoris (UAP): 19 patients Lui et al. – heart failure

Control: 19 healthy donor Ischemic heart disease (IHD) – 22 patients
 Idiopathic dilated cardiomyopathy (IDCM)– 15 patients

GIW 2008, Gold CoastResult: Brown et al.
Our Results

GSEA Result

GIW 2008, Gold CoastResult: Lui et al.
Our Results

GSEA Results

GIW 2008, Gold CoastInterpretation
 We found that cardiovascular diseases has:

Decreased T cell activity
 Decreased NK cell activity
 Increased monocyte activity Consistent with previous findings
 GSEA results

Significant drop in T and NK cells in only somecases Failed to identify increases in monocyte activity
 Significant B cell enrichment in HF is not consistent
with visual inspection and known biology

GIW 2008, Gold CoastSummary We developed a latent variable model to inferdifferential leukocyte activity from antibody
microarrays
 We show its usefulness with simulateddataset and two cardiovascular datasets Our result is better than GSEA-based
analysis given the known biology
 We demonstrates how incorporating
underlying biological processes using aprobabilistic model is a very powerfulapproach in analyzing microarray data
GIW 2008, Gold CoastAcknowledgement
Joint work with
Dr. Michael Charleston
 Prof. Cristobal dos Remedios
 Dr. Tiberio Ceatano (NICTA) Rajeev Koundinya

Funding:
The University of Sydney
 NICTA GIW student bursary
 Sydney Bioinformatics


GIW 2008, Gold CoastSystems BiologyAnalysis of high-throughput data in the context of large
interacting systems



Need to model the biological data generation process, notjust association mining

Recover systems level properties that are not apparent
when considering individual components independently
GIW 2008, Gold Coast